oft
1. INTRODUCTION
1.1. Objectives of Physical Properties Measurements
Physical properties of rocks and sediments are indicators of composition,
formation, and environmental conditions of the deposits. Some physical properties
can be measured rapidly and easily at high spatial resolution (core logging) and
serve as proxies for processes such as paleoclimatic changes. Physical properties
data are usually well defined and quantitative, which helps constrain the complex
mineralogical and fluid systems in rocks and sediments. They are used
increasingly by a wide scientific community for various scientific objectives. For
these reasons, physical properties data form the bulk of all core data collected on
board the JOIDES Resolution on each leg.
In soft and semiconsolidated sediment sections, physical properties data serve
mostly as proxies for sediment composition, which is controlled by provenance,
depositional and erosional processes, oceanographic and climatic changes, and
postdepositional processes such as consolidation, and early diagenesis. In
consolidated sediments and igneous rocks, diagenetic processes, including
cementation, major lithological changes, and major faults, tend to dominate many
physical properties. Hydrothermal circulation can be detected in sediment and rock
environments by using physical property measurements.
A major application of data collected at small sampling intervals (a few
centimeters), such as magnetic susceptibility, color reflectance, gamma-ray
density, and natural gamma radiation, is for core-to-core and hole-to-hole
correlation and for correlating core data to wireline log data. These correlation
procedures are essential for stratigraphic studies, and some of the most important
ocean drilling projects are unthinkable without the high-performance acquisition
of physical properties data.
1.2. Shipboard Laboratory Stations and Sampling
OVERVIEW
After cores arrive on deck they are cut into 1.5-m-long sections and stored in racks
for temperature equilibration. The first measurement station is the multisensor
track (MST), where the whole-core sections are loaded on a motorized core
conveyor “boat” for the automatic measurement of gamma-ray density,
compressional (P-)wave velocity, magnetic susceptibility, and natural gamma
radiation. The MST is used most effectively with cores completely filled with s
1—1PP Handbook , Peter Blum , November, 1997
s
that
,
lit
as
ed for
r
t
oard
ce
of
res
to semiconsolidated sediments that were retrieved with the advanced hydraulic
piston corer (APC). Intact sedimentary or igneous rock cores cut with the extended
core barrel (XCB) or rotary core barrel (RCB) also give good MST measurements.
Coring disturbance such as severe “biscuiting” (typical for XCB cores) and
fracturing (typical for RCB cores) associated with torquing significantly reduce
the accuracy and usefulness of MST measurements, sometimes to a degree
MST measurements should not be performed.
For soft sediment cores, the second station is the thermal conductivity station
where needle probes are inserted into the whole cores. Next, the cores are sp
either with a wire (soft sediment) or with a saw. The half-cores are designated
archive-half cores and working-half cores. Figure 1—1 shows the relative core
orientation conventions established to place core measurements, particularly
paleomagnetic data, in a geographic reference frame using absolute core
orientation measurements when the core is cut. The same conventions are us
other physical properties measurements that can be performed in multiple
directions and that may reveal anisotropy (e.g., acoustic measurements) or fo
structural measurements. The archive-half cores are preserved in a pristine
condition whereas the working-half cores are available for measurements tha
physically disturb parts of the cores and for theremoval of specimens for shipb
as well as shore-based studies.
The archive-half core is used for the visual core description, paleomagnetic
measurements using the cryogenic magnetometer, noncontact color reflectan
measurements (to be implemented), and photography. A track system is in
development that will measure the two physical properties of magnetic
susceptibility and color reflectance along with the acquisition of color images
the core surface. After core photographs have been taken, the archive-half co
are stored in plastic tubes and refrigerated.
UPWorking half
y(90°)
z
Split-core face
x(0°)
(Double line)
UPArchive half
z
Split-core face
-x(180°)
(Single line)
Figure 1—1 Core orientation conventions.
1—2 PP Handbook , Peter Blum , November, 1997
ded
recise
the
.
e
illed
core
core
ndle
PWL
.
face.
m
ts
re
e
ented
The working-half core is used for the measurement of color reflectance (the
present mode of manual operation requires contact with sediment), P-wave
velocity by using probes that are inserted into the soft sediment, vane shear
strength by inserting a miniaturized vane into the sediment, and similar strength
measurements with the hand-held Torvane and penetrometer devices. Half-core
pieces of rocks are used for the measurement of thermal conductivity by using the
“half-space” needle probe. In the future, a gamma-ray densiometer will be ad
to the working-half station. Along with the use of a caliper (associated with theP-
wave system on this track) gamma-ray densities may be more accurate and p
than those obtained currently from the MST.
For the final physical properties measurement, specimens are extracted from
working-half core to determine moisture content and average mineral density
(MAD station). P-wave velocity can also be determined on specimens of
sedimentary or igneous rock extracted using a parallel-blade or cylindrical saw
The working-half core then proceeds to the “sampling table” where one to thre
individuals extract specimens for analysis on shore. The sampling voids are f
with Styrofoam, and the working-half core is stored in plastic tubes and
refrigerated along with the archive-half core.
MULTISENSOR TRACK (WHOLE-CORE MST) STATION
Measurement Systems The MST is an automated core conveying and positioning system for logging
physical properties at small sampling intervals. At present, the MST system
includes the following measurements:
• gamma-ray attenuation densiometry (GRA)
• P-wave velocity logging (PWL)
• magnetic susceptibility logging (MSL)
• natural gamma ray (NGR) measurements
The MST is one of the most routinely used devices onboard the JOIDES
Resolution. No other shipboard instrument produces a comparable amount of
data, and the MST data set is among the most widely used ODP data and
represents a worldwide standard of core analysis. The MST is designed to ha
the sampling of whole cores automatically, and all measurements except the
can also be used on split cores and for measurements on individual core
specimens. A new flexible, intuitive control interface was implemented in 1996
Sampling One of the most useful new features is the improved sampling parameter inter
The user can set sampling intervals and periods for all sensors and the progra
returns the calculated total measuring time for a core section based on an
optimized measuring sequence. A graphical display shows the sampling poin
with depth. Typically, the time permissible for a whole core (typically seven co
sections) is about 1 hr on legs with high core recovery (about 4 km of core or
more). Therefore, if full-time attention is given to the MST, about 10 min can b
allowed for measuring one core section. An overview of useful sampling
parameter settings is given in this section. More data and information are pres
in the individual sensor sections as appropriate.
1—3PP Handbook , Peter Blum , November, 1997
sors,
for
ors
ieved
nce.
about
of
for
The
d
ause
ore
al to
ter
of K,
n
the
nly
takes
d for
is
e of
re
ore
e
e.
When selecting sampling intervals, consideration should be given to the depth
interval each sensor can resolve (see Table 1—1). For the GRA and PWL sen
the depth intervals are less than 1 cm, for the MSL loop it is about 4 cm, and
the NGR it is about 15 cm. Because the sensitivity of the MSL and NGR sens
decreases away from the center of the sensor, better resolution can still be ach
by taking measurements at intervals smaller than the intrinsic interval of influe
Generally, ideal sampling intervals for the GRA, MSL, and PWL are 1 cm and
should not exceed 5 cm. For the NGR, the best depth resolution possible is at
5 cm. Intervals should not exceed 30 cm, which is about the depth resolution
downhole logging tools.
Sampling periods are directly related to the data quality (precision) particularly
the nuclear sensors. Because of the high flux provided by the 137Ce gamma-ray
source, 2-s sampling with the GRA is sufficient. The MSL has an internal
integration time of 0.9 s (1.0 range) or 9 s (0.1 range); it should be set at 1 s.
MST program is best set to 2-s sampling time to allow for minor electronic an
communications delay. The NGR is most sensitive to the sampling period bec
of the low intensity and random nature of natural gamma ray emissions. The m
counts are accumulated, the more reliable the signal (the error is is proportion
N-0.5, where N is the number of counts; see “Natural Gamma Radiation” chap
for more discussion). If spectral analysis is attempted to estimate abundance
U, and Th (which is not implemented for routine application yet), at least 1 mi
should be counted. (One hour would probably be more appropriate to reduce
statistical error to a level that would yield a good estimate of K, U, and Th). If o
a total counts signal is desired, as little as 15 s is sufficient in terrigenous
sediments, whereas 30 s should be measured in carbonates. The PWL system
five measurements (data acquisitions or DAQs) at each point that are average
the sample and provide a sufficiently precise value.
For optimized sampling parameter settings it is important that intervals and
periods are multiples of each other. This ensures that the idle time of sensors
minimized and data quantity and quality are maximized for a given total core
section scan period. For example, if GRA is set to 2 cm and MSL to 3 cm, on
the two sensors is partly idle while the other is taking a measurement. It is mo
efficient to set both at 2 cm so they measure simultaneously. Similarly, if the c
stops every 1 cm for GRA and MSL measurements and 4 s are required for th
MSL, the GRA sampling period should also be 4 s rather that 2 s because the
additional time improves data quality but it does not require any additional tim
Table 1—1 MST sampling parameters.
Sensor Sensitivity Interval (cm) Period (s)
interval (cm) Best Typical Maximum Best Typical Minimum
GRA <1 0.5 1 5 4 2 1
MSL 4 1 1 5 10 4 1
PWL <1 0.5 1 5 10a 1a 1a
NGR 15 1 10 30 >100 20 5b
Notes: aFive DAQs are averaged per second. bFor amoving average applied to data taken at close spacing.
1—4 PP Handbook , Peter Blum , November, 1997
have
lume
e
ut
s
ut 1
l).
ed
ent
t
olor
f
ct
may
A further optimization can be considered for NGR measurements. Rather than
taking a 20-s reading every 20 cm and leaving the other sensors mostly idle during
that time, a 5-s reading can be taken every 5 cm, simultaneously with the other
readings. This shortens total scanning time considerably. To get data quality
(statistical error range) equivalent to a 20-s counting time, the user simply runs a
moving average with a four-point window on the data.
THERMAL CONDUCTIVITY (TC) STATION
Measurement Systems Thermal conductivity is the only property measured at this station. Two systems
are available currently:
• Thermcon-85 system customized for ODP use and
• new TK04 system not customized for ODP.
A project plan exists to replace these with a fully integrated system that would
incorporate the best features of both existing systems. However, no resources
been allocated yet.
Soft-sediment cores are measured before they are split because the larger vo
of material surrounding the needle probe reduces geometrical problems (edg
effects). If the core material is too hard to be penetrated by the needles witho
excessive force, thermal conductivity is measured on working-half core piece
using the half-space needle probes.
Sampling Given the minimum time available until a soft sediment core must be split (abo
hr), at least 5-10 measurements can be performed (1- to 2-m sampling interva
This is usually sufficient because thermal conductivity variations are strongly
proportional to, but less sensitive and less precise than, bulk density
measurements. Density can be used as a proxy and calibrated against a limit
number of thermal conductivity measurements if higher spatial resolution is
required.
ARCHIVE-HALF CORE LOGGER (A-LOGGER, TO BE IMPLEMENTED)
Measurement Systems (to be implemented)
The archive-half core logger is under development and scheduled for deploym
later this year (1997). It will include the following measurement systems:
• color line-scan images,
• color reflectance spectrophotometry and colorimetry, and
• magnetic susceptibility.
The main goal for this development is to acquire color images of the cores (no
discussed in this note) and to automate the routine acquisition of visible light c
reflectance measurements. In addition, the spacial resolution and sensitivity o
magnetic susceptibility logging will be improved with a “point-sensor” that
requires contact with the core surface. Although line scans are truly nonconta
and nondestructive (i.e., ideally suited for archive-half logging),
photospectrometry and magnetic susceptibility require contact with the core
surface and these implications still must be evaluated. These measurements
haveto be obtained from working-half cores.
1—5PP Handbook , Peter Blum , November, 1997
-half
. A
ator
use it
e as
area.
2 to
ss.
ing
rovide
e
Present Measurement System
The present “proto-A-logger” consists of a manually operated track for color
reflectance measurements. Measurements are usually performed on working
cores because imprints are left on the core surface from the manual operation
simple computer program writes the data directly to disk and assists the oper
further by incrementing sampling intervals automatically.
Sampling Color reflectance should be measured at the smallest intervals possible beca
is very sensitive to compositional changes. Variations in color reflectance serv
an excellent proxy for detailed correlation and compositional interpretation. A
measurement with the Minolta spectrophotometer covers an 0.8-cm-diameter
The manual mode sampling intervals used by shipboard scientific parties are
20 cm. With the future automated system, intervals should be set at 1 cm or le
WORKING-HALF CORE STATION (W-LOGGER)
Measurement Systems The working-half core station is semiautomated currently. It includes the follow
measurements (Figure 1—2):
• P-wave velocity with the PWS1, PWS2, and PWS3 systems,
• Shear strength using the automated vane shear (AVS),
• Shear strength using the manual Torvane (TOR), and
• Compressional strength using a pen-size penetrometer (PEN)
A component analyzer is available for resistivity measurements, but these
measurements are not supported by ODP at present. Users are required to p
their own probes, perform their own calibrations, and develop their own
procedures.
P-wave velocity Shear strength
AVSPWS1 PWS2 PWS3
y xzn/a any directionn/a
Split-core:Measurement direction
Specimens:z-y plane
n/a
Figure 1—2 Schematic view of the semiautomated instrumentation on thworking-half core track. n/a = not applicable.
1—6 PP Handbook , Peter Blum , November, 1997
tent
try.
ensity,
e
. If
t the
pling
es,
d be
g
Sampling Sampling intervals for these measurements are mainly a function of available time
at a given core recovery rate and how much core destruction (particularly using the
AVS system) is permissible. The minimum sampling frequency on soft sediment
cores is one per core section; a more typical sampling rate is two per section (75-
cm sampling interval). If numerous measurements are desired on specimens that
must be extracted from the working-half core or that disturb the core, the ODP
staff representative must be consulted.
Whenever possible, the same sampling location should be coordinated for P-wave
velocity and strength measurements, as well as for subsequent extraction of
specimens for moisture and density measurements, carbonate, X-ray diffraction
(XRD), and/or magnetic rock properties measurements.
For velocity measurements on split cores in liners, no sample preparation is
necessary. An undisturbed interval is chosen for the measurement. For
measurements on specimens that require two parallel faces to obtain optimum
values, there are several ways to obtain such samples. In semiconsolidated
sediment, use a spatula or knife to cut a cube of approximately 20 cm3. For
indurated sediment, use a hammer and chisel or the Felker saw. The Torrance
double-bladed saw cuts good parallel faces. The easiest way to obtain a velocity
sample in hard rock is to “drill” cylindrical minicores. These samples are
particularly useful for sharing with the paleomagnetics laboratory (note the
orientation when taking the sample).
MOISTURE AND DENSITY (MAD) STATION
Measurement Systems At the MAD station, the following are measured:
• wet-bulk mass and dry mass of the same specimen (for moisture conand density) and
• volume of dry (and optionally wet-bulk) specimen using gas pycnome
From these measurements, basic phase relationships such as porosity, bulk d
grain density, dry density, and void ratio can be calculated. At present, a
convection oven is used to dry the specimens. Ideally, a freeze-dryer should b
used to avoid excessive extraction of interlayer water from clay minerals,
particularly smectite.
Sampling Sampling is typically 1-2 specimens per section, 10-mL volume per specimen
possible, the same sample interval should be used as for strength and/or P-wave
velocity measurements. Where numerous lithologic changes occur, denser
sampling may ensure measurements from all significant lithologies throughou
core. Where cyclic changes in gamma-ray density are observed, a denser sam
program over a characteristic interval may be desirable. In XCB and RCB cor
which commonly show the biscuiting type of disturbance, particular care shoul
taken to sample undisturbed parts of the core sections and to avoid the drillin
slurry.
1—7PP Handbook , Peter Blum , November, 1997
stem,
ing
le
y
ed:
s
w
mal
o the
me
l
1.3. New Shipboard Data Management Environment
BACKGROUND
In the early 1990s, the JOIDES advisory structure, through input from shipboard
participants identified the need to design and implement a new database system on
the ship as well as on shore. The complexity and level of productivity of the
shipboard data acquisition environment made this a multiyear, multimillion dollar
project. The physical properties laboratory was the first shipboard laboratory to be
integrated into the new data management environment once the basic operational,
curatorial, and depth calculation functions were redefined and implemented.
The process of redefining the entire ODP data structure offered the opportunity to
implement more rigorous data acquisition, calibration, and control measurement
protocols for physical properties measurements and to give the user access to these
quality control data. A uniform data structure, compatible with the rules of
relational data management, was created wherever possible. Leg 173 (April to
June 1997) was the official “acceptance leg” for the new data management sy
as described in this first edition of the note.
From the user’s perspective, the data management system includes the follow
components:
• data acquisition interfaces and controls,
• data upload utilities,
• database and data models, and
• data access and standard queries.
The following section briefly introduces these components.
COMPONENTS OF SHIPBOARD DATA MANAGEMENT
Data Acquisition Interfaces and Controls
DAQ programs are written in various programs depending on the most suitab
software tools and available expertise and hardware at the time and place the
were written. During the past two years, two dominating standards have evolv
Neuron Data for operational and curatorial functions and descriptive data type
(excellent for PCs, but performs poorly on Macintosh computers); and Labvie
for instrumental data (Macintosh or PC). The Neuron Data applications are
integrated into a common user interface, called the Janus Application. Most
physical properties DAQ programs are written in Labview now, including the
MST control, MAD program, P-wave velocity and vane shear strength on half
cores (PWS, AVS), and control of the Minolta photospectrometer (COL). Ther
conductivity remains in a state of development, and both available systems
controls are written in QuickBasic.
Data Upload Utilities Once data are acquired and located on a local drive, they must be uploaded t
Oracle database. Although procedure this could be fully automated and beco
part of the DAQ program, it was decided that an interactive user quality contro
should separate the two functions. Invalid or erroneous data are frequently
1—8 PP Handbook , Peter Blum , November, 1997
data
g,
, not
ther
lp the
al
,
r the
on to
ess
iently
using
age
st
n
tosh,
acquired, particularly on highly automated systems that are operated in a
conveyer-belt mode. The user has the option to delete such data from the local
directory before triggering upload to the database, which avoids excessive editing
within the database, a process that involves significantly more risk and effort.
Data upload utility programs are written in Neuron Data and are closely integrated
with DAQ programs written in Neuron Data. For DAQ programs written in
Labview or another language, a separate data upload utility must be operated. This
is the responsibility of the ODP technical support representative, but scientists may
learn the procedure and operate it themselves.
Database and Data Models
The new ODP Oracle database is designed specifically for ODP’s unique
shipboard environment and user needs. The system includes more than 250
tables in a complex relational scheme, capturing data from the initiation of a le
through core recovery and curation, physical and chemical analyses, core
description, and sampling. Physical properties alone use 65 tables at present
counting related tables for sample identification and depth data shared with o
laboratories, and will involve more than 100 tables once the remaining
measurement systems are integrated. The tables pertaining to a particular
measurement system are presented in the “Data Specification” sections to he
user understand how the data are structured and how they can be accessed.
Data Access and Standard Queries
At this early stage of using the new database, there are three different technic
approaches to data access, and the next few legs will show which is the most
efficient and user-friendly one. The three approaches are referred to as
• Janus Application,
• Report Access Program, and
• World Wide Web Data Access.
The first solution integrates an off-the-shelf reporting utility, Business Objects
into the Janus Application. Many reports are available through this main
interfacefrom which the user selects a particular report from a submenu.
The Report Access Program (RAP) was written as an alternative manager fo
Business Objects reports. The advantage is that the user does not have to log
the Janus Application, which may be somewhat time-consuming, and that acc
to and expansion of Business Objects reports and queries could be more effic
managed by ODP. This environment allows the user to create special reports
existing Business Object macros relatively easily.
The third approach is for ODP personnel to write standard queries in C-langu
and make them available through a World Wide Web (WWW) browser. This
approach has the advantages that routines are directly suitable for global data
access and that accessing data on the ship on the local web may be the faste
method. It will not provide the freestyle access to the database that Business
Objects in the RAP environment offers to the user. However, recent informatio
indicates that Business Objects will not continue to be supported on the Macin
which rules out its future use. The third approach will therefore most likely be
fully implemented.
1—9PP Handbook , Peter Blum , November, 1997
2.
he
g
ow
low
ay be
from
ts in
is a
d at
ered
ore-
ained
d to
ew
depth
—2 ).
SAMPLE IDENTIFIERS AND DEPTH CALCULATION
Links to Curatorial Identifiers
In the relational ODP database, redundancy of information is minimized for
efficient data management. For example, site, hole, core, and section information
is entered in specific tables linked in a logical way, and all measurement locations
in a particular section are linked to the <Section> table. Similarly, if a core
specimen is extracted for shipboard or shore-based analysis, the basic curatorial
information is accessed through the <Sample> table, which is linked to the
<Section> table, etc. In the physical properties database models presented in the
following chapters, the field <section_id> alone or with the fields <interval_top>
and <interval_bottom> are the links to the more specific information in the
appropriate tables. The <Sample> and <Section> tables are listed in Table 1—
Depth Types Depth below seafloor of a core specimen or measurement location can be
calculated in different ways. The standard way is to measure the distance in t
recovered and physically expanded core and add it to the measured drill strin
depth datum for the top of the core. This depth scale is known as “meters bel
seafloor” (mbsf). Of course, this is only an approximation to the true depth be
seafloor. Problems inherent in this scale are that the recovered core length m
greater than the interval advanced by the drill string, and some of the material
this interval was lost between successive cores. With APC material, this resul
apparently overlapping sections between successive cores when in fact there
coring gap.
If a complete stratigraphic section is to be constructed, multiple holes are drille
the same site and a composite section is developed at the “meters composite
depth” (mcd) scale. This scale is at the physically expanded state of the recov
cores and does not match the drilled interval. However, it is a much more
continuous scale that can be fit approximately to the drilled interval using the c
top data (mbsf) or fit more precisely if good-quality downhole logging data are
available.
There are additional corrections that can be applied to derive a more accurate
approximation to depth below seafloor. These and other depth issues are expl
in detail in a workshop report (Blum et al., 1995), and a technical note dedicate
these issues will be produced. The redefined concepts are integrated in the n
database, which features a depth map that allows the rapid calculation of any
type provided that pertinent data have been acquired and entered (see Table 1
1—10 PP Handbook , Peter Blum , November, 1997
y. The
a and
n is to
ble is
es
ries
h
tific
s for
Standard data queries prompt the user to specify the desired depth type. The
default map type (mbsf) is referred to (map_type_name) as “standard.”
1.4. Physical Properties Standards
Standard materials used to calibrate instruments are an essential part of the
analyses and should be integrated into the measurement systems accordingl
goal is to enter all standards used into database tables so that calibration dat
results can be tracked to the particular standard used at a given time. Our pla
populate a <Physical Properties Standard> table shown in Table 1—3. The ta
generic enough to accommodate any type of standard, and the value of any
property can be linked to any calibration utility and file in the physical properti
environment. This table may principally include standards from other laborato
as well.
A table of existing standards is in preparation.
Unfortunately, ODP has not made significant efforts to share standards and
calibration procedures with other core laboratories (with rare exceptions). Suc
efforts would benefit ODP as well as other laboratories, and therefore the scien
drilling community, because reliable and widely endorsed calibration standard
systems that measure complex natural systems are difficult to find.
Table 1—2 Database model for some essential s.
Map Type Depth Map Section Samplemap_type [PK1] section_id [PK1] [FK] section_id [PK1] sample_id [PK1]
description map_type [PK2] [FK] section_number location [PK2]map_type_name sect_interval_top [PK3] section_type sam_section_id . section_idmap_type_date sect_interval_bottom [PK4} curated_length sam_archive_working
map_interval_top liner_length top_intervalmap_interval_bottom core_catcher_stored_in bottom_interval
section_comments pieceleg sub_piecesite beaker_id . mad_beaker_id
hole volumecore entered_by
core_type sample_depthsample_commentsam_repository . repository
s_c_leg . legs_c_sam_code . sam_code
sam_sample_code_lab . s_c_l
Table 1—3 Physical properties standards database model.
Physical Properties Standard Physical Properties Std Datastandard_id [PK1] standard_id
standard_name property_namestandard_set_name property_descriptiondate_time_commissioned property_value
date_time_decommissioned property_unitslot_serial_numbercomments
1—11PP Handbook , Peter Blum , November, 1997
ass
on
to
yer
ich
ave
f
and
The
dard
t
sion.
n user
p) or
The
inly
no
d.
2. MOISTURE AND DENSITY (BY MASS AND VOLUME)
2.1. Principles
PHYSICAL BACKGROUND
Moisture content and mineral density are basic sediment and rock properties that
are determined most accurately through mass and volume determinations. Core
specimens of approximately 8 cm3 are extruded from the working-half core for
this purpose. Moisture content is determined by measuring the specimen’s m
before and after removal of interstitial pore fluid through drying. The drying
method is the most critical part of the entire procedure. At present, a convecti
oven is used for this purpose for 24 hours at temperatures varying from 100°
110°C. This method is suspected to remove a substantial portion of the interla
(hydrated) water from clays such as smectite in addition to interstitial water, wh
may result in porosity errors of up to 20%. Alternative methods such as microw
or freeze-drying have other potential problems and have not replaced the
convection oven.
Moisture content, porosity, and void ratio are defined by the mass or volume o
extracted water (assumed to be interstitial pore fluid), corrected for the mass
volume of salt evaporated during the drying process (see also ASTM, 1990).
mass and volume of the evaporated pore-water salts are calculated for a stan
seawater salinity (35), seawater density at laboratory conditions (1.024 g/cm3),
and an average seawater salt density (2.20 g/cm3). Any gases that may be presen
are allowed to escape during core retrieval, core splitting, and specimen extru
The volume of a specimen can be measured in three ways:
• method A: wet-bulk volume measured with special volume sampler,
• method B: wet-bulk volume measured by gas pycnometry, and
• method C: dry volume measured by gas pycnometry
Method A is the least standardized method. The device to be used depends o
preference and can be a simple steel ring (“fixed volume,” available on the shi
some sort of syringe (volume is measured after the sample has been taken).
advantage of method A, according to some users, is that a larger number of
specimens can be measured than with gas pycnometry in a given time. The
disadvantages are (1) the method works only in soft, non-sticky sediment (ma
homogenous carbonate oozes to a depth of about 200 mbsf), (2) the volume
measured includes potential cracks or other spaces filled with air, (3) there is
precision estimate for this method, and (4) there is no standard for this metho
2—1PP Handbook , Peter Blum , November, 1997
ry
of this
re
al
. The
ith
that
nd
ker
m’s
user
dard
. It
in
ble.
This method should therefore be used only if there is ample justification, and
measurements must be “calibrated” with an appropriate number of pycnomet
results.
Methods B and C use the same gas pycnometer. The measurement principle
device is briefly described in the following. Gas pycnometry works with pressu
ratios of an ideal gas (helium), which are sensitive to contamination with parti
pressures of other fluids. The material to be measured should therefore be dry
ODP database contains thousands of examples from specimens measured w
both method B and method C. A systematic error is clearly discernible in
comparing calculated results, with bulk densities 1%–5% too high and grain
densities about 5%–10% too high for method B. It is therefore recommended
only method C be used.
The following relationships can be computed from two mass measurements a
one or two volume measurements. First, if methods B or C are used, the bea
mass and volume, which are determined periodically and stored in the progra
lookup table, are subtracted from the measured total mass and volume
measurements. If method A is used, only the beaker mass is subtracted (the
must specify the use of method A in the program). This results in the following
directly measured values:
• Mb: bulk mass,
• Md: dry mass (mass of solids, Ms, plus mass of evaporated salt),
• Vb(A or B): bulk volume, method A or method B, and
• Vd(C): dry volume = volume of solids, Vs(C), plus volume of evaporated salt, Vsalt.
Variations in pore-water salinity, s (s = S/1000), and density, ρpw, that typically
occur in marine sediments do not affect the calculations significantly, and stan
seawater values at laboratory conditions are used:
s = 0.035 (1)
ρpw = 1.024. (2)
Pore-water mass, Mpw, mass of solids, Ms, and pore-water volume, Vpw, can then
be calculated:
Mpw = (Mb – Md) / (1 – s) (3)
Ms = Mb – Mpw = (Md – s Mb) / (1 – s) (4)
Vpw = Mpw/ρpw = (Mb – Md) / [(1 – s) ρpw]. (5)
Additional parameters required are the mass and volume of salt (Msalt and Vsalt,
respectively) to account for the phase change of pore-water salt during drying
should be kept in mind that for practical purposes the mass of salt is the same
solution or as precipitate, whereas the volume of the salt in solution is negligi
Msalt = Mpw – (Mb – Md) = (Mb – Md) s / (1 – s) (6)
Vsalt = Msalt / ρsalt = [(Mb – Md) s / (1 – s)] / ρsalt, (7)
where the salt density value ρsalt = 2.20 g/cm3 is a value calculated for an average
composition of seawater salt (Lyman and Fleming, 1940; Weast et al., 1985).
2—2 PP Handbook , Peter Blum , November, 1997
the
loor,
every
or
g
is in
ip
ive
veral
lume
refore
s
.
ions
itions
Moisture content is the pore water mass expressed either as percentage of wet bulk
mass or as percentage of the mass of salt-corrected solids:
Wb = Mpw / Mb = (Mb – Md) / Mb (1 – s) (8)
Ws = Mpw / Ms = (Mb – Md) / (Md – s Mb). (9)
Calculation of the bulk volume for method C and volume of solids depend on
volume measurement method used:
Vs(A or B) = Vb(A or B) – Vpw (10)
Vs(C) = Vd(C) – Vsalt (11)
Vb(C) = Vs(C) + Vpw. (12)
Bulk density, ρb, density of solids or grain density, ρs, dry density, ρd, porosity, P,
and void ratio, e, are then calculated accordingly for each method:
ρb(A,B,C) = Mb / Vb(A,B,C) (13)
ρs(A,B,C) = Ms / Vs(A,B,C) (14)
ρd(A,B,C) = Ms / Vb(A,B,C) (15)
P(A,B,C) = Vpw / Vb(A,B,C) (16)
e(A,B,C) = Vpw / Vs(A,B,C). (17)
ENVIRONMENTAL EFFECTS
Core Expansion Cores, particularly sediment cores from a few hundred meters below the seaf
expand upon recovery for a number of reasons, which include
• elastic recovery,
• gas expansion, and
• mechanical stretching.
Expansions of solids can be neglected. Pore water expands by about 4% for
1000 bar (100 MPa) pressure release. This is what the pore water of a seaflo
sample from about 10,000-m water depth would experience, or in ocean drillin
terms, what a sample buried by about 2000 m of water and about 3000 m of
sediment would experience. For the bulk of ODP cores, pore-water expansion
the order of 1% and therefore negligible compared with the analytical error.
Free gas expands by orders of magnitude, according to the simple relationsh
P1V1 = P2V2. A few percent of free gas in the sediment can produce an explos
sediment-gas mixture that has torn apart plastic core liners on the ship on se
occasions. Most gas escapes before the cores are analyzed and can produce
microfractures, which appear as porosity with methods based on core unit-vo
measurements, such as the gamma-ray attenuation bulk density method.
Mechanical stretching may also cause microfracturing. The MAD method
measures the mass and volume of the solid and liquid phases only and is the
not affected by this type of artificial porosity. The original contribution of the ga
to in situ porosity cannot be measured with our routine core analysis program
Composition of Seawater
Different water masses of the world oceans have different chemical composit
and physical properties. For the purpose of correcting oven-dried sediment
specimens for the evaporated salt from the pore water, the standard compos
2—3PP Handbook , Peter Blum , November, 1997
10 and
n of
lues
ffects
the
22°C
e
n our
-
, the
situ
after Lyman and Fleming (1940) and salt densities after Weast et al. (1985) are
used (Table 2—1).
aLyman and Fleming (1940).
bWeast et al. (1985).
Given the uncertainty in regard to the crystalline structure of some evaporated
components, the average density of the standard seawater salt is between 2.
2.24 g/cm3. A value of 2.20 g/cm3 is used routinely for the MAD calculations.
Density of Pore water Density of pore water is a function of temperature (T), salinity (S), and pressure
(P). Equations of state for seawater (Millero et al., 1980; Millero and Poisson,
1981) can be used to illustrate the variability of pore-water density as a functio
these three parameters (Figure on page 5).
Typical salinity values for pore waters are 30 to 40, although more extreme va
exist. At laboratory pressure and temperature, this range of salinity change a
pore-water density change of less than 1%, which is negligible compared with
analytical uncertainty. We therefore use a standard value of 35 for all MAD
calculations and leave it up to the user to apply corrections if warranted.
The typical temperature change experienced by nonlithified sediment upon
recovery is from about 100°C at depth to few degrees at the seafloor to about
in the laboratory. At standard salinity and laboratory pressure, a 100°C chang
results in about a 2% change in seawater density. These effect is not figured i
MAD calcualations because it is close to the uncertainty.
The effect of pressure change on density is of a similar magnitude. For a high
porosity mud sample (for example, from 100 mbsf) at a water depth of 3000 m
pressure release is about 320 bar (32 MPa). According to Figure 2—1, if the in
Table 2—1 Composition of sea water.
Salt Mass fractiona
(x 103)
Densityb
(g/cm-3)
NaCl 23.476 2.165
MgCl2 4.981 2.316-2.33
Na2SO4 3.917 1.46 (monocl.)2.68 (orthorh.)
CaCl2 1.102 2.15
KCl 0.664 1.984
NaHCO3 0.192 2.159
KBr 0.096 2.75
H3BO3 0.026
SrCl2SrCl2.2H2O
0.024 2.671 (leaf)3.052 (cub.)
NaF 0.003 2.08
Total 34.481
Weighted average 2.10-2.24
2—4 PP Handbook , Peter Blum , November, 1997
era-m 0
reme40°Cndi-
temperature is about the same as laboratory temperature and salinity is 35, pore-
water density decreases by about 1% upon recovery.
Figure 2—1 Density of seawater as a function of pressure, salinity, and tempture, using equations from Millero and Poisson (1981). The pressure range froto 1000 bar covers most ODP situations. Standard salinity of 35 and two extsalinities (0 and 70) are plotted as a function of a temperature between 0º to (experimental temperature range of Poisson and Millero, 1981). The arrow icates standard laboratory conditions.
USE OF MAD DATA
MAD data are the only data that provide a direct estimate of porosity and void
ratio and the average density of the minerals. Porosity variations are controlled by
consolidation and lithification, composition, alteration, and deformation of the
sediments or rocks.
MAD data can be used to calibrate the high-resolution gamma-ray attenuation bulk
density data sampled automatically at much smaller intervals than would be
possible for MAD data. If mineral density can be defined with sufficient precision,
GRA bulk density can be expressed as porosity.
990
1010
1030
1050
1070
1090
0 200 400 600 800 1000
Density (kg/m
3 )
Pressure (bar)
0°C
S = 7
S = 3
S = 0
20°C
40°C
0°C
20°C
40°C
0°C
20°C
40°C
2—5PP Handbook , Peter Blum , November, 1997
cells
ce
he
ssible
e
tes
g is
imen.
120
n
the
e ideal
ring
l (for
sed. hich ent res a
he
ses.
2.2. Moisture and Density System
EQUIPMENT
Balance Mass is determined with two Scientech 202 electronic balances to compensate for
the ship’s motion. A set of mass standards ranging from 1 to 20 g is used for
calibration and on the reference balance during measurements.
Gas Pycnometer The helium displacement pycnometer with five cells (penta-pycnometer),
manufactured by Quantachrome Corp., employs Archimedes’ principle of fluid
displacement to determine the volume of solid objects. The five measurement
contain custom-fabricated inserts that reduce the chamber to a cylindrical spa
that holds exactly one 10-mL Pyrex beaker. The measurement chamber must
contain as little air space as possible to maximize measurement precision. (T
user should also ensure that the Pyrex beakers are filled as completely as po
with core material.)
Each sample cell of volume VC has an input valve (from the gas tank) and an
output valve (to the pressure transducer). An additional reference cell of volum
VA is located downvent of the sample cells, with an input valve (which separa
VA from the pressure transducer) and a vent valve (Figure on page 7). All cell
volumes must be calibrated periodically (see calibration section). The followin
the operation sequence of the pycnometer during the measurement of a spec
The specimen to be measured is placed in a cell of known volume, VC. It is
pressurized, using helium, to an exactly measured pressure of about 18 psi (~
kPa). The solenoid valve between sample cell and the reference cell of know
volume VA is opened and the helium from the pressurized chamber is ported to
reference cell. The subsequent pressure in the system is measured. Using th
gas law, the sample volume can be calculated from the pressure ratio. The
following is the sequence of operation (Figure 2—2).
1. Gas input valves to all five cells are closed (corresponding light-emittingdisplays [LEDs] on pycnometer are unlit). The five sample cell output valves, the reference cell input valve, and the vent valve are open, ensuthat all cells are at the ambient pressure, Pa.
2. For all cells in use all valves are opened and cells are purged in parallea 1-min minimum). Cells not being used (not identified by the user) are isolated by closing the input and output valves.
3. At the end of the purge period, processing begins on the first cell to be u(Cells are run in ascending numerical order regardless of the order in wthey were specified). When a stable ambient pressure is reached, the vvalve of the reference cell closes and the pycnometer acquires and stozero pressure value.
4. The reference cell input valve is closed to isolate VA from the cell. Approximately 6 s later, the current sample cell input valve opens and tcell is pressurized to approximately 17 psi or until 3 min elapse.
5. When the cell pressurization is complete, the current cell input valve clo
2—6 PP Handbook , Peter Blum , November, 1997
Blue
The pycnometer waits until a stable pressure is detected and then acquires and stores the pressure P1 (LED display: pressure A).
6. The VA input valve opens. This will cause a pressure drop in the sample cell that is proportional to the change in volume because of the introduction of VA. When the pressure stabilizes, the system acquires and stores the cell pressure P2 (LED display: pressure B).
7. The vent valve is opened to return the cell to ambient pressure. After a short vent period, the instrument begins processing the next specified cell (if any) by venting the cell to ambient pressure.
8. After all cells defined for use have been processed, samples may be removed. The pycnometer indicates this by displaying “<RUN COMPLETED>”.
Figure 2—2 Operating sequence of the Quantachrome penta-pycnometer. lines and cells are under ambient pressure Pa (Pa = P0). Red lines and cells are
1
2
3
4
5
VA
1
2
3
4
5
VA
P0
1
2
3
4
5
VA
PpurgeP0
1
2
3
4
5
VA
P0
1
2
3
4
5
VA
P0 1
2
3
4
5
VA
P1
1
2
3
4
5
VA
1
2
3
4
5
VA
P2
1
2
3
4
5
VA
P0 1
2
3
4
5
VA
P0
A
C
E
G
I J
H
F
D
B
P1
Vent valve closed
System idle Purging cells to be used
Equilibrate to ambient
Reference volume isolated Sample cell pressurized
Sample pressure isolated Reference volume added
V l d E ilib i ll
2—7PP Handbook , Peter Blum , November, 1997
stem
ct the
under system pressure P1 (about 17 psi). Green lines and cells are under areduced system pressure P2.
The sample volume can be calculated using the ideal gas law. By opening the
solenoid valves on one sample cell with volume VC, the system is brought to
ambient pressure Pa after being purged with helium. The state of the system is then
defined as
Pa VC = n R Ta , (18)
where n is the moles of gas occupying volume VC at pressure Pa, R is the gas
constant, and Ta is the ambient temperature in degrees Kelvin.
When the solid sample of volume VS is placed in the sample cell, the equation can
be written as
Pa (VC – VS) = n0 R Ta . (19)
After pressurizing to about 17 psi above ambient pressure, the state of the sy
is given by
P1 (VC – VS) = n1 R Ta . (20)
Here, P1 indicates a pressure above ambient and n1 represents the total moles of
gas contained in the sample cell. When the solenoid valve is opened to conne
added volume VA to that of the cell VC, the pressure falls to the lower value P2
given by
P2 (VC – VS + VA) = n1 R Ta + nA R Ta , (21)
where nA is the moles of gas contained in the added volume when at ambient
pressure.
The term Pa VA can be used in place of nA R Ta in Equation on page 8yielding
P2 (VC – VS + VA) = n1 R Ta + Pa VA . (22)
Substituting P1 (VC – VS) from Equation on page 8 for n1 R Ta:
P2 (VC – VS + VA) = P1 (VC – VS) + Pa VA (23)
(P2 – P1) (VC – VS) = (Pa – P2) VA (24)
VC – VS = (Pa – P2) / (P2 – P1) VA . (25)
Equation on page 8 is further reduced by adding and subtracting Pa from P2 and P1
in the denominator, giving
VS = VC – {[(Pa – P2) VA / [(P2 – Pa) – (P1 – Pa)]} (26)
= VC + VA / {1 – [(P1 – Pa) / (P2 – Pa)]}. (27)
Because Pa is zeroed prior to pressurizing:
VS = VC + VA / [1 – (P1/ P2)]. (28)
This is the working equation employed by the penta-pycnometer.
Convection Oven The convection oven can maintain 105° ± 5°C.
2—8 PP Handbook , Peter Blum , November, 1997
akers
e low
e
y by
nd
re
78 g/
n be
the
This drying process has two main problems: (1) clay mineral interlayer water is
largely lost in addition to interstitial water and (2) specimens dried in a convection
oven become brick hard and are rarely useful for any other analyses that require
substantial sample volumes. Use of freeze-drying would partly eliminate these
problems. In particular, stable isotope analyses on foraminifers would be possible
from freeze-dried samples, but not from oven-dried samples. The convection oven
is used based on advice from the relevant JOIDES advisory panel, because drying
at 105° ± 5°C for 24 hr is a well-established soil science standard.
CALIBRATION
Beaker Mass and Volume
Beaker mass must be measured and entered into the MAD program for all be
to be used on a leg. Beaker volume is not convenient to measure because th
material volume to void ratio in the pycnometer cell gives inaccurate values. W
have therefore determined the density of the Pyrex beaker material accuratel
filling a beaker with chips of other beakers, measuring its mass and volume, a
calculating its density. The density of 2.2 g/cm3 is stored in the MAD program,
which returns the volume corresponding to each beaker mass.
Custom-made aluminum beakers were used until Leg 168. These beakers we
difficult to clean, corroded with time, and were expensive to manufacture. For
historical data migration purposes, those beaker materials had a density of 2.
cm3 (determined by P. Blum, 1996).
Balance Calibration The ship is an environment of cyclically changing gravity, and the measured
weight W of a mass M is significantly affected by the ship's motion. If W is
measured over a period of time several times the periodicity of the ship’s
acceleration a, the average can be related to M. By using two balances, mass
determination can be significantly accelerated. The following two equations ca
written for two balances:
Fs = Ms × a(t) = As + Bs × Vs(t) (29)
Fr = Mr × a(t) = Ar + Br × Vr(t), (30)
where Fs and Fr are average measured weights and Ms and Mr are known mass
standards on the sample and the reference balance, respectively, a is the ship's
average acceleration, V is the average voltage measured, and A and B are constants
characteristic for the balances. The calibration principle is to measure multiple
standards (typically 1, 5, 10, 20, and 30 g) to determine A and B for each balance.
For the calibration, measuring time should be at least 30 s to cover several of
7–8 heave cycles of JOIDES Resolution.
Equations on page 9 and on page 9 can be solved for a(t), which is assumed to be
equal for both balances:
Ms = [As + Bs × Vs(t)] × Mr / [Ar + Br × Vr(t)], (31)
which is identical to
Ms (unknown) / Ms (calculated) = Mr (known) / Mr (calculated). (32)
2—9PP Handbook , Peter Blum , November, 1997
ion
ken
ation
.
ugh
rate
h
lues
o
n
The first right-hand term in Equation on page 9 is the first approximation to the
calculated sample mass. This value is uncorrected for motion and is returned
instead of 0 if the user sets Mr (known) = 0. The second right-hand term in
Equation on page 9 uses the ratio between a known mass Mr on the reference
balance and its corresponding calculated value to correct the first term for the
ship’s motion.
The MAD program performs the linear regression for multiple standards and
stores the coefficients until a new calibration is performed. A balance calibrat
takes up to 15 min. It is recommended that a few control measurements be ta
after a calibration to verify the correct mean value and a percent standard devi
of less that 1% for 100 or more measurements taken over approximately 30 s
Pycnometer Calibration
The pycnometer has an internal calibration procedure. The user is guided thro
the procedure step by step by the program. First, cell 4 must be used to calib
the reference volume (pressure) VA. Then, the calibration sphere is cycled throug
all five cells to determine the empty cell volume (pressure). The calibration va
are stored in the pycnometer and used until a new calibration is performed. A
pycnometer calibration takes up to 30 min.
The instrument calibrates VA by performing two pressurizations, once with the
sample cell empty (VS = 0) and once with the calibration standard of volume Vstd
in the same sample cell.
Equation on page 8 derived previously for a sample measurement for these tw
conditions can be written as
VS = 0 = VC - VA / [(P'1/P'2) - 1] (33)
and
VS = Vstd = VC - VA / [(P1/ P2) - 1]. (34)
Combining these two equations yields
VA = Vstd / {[1/(P'1/ P'2) - 1)] - [1/(P1/ P2) - 1]}. (35)
The instrument calibrates the volume VC of each cell with one pressurization of
each cell holding the appropriate sample holder and the calibration standard.
Equation on page 10 is then used and can be written as:
VS = Vstd = VC + VA / [1 - (P1/ P2)] (36)
VC = VA {1 / [(P1/ P2) - 1]}. (37)
PERFORMANCE
Precision Standards of 1and 20 g are measured to confirm balance calibration, and the
readings should be within 1 mg, or better than 0.1%. Repeatability of specime
mass at sea should also be within 0.1%.
For the pycnometer, a standard sphere is measured (e.g., 7.0699 cm3) and
precision should be within 0.1% (0.005 cm3 for the sphere mentioned). Repeat
measurements on sediment samples yield a precision of about 1%, probably
2—10 PP Handbook , Peter Blum , November, 1997
the
not
in a
box.
resulting from changes in ambient pressure and temperature and the material
during handling.
Accuracy Mass error: 0.1%.
Volume error: 1%.
MEASUREMENT
The user is guided through data entry by the MAD program, which controls the
balance as well as the pycnometer. The sample ID needs to be entered only once
for the entire process. The pycnometer key pad is not used during measurement.
The following is the general measurement protocol:
1. Typical sampling frequency for MAD measurements is two per section. One per section is considered a minimum; more than two per section on medium- to high-recovery legs is rather demanding with the present staff assignments.
2. Fill a numbered 10-mL Pyrex beaker with sediment to about 3 mm below the rim so that material is not lost during handling of the beaker. The largest errors in MAD measurements probably stem from lost material during the process and from volume measurements with incompletely filled beakers. It is the operator’s responsibility to find the optimum. Place a special PP Styrofoam plug into the hole left from where the sample was taken fromworking-half core.
3. Enter the sample and beaker number into the Sample program at the sampling table. This information will then be in the database; only the beaker number is used at the MAD station to select samples.
4. Measure the mass. Do not let the sample stand without covering it withplastic film, being careful not to lose material.
5. Optionally, measure the wet volume in the pycnometer. However, this isnecessary and years of experience have shown that wet volume measurements (method B) appear to have a large error.
6. Place the sample in the oven at 105° ± 5°C for 24 hr. Place the sampledesiccator after it is removed it from the oven.
7. Measure the mass and volume of the dry sample and beaker.
8. Place the residue in a sample bag, attach a completed label, seal, and
9. Clean the beaker.
2—11PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database model
Notes: The Sample table is used for all ODP core samples. MAD samples are identified by sampling code; the ODP standard designation is linked through the beaker_id. If method A is used the “fixed_volume” must be set to be TRUE. The MAD calibration history table is a log of calibrations but does not hold the calibration data.
Standard Queries
Table 2—2 MAD database model.
Sample MAD sample data MAD control data MAD beaker historysample_id [PK1] sample_id [PK1] [FK] mad_control_id [PK1] mad_beaker_id [PK1]location [PK2] location [PK2] [FK] run_date_time beaker_date_time [PK2]
sam_section_id . section_id mad_beaker_id ctrl_standard_id beaker_numbersam_archive_working beaker_date_time control_type beaker_type
top_interval fixed_volume expected_value beaker_massbottom_interval mass_wet_and_beaker pyc_cell_no beaker_volumepiece mass_dry_and_beaker measured_value
sub_piece vol_wet_and_beaker measured_stdev MAD beakerbeaker_id . mad_beaker_id vol_wet_and_beaker_stdev mad_beaker_id [PK1]
volume vol_wet_and_beaker_nentered_by vol_wet_and_beaker_cellsample_depth vol_dry_and_beaker MAD calibration history
sample_comment vol_dry_and_beaker _stdev mad_calibratin_id [PK1]sam_repository . repository vol_dry_and_beaker_n calibration_date_time
s_c_leg . leg vol_dry_and_beaker_cell calibration_types_c_sam_code . sam_code commentssam_sample_code_lab . s_c_l sample_date_time
Table 2—3 MAD query A (results).
Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idWb Water content, relative to bulk mass see MAD Query BWs Water content, relative to solid mass see MAD Query BCalculations depend on the volume measurement method used: A, B, or CBulk density Bulk density, method A, B, or C see MAD Query BDry density Dry density, method A or B see MAD Query BGrain density) Grain density, method A or B see MAD Query BPorosity Porosity, method A or B see MAD Query BVoid ratio Void ratio, method A or B see MAD Query B
Table 2—4 MAD query B (results, measurements, and parameters) (to be implemented).
Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idMethod A Indicates if method A was used [MAD Sample Data] fixed_volume Mb+beak Bulk mass of sample + beaker [MAD Sample Data] mass_wet_and_beakerMd+beak Dry mass of sample + beaker [MAD Sample Data] mass_dry_and_beakerVb+beak Bulk volume of sample (+ beaker for B) [MAD Sample Data] vol_wet_and_beakersd(Vb+beak) Std. dev. of n vol. measurements (for B) [MAD Sample Data] vol_wet_and_bkr_sdn(Vb+beak) No. of vol. measurements (for B) [MAD Sample Data] vol_wet_and_bkr_nc(Vb+beak) Cell no. used for vol. measurement (for B) [MAD Sample Data] vol_wet_and_bkr_cellVd+beak Dry volume of sample + beaker [MAD Sample Data] vol_wet_and_beaker
2—12 PP Handbook , Peter Blum , November, 1997
sd(Vd+beak) Std. dev. of n vol. measurements [MAD Sample Data] vol_wet_and_bkr_sdn(Vd+beak) No. of vol. measurements [MAD Sample Data] vol_wet_and_bkr_nc(Vd+beak) Cell no. used for vol. measurement. [MAD Sample Data] vol_wet_and_bkr_cellComments Comments commentsDate/Time Date and time of measurement sample_date_timeBeaker Beaker number [MAD Beaker History] beaker_numberMbeak Mass of beaker [MAD Beaker History] beaker_massVbeak Volume of beaker [MAD Beaker History] beaker_volumeMb Bulk mass = (Mb+beak) - MbeakMd Dry mass (includes evaporated salt) = (Md+beak) - MbeakMpw Mass of porewater = (Mb - Md) / 0.965Ms Mass of solids (salt-corrected) = (Md - 0.035*Mb) / 0.965Vpw Volume of porewater = Mpw / 1.024Msalt Mass of evaporated salt = Mpw - (Mb - Md)Vsalt Volume of evaporated salt = Msalt / 2.20Wb Water content relative to bulk mass = Mpw / MbWs Water content relative to solid mass = Mpw / MsFor volume method AVb(A) Bulk volume (method A) = (Vb+beak)Vs(A) Volume of solids (methods A) = Vb(A) - VpwFor volume method BVb(B) Bulk volume (method B) = (Vb+beak) - VbeakVs(B) Volume of solids (methods B) = Vb(B) - VpwFor volume method CVd(C) Dry volume (method C) = (Vd+beak) - VbeakVs(C) Volume of solids (method C) = Vd(C) - VsaltVb(C) Bulk volume (method C) = Vs(C) + VpwFor volume method A or BBulk density Bulk density, method A or B = Mb / Vb(A,B)Dry density Dry density, method A or B = Ms / Vb(A,B)Grain density) Grain density, method A or B = Ms / Vs(A,B)Porosity Porosity, method A or B = Vpw / Vb(A,B)Void ratio Void ratio, method A or B = Vpw / Vs(A,B)For volume method CBulk density Bulk density, method C = Mb / Vb(C)Dry density Dry density, method C = Ms / Vb(C)Grain density) Grain density, method C = Ms / Vs(C)Porosity Porosity, method C = Vpw / Vb(C)Void ratio Void ratio, method C = Vpw / Vs(C)
Table 2—4 MAD query B (results, measurements, and parameters) (to be implemented).
Table 2—5 MAD query C (control measurements) (to be implemented).
Short description Description DatabaseDate/Time Date/time of control measurement. [MAD Control Data] run_date_timeStandard Standard identification [MAD Control Data] ctrl_standard_idType Type of control meas. (mass or vol.) [MAD Control Data] control_typeExpected Expected value [MAD Control Data] expected_valueCell If pycnometer, cell number used [MAD Control Data] pyc_cell_noMeasured Measured value [MAD Control Data] measured_valueStdev. Std. dev. of multiple vol. meas. [MAD Control Data] measured_stdev
Table 2—6 MAD query D (beaker data) (to be implemented).
Short description Description DatabaseDate/Time Data/time of beaker meas. [MAD Beaker History] beaker_date_time
2—13PP Handbook , Peter Blum , November, 1997
Beaker Beaker number [MAD Beaker History] beaker_numberType Type of beaker (e.g., Pyrex 10 mL) [MAD Beaker History] beaker_typeMbeak Measured mass of beaker [MAD Beaker History] beaker_massVbeak Calculated volume of beaker [MAD Beaker History] beaker_volume
Table 2—6 MAD query D (beaker data) (to be implemented).
Table 2—7 MAD query E (calibration log) (to be implemented).
Short description Description DatabaseDate/Time Date/time of calibration calibration_date_timeType Type of calibration (mass or vol.) calibration_type
2—14 PP Handbook , Peter Blum , November, 1997
2—15PP Handbook , Peter Blum , November, 1997
eV)
ion
he
. A
core
f
).
3. GAMMA-RAY DENSIOMETRY
3.1. Principles
PHYSICAL BACKGROUND
Bulk density of sediments and rocks is estimated from the measurement of
gamma-ray attenuation (GRA) (Tittman and Wahl, 1965; Evans, 1965). The
familiar acronym GRAPE (Evans, 1965) stands for GRA porosity evaluator,
referring to the computer that Evans attached to the density measurement device to
compute porosity using an assumed grain density. The measurement device does
not estimate porosity, and is therefore referred to as GRA densiometer.
The principle is based on the facts that medium-energy gamma rays (0.1–1 M
interact with the formation material mainly by Compton scattering, that the
elements of most rock-forming minerals have similar Compton mass attenuat
coefficients, and that the electron density measured can easily be related to t
material bulk density. The 137Ce source used transmits gamma rays at 660 KeV
scintillation detector measures the gamma-ray beam transmitted through the
material. If the predominant interaction is Compton scattering, transmission o
gamma rays through matter can be related to the electron density by:
Yt = Yi e–nsd, (1)
where Yi is the flux incident on the scatterer of thickness d, Yt is the flux
transmitted through the scatterer, n is the number of scatterers per unit volume or
the electron density, and s is the Compton cross section for scattering per scatterer
in square centimeters per electron. Bulk density ρ of the material is related to the
electron density by
n = ρ NAv (Z/A), (2)
where Z is the atomic number or the number of electrons, A is the atomic mass of
the material, and NAv is the Avogadro number. Bulk density estimates are therefore
accurate for a wide range of lithologies if the Z/A of the constituent elements is
approximately constant. Variations of Z/A are indeed negligible for the most
common rock-forming elements. The GRA coefficient is defined as
µ = (Z/A) NAv × s (cm2/g). (3)
For the medium energy range of gamma rays and for materials with Z/A of about 1/
2, such as the most common minerals, the “Compton µ” is approximately 0.10
cm2/g, increasing with decreasing energy. For water, µ is about 11% higher than
for common minerals at a particular energy (e.g., Harms and Choquette, 1965
Sediments can therefore be regarded as two-phase systems in regard to GRA
(mineral-water mixtures).
3—1PP Handbook , Peter Blum , November, 1997
Equation on page 1 can now be written in the more frequently referenced form
Yt = Yi × e–ρµd (4)
and the expression for the bulk density becomes
ρ = ln (Yt / Yi) / µd. (5)
If the coefficient µ could be determined with sufficient accuracy, it could be used
directly to compute bulk density. However, µ is a function of detected gamma-ray
energy and is therefore dependent on the particular device, including source,
detector, spectral component used, and the material itself (degree of scattering). A
more practical and accurate method is to calibrate the gamma radiation with bulk
density standards as described later in this chapter.
ENVIRONMENTAL EFFECTS
Attenuation Coefficient of Minerals
An important assumption of this densiometry method is that for a given
measurement system the average attenuation coefficient µ is constant for the
measured materials. For a more accurate density estimate, variations in the average
composition of the material must be taken into consideration. If mineralogical
analysis determines that the average µ1 deviates significantly from the standard µ,
the following correction can be applied:
ρ1 = ρ × µ/µ1 , (6)
where the ratio of average coefficients can be calculated from reference tables.
Core Thickness The GRA routine calculations assume a constant core diameter of 66 mm. If voids
or otherwise incompletely filled core liner segments occur because of gas pressure,
gas escape, or other coring disturbances, the density estimate will be too low. (The
highest values are therefore the most reliable ones in disturbed cores.) Using a
thickness log obtained from core photographs or by other means, density can
easily be corrected for varying core thickness using
ρ1 = ρ × d/d1 . (7)
USE OF GRA DATA
GRA data provide a precise and densely sampled record of bulk density, an
indicator of lithology and porosity changes. The records are frequently used for
core-to-core correlation. Another important application is the calculation of
acoustic impedance and construction of synthetic seismograms.
3.2. MST (Whole-Core) GRA System
EQUIPMENT
Gamma-ray Source The 137Ce source used transmits gamma rays at 660 KeV.
3—2 PP Handbook , Peter Blum , November, 1997
6.6
%)
ast
the
as
er
e
n is
Scintillation Counter A standard NaI scintillation detector is used in conjunction with a universal
counter.
CALIBRATION
New Procedure GRA calibration assumes a two-phase system model for sediments and rocks,
where the two phases are the minerals and the interstitial water. Aluminum has an
attenuation coefficient similar to common minerals and is used as the mineral
phase standard. Pure water is used as the interstitial-water phase standard. The
actual standard consists of a telescoping aluminum rod (five elements of varying
thickness) mounted in a piece of core liner and filled with distilled water (Figure
3—1). The standard element i has an average bulk density ρi of
ρi = di /D × ρAl + (D – di)/D × ρwater (8)
where D is the maximum aluminum rod thickness (inner diameter of core liner,
cm), di is the diameter of the aluminum rod in element i, and ρAl and ρwater are the
densities of aluminum and water, respectively. The first element (porosity of 0
has a bulk density of aluminum (2.70 g/cm3) and the last element (porosity of
100%) has a bulk density of water at laboratory temperature (1.00 g/cm3).
Intermediate elements are used to verify the linearity of the ln(Y) to density
relationship, as well as the precise alignement of core and sensor. A linear le
squares fit through three to five calibration points (ln(counts/tcal), ρ) yields the
calibration coefficients m0 (intercept) and m1 (slope, negative). Total measured
counts are automatically divided by the counting time, tcal, to normalize the
coefficients to counts per second. Sample density is then determined:
ρcore = m0 + ln (counts/tsample) × m1 , (9)
where the measured counts are again normalized to counts per second using
sampling period, tsample , before the calibration coefficients are applied.
Old Procedure The present calibration procedure has been implemented only since Leg 169
(August 1996). Before that time, calibration was performed with two aluminum
cylinders of different thickness, but without water. The thinner aluminum rod w
cut to a diameter of 25 mm to give an “aluminum density of 1.00.” The counts
returned from measuring the thin aluminum rod were not compatible with the
Compton attenuation coefficient for water, however, and when measuring wat
the density was about 11% too high. A fluid-correction had to be applied to th
initial density estimate. This procedure is obsolete now, and no fluid correctio
required because water is used in the calibration procedure.
3—3PP Handbook , Peter Blum , November, 1997
ea-to
ility,
ud).
MEASUREMENT
The GRA is logged downcore automatically..
Figure 3—1 Schematic of GRA calibration. A. Physical standard used. B. Msurement geometery. C. Calibration principle. D. Application of calibration core measurement
PERFORMANCE
Precision Precision is proportional to the square root of the counts measured because
gamma-ray emission is subject to Poisson statistics (see “Natural Gamma
Radiation” chapter for additional explanation). The statistical uncertainty is
t N ± z (t N)1/2, (10)
where N is the count rate (counts per second, cps), t is the sampling period (s), and
z is the number of standard deviations for the normal distribution (0.68 probab
or confidence, for z = 1; 0.95 for z = 1.96, etc.). Measurements with the present
system have typically count rates of 10,000 (dense rock) to 20,000 cps (soft m
If measured for 4 s, the statistical error is therefore less than 40,000 ± 200, or
ln(counts/tcal)
Density (g/cm3)
m0(g/cm3) m1
(g/cm3)
• counts = total measured counts• tcal = calibration counting period (s)
ρcore = ρ'core × dcore / dstandard
S1 S3S2 S4 S5
Distilled water
Aluminum
49 mm2.28 g/cm3
32 mm1.83 g/cm3
66 mm2.72 g/cm3
16 mm1.42 g/cm3
0 mm1.00 g/cm3
Rod thickness:Average density:
Core liner
Center/support disk
Thin rodprovides
alignmentcontrol
GAMMA-RAY ATTENUATION DENSIOMETRY
Scintillationdetector
A
B C
D
137Ceγ source
dcore
Two-phase model: minerals = aluminum; pore water = distilled water
• tsam = sampling period (s)• dcore values are determined separately, standard report assumes full core liner, so that dcore = dstandard (= 66 mm for ODP)
ρ’core = m0 + m1× ln(counts/tsam)
3—4 PP Handbook , Peter Blum , November, 1997
0.5%. This shows that the high flux of the 137Ce source does not require excessive
counting times.
Accuracy Accuracy is limited by the assumption that the measured material has the same
attenuation coefficent as the calibration standards used. For general sediment-
water mixtures, this should be the case and errors should be less than 5%.
Spatial Resolution The GRA system allows high spatial resolution of about 0.5 cm.
DATA SPECIFICATIONS
Database Model
Notes: GRA control 1 are control measurements run the same way as a core section. GRA control 2 are measurement taken before run. GRA control 3 are control measurements from a standard mounted on the core boat.
Standard Queries
Table 3—1 GRA database model.
GRA section GRA control 1 GRA control 3 GRA calibrationgra_id [PK1] gra_ctrl_1_id [PK1] [FK] gra_ctrl_3_id [PK1] density_calibration_id [PK1]section_id run_number run_number calibration_date_time
run_number run_date_time run_date_time run_numberrun_date_time core_status requested_daq_period system_id
core_status liner_status actual_daq_period liner_statusliner_status requested_daq_interval density_calibration_id requested_daq_periodrequested_daq_interval requested_daq_period standard_id density_m0
requested_daq_period density_calibration_id meas_counts density_m1density_calibration_id standard_id density_mse
mst_gra_ctrl_2_id commentsmst_gra_ctrl_3_id GRA control 2
gra_ctrl_2_id [PK1] GRA calibration data
GRA section data GRA control 1 Data run_number density_calibration_id [PK1] [FK]gra_id [PK1] [FK] gra_ctrl_1_id [PK1] [FK] run_date_time mst_top_interval [PK2]
mst_top_interval [PK2] mst_top_interval [PK2] requested_daq_period standard_id [PK3][FK]mst_bottom_interval mst_bottom_interval actual_daq_period mst_bottom_intervalactual_daq_period actual_daq_period density_calibration_id standard_density
meas_counts meas_counts meas_counts actual_daq_periodcore_diameter core_diameter meas_counts
Table 3—2 GRA report.
Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [GRA Section]section_idDepth User-selected depth type Link through [GRA Section]section_idBulk density = [GRA Calibration] density_m0 +
ln ([GRA Section data] meas_counts)/ [GRA Section data] actual_daq_period)* [GRA Calibration] density_m1
B (optional): Parameters and measurementsRun Run number [GRA Section] run_numberDate/Time Run date/time [GRA Section] run_date_timeCore Status HALF or FULL [GRA Section] core_status
3—5PP Handbook , Peter Blum , November, 1997
Liner Status NONE, HALF or FULL [GRA Section] liner_status Req. Interval User-defined sampling interval (cm) [GRA Section] requested_daq_intervalReq. Period User-defined sampling period (s) [GRA Section] requested_daq_periodPeriod Measured sampling period (s) [GRA Section Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Section Data] meas_countsCore Dia. Core diameter, default = 6.6 cm [GRA Section Data] core_diameterCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [GRA Calibration] density_m1
Table 3—2 GRA report.
Table 3—3 GRA control 1 measurements (to be implemented).
Short description Description DatabaseBulk density =[GRA Calibration] density_m0 +
ln ([GRA Ctrl 1 Data] meas_counts/ [GRA Ctrl 1 Data] actual_daq_period)* [GRA Calibration] density_m1
Run Run number [GRA Ctrl 1] run_numberDate/Time Run date/time [GRA Ctrl 1] run_date_timeCore Status HALF or FULL [GRA Ctrl 1] core_statusLiner Status NONE, HALF or FULL [GRA Ctrl 1] liner_statusStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Interval Interval top [GRA Ctrl 1 Data] mst_top_intervalReq. Interval User-defined sampling interval (cm) [GRA Ctrl 1] requested_daq_intervalReq. Period User-defined sampling period (s) [GRA Ctrl 1] requested_daq_periodPeriod Measured sampling period (s) [GRA Ctrl 1 Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Ctrl 1 Data] meas_countsCore Dia. Core diameter, default = 6.6 cm [GRA Ctrl 1 Data] core_diameterCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [GRA Calibration] density_m1
Table 3—4 GRA control 2 measurements (to be implemented).
Short description Description DatabaseBulk density =[GRA Calibration] density_m0 +
ln ([GRA Ctrl 2 Data] meas_counts/ [GRA Ctrl 2 Data] actual_daq_period)* [GRA Calibration] density_m1
Run Run number [GRA Ctrl 2] run_numberDate/Time Run date/time [GRA Ctrl 2] run_date_timeReq. Period User-defined sampling period (s) [GRA Ctrl 2] requested_daq_periodPeriod Measured sampling period (s) [GRA Ctrl 2 Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Ctrl 2 Data] meas_countsCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [GRA Calibration] density_m1
Table 3—5 GRA control 3 measurements (to be implemented).
Short description Description DatabaseBulk density =[GRA Calibration] density_m0 +
ln ([GRA Ctrl 3 Data] meas_counts/ [GRA Ctrl 3 Data] actual_daq_period)
3—6 PP Handbook , Peter Blum , November, 1997
3.3. Split-core GRA System
ODP has purchased a split-core GRA system that will be implemented as soon as
resources become available. This system must be implemented together with the
latest model GEOTEK P-wave logger which provides the caliper measurement
required to correct split-core GRA measurements for uneven split-core thickness.
* [GRA Calibration] density_m1Run Run number [GRA Ctrl 3] run_numberDate/Time Run date/time [GRA Ctrl 3] run_date_timeStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Req. Period User-defined sampling period (s) [GRA Ctrl 3] requested_daq_periodPeriod Measured sampling period (s) [GRA Ctrl 3 Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Ctrl 3 Data] meas_countsCal. Date/Time Calibration date/time [GRA Calibration] Calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [GRA Calibration] density_m1
Table 3—5 GRA control 3 measurements (to be implemented).
Table 3—6 GRA calibration data (to be implemented).
Short description Description DatabaseDate/Time Calibration date/time [GRA Calibration] calibration_date_timeCal. m0 Calibration intercept (g/cm3) [GRA Calibration] density_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [GRA Calibration] density_m1
Cal. mse Calibration mean squared error [GRA Calibration] mseRun Run number [GRA Calibration] run_numberLiner Status NONE, HALF or FULL [GRA Calibration] liner_statusReq. Period User-defined sampling period (s) [GRA Calibration] requested_daq_periodComments Comments [GRA Calibration] commentsStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Density Density value from MST control [GRA Calibration Data] standard_densityInterval Interval top [GRA Calibration Data] mst_top_intervalPeriod Measured sampling period (s) [GRA Calibration Data] actual_daq_periodCounts Measured counts (not normalized) [GRA Calibration Data] meas_counts
3—7PP Handbook , Peter Blum , November, 1997
ral
4. MAGNETIC SUSCEPTIBILITY
4.1. Principles
PHYSICAL BACKGROUND
Magnetic susceptibility is the degree to which a material can be magnetized in an
external magnetic field. If the ratio of the magnetization is expressed per unit
volume, volume susceptibility is defined as
κ = M / H, (1)
where M is the volume magnetization induced in a material of susceptibility κ by
the applied external field H. Volume susceptibility is a dimensionless quantity. The
value depends on the measurement system used:
κ(SI) = 4π κ(cgs) = 4π G Oe–1, (2)
where G and Oe are abbreviations for Gauss and Orstedt, respectively. The SI
system should be used.
Mass, or specific, susceptibility is defined as
χ = κ / ρ , (3)
where ρ is the density of the material. The dimensions of mass susceptibility are
therefore m3/kg.
Magnetic susceptibility measured by the common methods is an apparent value
because of the self-demagnetizing effect associated with anisotropy connected
with the shape of magnetic bodies, such as magnetite grains (Thompson and
Oldfield, 1986). When a substance is magnetized its internal magnetic field is less
than the externally applied field. κi, the intrinsic susceptibility, relates the induced
magnetization to the internal magnetic field, whereas κe, the extrinsic
susceptibility which we actually observe, relates the induced magnetization to the
externally applied field. The relationship between the two susceptibilities can be
shown to be
κe = κi / (1 + Nκi), (4)
where N is the demagnetization factor. For a strongly magnetic mineral, such as
magnetite, Nκi > 1, and κe ~ 1/N. If N is known, there is a simple relationship
between the concentration of ferrimagnetic grains and the magnetic susceptibility.
This is the case for natural samples where the concentration of ferrimagnetic
minerals is a few percent or less. The measured susceptibility κ can be
approximated:
κ = ƒκe ~ ƒ/N , (5)
where ƒ is the volume fraction of ferrimagnetic grains. It is found that for natu
samples N is reasonably constant with a value close to 1/3. Thus, if the grain
4—1PP Handbook , Peter Blum , November, 1997
ally
in
ents
ces,
e
ge
ility
s can
with
mass.
4—
unt ld,
shapes are roughly spherical and the dominant mineral is magnetite, the volume
fraction (ƒ << 1) can be estimated by dividing the volume susceptibility by 3.
The commonly used magnetic susceptibility is measured at very low fields usu
not exceeding 0.5 mT (millitesla). It is therefore also referred to as low-field
susceptibility. For comparison, about 50 mT is required to change orientation
magnetite, and high-field susceptibility is obtained from hysteresis measurem
at fields of a few hundred millitesla.
In practice, volume susceptibility is generally measured with core logging devi
for which calibration factors must be established to account for the specific
geometry and effects of core conveyors and core liners. In the case of discret
specimen measurements, the mass of the specimen can be determined more
accurately than volume and specific susceptibility is directly obtained. If avera
grain density and moisture content of the specimen are known, the specimen
measurements can be compared with core logging measurements. Susceptib
values can then be normalized to mass and volume corrected for porosity. Thi
make susceptibility data more useful for quantitative estimates in conjunction
other mineral phases, such as carbonate, which are always normalized to dry
Susceptibility values for some common minerals and rocks are listed in Table
1.
Table 4—1 Susceptibilities of common minerals and rocks (simplified from Het al., 1995; supplemented with underlined values from Thompson and Oldfie1986).
κ (10-6 SI) χ (10-8 m3/kg)
Non-iron-bearing
Plastic (e.g., perspex, PVC) ~-5 ~-0.5
Ice or water -9 -1/-0.9
Calcite -7.5 to -39 -0.3 to -1.4
Quartz, feldspar, magnesite -13 to -17 -0.5 to -0.6
Kaolinite -50 -2
Halite, gypsum, anhydrite -10 to -60 -0.5 to -2.0
Serpentinite 3,100 to 75,000 120 to 2,900
Iron-bearing minerals
Illite, montmorillonite 330 to 410 5 to 13 to 15
Biotite 1,500 to 2,900 5 to 52 to 95 to 98
Orthopyroxene, olivines, amphiboles 1,500 to 1,800 1 to 43 to 50 to 130
Goethitea 1,100 to 12,000 26 to 70 to 280
Franklinites 450,000 8,700
Irona 3,900,000 50,000 to 2,000,000
Iron sulfides
Chalcopyrite 23 to 400 0.6 to 3 to 10
Pyrite 35-5,000 1 to 30 to 100
Pyrrhotitesa 460 to 1,400,000 10 to 5,000 to 30,000
4—2 PP Handbook , Peter Blum , November, 1997
unt ld,
ENVIRONMENTAL EFFECTS
Cores should be equilibrated to room temperature before measurement.
USE OF MAGNETIC SUSCEPTIBILITY
Magnetic susceptibility is used mostly as a relative proxy indicator for changes in
composition that can be linked to paleoclimate-controlled depositional processes.
The high precision and sensitivity of susceptibility loggers makes this
measurement extremely useful for core-to-core and core-downhole log correlation.
The physical link of magnetic susceptibility to particular sediment components,
ocean or wind current strength and direction, or provenance, usually requires more
detailed magnetic properties studies in a specialized shorebased laboratory.
Iron-titanium oxides
Hematitea 500 to 40,000 10 to 60 to 760
Maghemitea 2,000,000 to 2,500,000 40,000 to 50,000
Ilmenitea 2,200 to 3,800,000 46 to 200 to 80,000
Magnetitea 1,000,000 to 5,700,000 20,000 to 50,000 to 110,000
Titanomagnetite 130,000 to 620,000 2,500 to 12,000
Titanomaghemite 2,200,000 57,000
Ulvospinel 4,800 100
Average rock values
Sandstones, shales, limestones 0 to 25,000 0 to 1,200
Dolomite -10 to -940 -1 to -41
Clay 170 to 250 10 to 15
Coal 25 1.9
Basalt, diabase 250 to 180,000 8.4 - 6,100
Gabbro 1,000 to 90,000 26 to 3,000
Peridotite 96,000 to 200,000 3,000 to 6,200
Granite 0 to 50,000 0 to 1,900
Rhyolite 250 to 38,000 10 to 1,500
Amphibolite 750 25
Gneiss 0 to 25,000 0 to 900
Slate 0 to 38,000 0 to 1,400
Schist, phyllite 26 to 3,000 1 to 110
Serpentine 3,100 to 18,000 110 to 630
aRemanence-carrying minerals
Table 4—1 Susceptibilities of common minerals and rocks (simplified from Het al., 1995; supplemented with underlined values from Thompson and Oldfie1986).
4—3PP Handbook , Peter Blum , November, 1997
ibits
can
cy
ever
n,
f a
the
the
ed
in
4.2. Bartington MS2C Coil Sensor for Whole Cores (MSL)
EQUIPMENT
A Bartington Instruments MS2C system is integrated in the ODP MST for whole-
core logging. The main unit is the widely used, versatile MS2 susceptometer for
rapid measurements with a number of sensors. The unit has a measuring range of 1
×10–5 to 9999 × 10–5 (SI, volume specific) or 1 ×10–8 to 9999 × 10–8 (SI, mass
specific). It has five front panel controls: on-off switch, sensitivity range switch, SI
or cgs unit switch, zero button, measure button, and continuous measurement
switch. None of these controls needs to be operated because the instrument is
controlled by the MST program. The unit switch should always be on SI. The
range switch should be on the lower sensitivity (1.0), which allows rapid 1-s
measurements. The MST program allows the collection of multiple 1-s
measurements, which are immediately averaged. This is useful if the sampling
period is set, for example, at 3 s for the GRA measurement and there is time to take
three susceptometer readings simultaneously.
The MS2C loop sensor has an internal diameter of 80 mm, which corresponds to a
coil diameter of 88 mm. It operates at a frequency of 0.565 kHz and an alternating
field (AF) intensity of 80 A/m (= 0.1 mT). Temperature drift is less than 10–5 SI
per hour. The resolution of the loop is 2 × 10–6 SI on the 0.1 range (9 s measuring
time).
Dual-frequency Measurements
Fine-grained magnetic material (single-domain, about 0.003 µm diameter) exh
frequency-dependent susceptibility. The coefficient of frequency dependence
be determined from measurements in dual-frequency mode. The high frequen
used is 5.65 kHz. This mode of measurement is rarely used in general, has n
been requested onboard JOIDES Resolution, and is therefore not implemented for
routine measurements in the MST program.
CALIBRATION
Drift Correction The Bartington instrument is automatically zeroed at the beginning of each ru
before the core enters the loop. Instrument drift may occur during the period o
core section scan. To correct for the drift, a zero-background measurement
(MSbkgd) is taken at the end of a core section log. The drift is corrected under
assumption that it is linear over the time of interest (about 10 min.). The time
elapsed between the zeroing of the instrument at the beginning of the run and
background measurement, tbkgd, is measured. For each measurement within the
core (MSmeas) the elapsed time (t) is also measured, and the background-correct
susceptibility, Mscorr, is calculated as
MScorr = MSmeas + MSbkgd / tbkgd × t . (6)
Absolute Susceptibility Values
The Bartington instrument output values are relative, volume-specific
susceptibilities (κrelative), which must be corrected before they can be reported
4—4 PP Handbook , Peter Blum , November, 1997
2).
nting
SI units. Currently, no correction is implemented for standard queries from the
database. Three ways of correcting the susceptibilities are described here. The
third method is recommended for implementation on JOIDES Resolution in the
near future.
1. Bartington correction factors. Theoretically, the instrument output is in volume-
specific SI units for cores with diameters (d) passing exactly through the coil
diameter (D), i.e., if d/D = 1. Bartington provides a table relating values of d/D to
correction factors that must be applied to the relative susceptibility readings from
the meter. For d = 66 mm and D = 88 mm, d/D is 0.75 and the corresponding
correction factor is 1.48. Then,
κ = κrelative / 1.48 × 10–5 = 0.68 × 10–5 κrelative . (7)
This correction does not take into account other effects such as those from core
liner and core conveyer boat, etc.
2. Calibration with laboratory measurements. Absolute susceptibility is easily
measured on sample cubes in shorebased or shipboard laboratories (Kappabridge).
These measurements can be compared with corresponding readings from the
Bartington instrument. Empirical correlation from Leg 154 and Leg 162 data gave
correction factors of 7.7 × 10–6 and 8.0 × 10–6, respectively. On Leg 154, volumes
of specimens were not exactly determined and may have been slightly smaller than
assumed, which would underestimate the factor.
3. Calibration with core standard (Figure 4—1). The most straightforward
approach is to calibrate the instrument using a piece of core liner (40 cm long)
filled one-half with a homogenous mixture of magnetite (about 0.5%, pseudo-
single domain) and epoxy (κstandard ~ 1000 × 10–6) and one-half with pure water
(κwater = -9 × 10–6). The magnetic susceptibiltiy of the standard core is determined
once and precisely from splits. The instrument response is then related to the actual
volume susceptibiltiy, which also eliminates effects related to core geometry and
the core conveyor system. Once this method is implemented, calibration
coefficients can be routinely applied to future measurements and standard data
queries will return absolute susceptibility in SI units.
PERFORMANCE
Precision Precision is 2 × 10–6 (SI). Susceptibility values in natural, marine sediment
samples over an interval of only a few meters (Milankovitch or millennial scale
cyclicity) can range from a few tens to several thousands of 10–6 SI units.
Typically, variations are 2 to 3 orders of magnitude greater than the precision. This
makes magnetic susceptibility one of the most precise proxies for stratigraphic
changes and extremely useful for core-to-core correlation.
Accuracy Accuracy is 5% (according to Bartington).
Spatial Resolution We determined the full-width-half-maximum (FWHM) response from
measurements of four thin discs with varying amounts of iron dust (Figure 4—
The discs were mounted 20 cm apart from each other in a core liner, represe
4—5PP Handbook , Peter Blum , November, 1997
cts.
ion.. Cal-
thin strata of high susceptibility. Relative susceptibility values ranged from 40 ×
10–6 to 200 × 10–6. The four widths associated with half-maxima ranged from 4.0
to 4.4 cm. The width along the core axis corresponding to >99% response is about
15 cm. It is recommended that the first and last measurement in each core section
be taken 3–4 cm away from the edge to avoid any deconvolution of edge effe
Figure 4—1 Schematic of proposed magnetic susceptibility logger calibratA. physical standard used (To be implemented). B. Measurement geometry. Cibration principle. D. Application of calibration to core measurement.
MEASUREMENT
The magnetic susceptibility is logged downcore automatically.
Relative volume susceptibilty
Absolute volumesusceptibility
(SI)
m0 (SI)
m1(SI)
20 cm
κcore = κ'core × dcore2 / dstandard2
k1) = 1,000 × 10-6 (SI)k = -9 × 10-6 (SI)
Distilled water
Volume susceptibility:
Core linerPseudo-single domainmagnetite in epoxy;e.g. 0.001 mass fraction
1)To be determined exactly in laboratory from splits of the homogeneous standard material.
20 cm
Induction and measurement loop
MAGNETIC SUSCEPTIBILITY LOGGER
dcore
Core liner
A
B C
D
• xdrift determined using elapsed time since start of core section log• Standard report assumes dcore = dstandard (= 66 mm for ODP).
κ’core = m0 + m1 × (x - xdrift)
(x)
x = total relative susceptibility measured
4—6 PP Handbook , Peter Blum , November, 1997
tem.rious
Figure 4—2 Magnetic susceptibility response curves from the MS2C coil sysThe curves were obtained from the measurement of four thin discs with vaamounts of iron powder mounted in a piece of core liner.
0
5
10
15
20
25
30
-15 -10 -5 0 5 10 15
Rel
ativ
e m
agne
tic v
olum
e su
scep
tibili
ty
Distance from coil plane (cm)
12.4
5.18
3.92.64
Half-maximumvalues
4—7PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database Model
Notes: MSL control 1 are control measurements run the same way as a core section. MSL control 3 are control measurements from a standard mounted on the core boat.
Standard Queries
Table 4—2 MSL database model.
MSL section MSL control 1 MSL control 3msl_id [PK1] msl_ctrl_1_id [PK1] msl_ctrl_3_id [PK1]
section_id run_number run_numberrun_number run_date_time run_date_time
run_date_time core_status req_daqs_per_samplecore_status liner_status standard_idliner_status requested_daq_interval bkgd_susceptibility
requested_daq_interval req_daqs_per_sample bkgd_elapsed_zero_timereq_daqs_per_sample standard_id core_temperature
bkgd_susceptibility bkgd_susceptibility loop_temperaturebkgd_elapsed_zero_time bkgd_elapsed_zero_time meas_susceptibilty_meancore_temperature core_temperature sample_elapsed_zero_time
loop_temperature loop_temperature actual_daq_period
MSL section data MSL control 1 datamsl_id[PK1] [FK] msl_ctrl_1_id [PK1] [FK]
mst_top_interval [PK2] mst_top_interval [PK2]mst_bottom_interval mst_bottom_interval
meas_susceptibility_mean meas_susceptibility_meansample_elapsed_zero_time sample_elapsed_zero_timeactual_daq_period actual_daq_period
core_diameter core_diameter
Table 4—3 MSL report
Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [MSL Section] section_idDepth User-selected depth type Link through [MSL Section] section_idMag. susc. Drift-corrected magnetic susceptibility =[MSL Section Data] meas_suscept_mean
-[MSL Section] bkgd_susceptibility/ [MSL Section] bkg_elapsed_zero_time* [MSL Section Data] sam_elapsed_zero_time
B (optional): Parameters and measurementsRun Run number [MSL Section] run_numberDate/Time Run date/time [MSL Section] run_date_timeCore Status HALF or FULL [MSL Section] core_statusLiner Status NONE, HALF or FULL [MSL Section] liner_status Req. Interval User-defined sampling interval (cm) [MSL Section] requested_daq_intervalDaqs/sample User-def. data acquisitions per sample [MSL Section] req_daqs_per_sampleBkgd. Susc. Background at end of section run [MSL Section] bkgd_susceptibilityBkgd. Time Time elapsed since start of section. run [MSL Section] bkgd_elapsed_zero_timeCore Temp. Core temperature [MSL Section] core_temperatureLoop Temp. Loop temperature (to be implemented.) [MSL Section] loop_temperatureMag. Susc. Measured magnetic susceptibility [MSL Section Data] meas_suscept_meanElapsed Time time elapsed since start of run (s) [MSL Section Data] sam_elapsed_zero_timePeriod Actual sampling period [MSL Section Data] actual_daq_period Core Dia. Core diameter, default = 6.6 cm [MSL Section Data] core_diameter
4—8 PP Handbook , Peter Blum , November, 1997
4.3. MS2E1 Point Sensor for Split-Core Logger
At the end of 1996, ODP has purchased a magnetic susceptibility probe type MS2F
manufactured by Bartington. This miniature probe is ideally suited for
measurements on splitcore surfaces with roughness <1 mm. The FWHM response
Table 4—4 MSL control 1 measurements (to be implemented).
Short description Description DatabaseMag. susc. =[MSL Ctrl 1 Data] meas_suscept_mean
-[MSL Ctrl 1] bkgd_susceptibility/ [MSL Ctrl 1] bkg_elapsed_zero_time* [MSL Ctrl 1 Data] sam_elapsed_zero_time
Run Run number [MSL Ctrl 1] run_numberDate/Time Run date/time [MSL Ctrl 1] run_date_timeCore Status HALF or FULL [MSL Ctrl 1] core_statusLiner Status NONE, HALF or FULL [MSL Ctrl 1] liner_status Req. Interval User-defined sampling interval (cm) [MSL Ctrl 1] requested_daq_intervalDaqs/sample User-def. data acquisitions per sample [MSL Ctrl 1] req_daqs_per_sampleStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Bkgd. Susc. Background at end of section run [MSL Ctrl 1] bkgd_susceptibilityBkgd. Time Time elapsed since start of section run [MSL Ctrl 1] bkgd_elapsed_zero_timeCore Temp. Core temperature [MSL Ctrl 1] core_temperatureLoop Temp. Loop temperature (to be implemented.) [MSL Ctrl 1] loop_temperatureInterval Interval top [MSL Ctrl 1 Data] mst_top_intervalMag. Susc. Measured magnetic susceptibility [MSL Ctrl 1 Data] meas_suscept_meanElapsed Time time elapsed since start of run (s) [MSL Ctrl 1 Data] sam_elapsed_zero_timePeriod Actual sampling period [MSL Ctrl 1 Data] actual_daq_period Core Dia. Core diameter, default = 6.6 cm [MSL Ctrl 1 Data] core_diameter
Table 4—5 MSL control 3 measurements (to be implemented).
Short description Description DatabaseMag. susc. =[MSL Ctrl 3] meas_suscept_mean
-[MSL Ctrl 3] bkgd_susceptibility/ [MSL Ctrl 3] bkg_elapsed_zero_time* [MSL Ctrl 3] sam_elapsed_zero_time
Run Run number [MSL Ctrl 3] run_numberDate/Time Run date/time [MSL Ctrl 3] run_date_timeDaqs/sample User-def. data acquisitions per sample [MSL Ctrl 3] req_daqs_per_sampleStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Bkgd. Susc. Background at end of section run [MSL Ctrl 3] bkgd_susceptibilityBkgd. Time Time elapsed since start of section run [MSL Ctrl 3] bkgd_elapsed_zero_timeCore Temp. Core temperature [MSL Ctrl 3] core_temperatureLoop Temp. Loop temperature (to be implemented) [MSL Ctrl 3] loop_temperatureMag. Susc. Measured magnetic susceptibility [MSL Ctrl 3] meas_suscept_meanElapsed Time time elapsed since start of run (s) [MSL Ctrl 3] sam_elapsed_zero_timePeriod Actual sampling period [MSL Ctrl 3 Data] actual_daq_period
4—9PP Handbook , Peter Blum , November, 1997
measured in two axes on the plane of the sensing surface has linear dimensions of
3.8 × 10.5 mm, giving a spatial resolution 1 order of magnitude better than with the
loop sensor (FWHM of 42 mm). The depth response below the surface of
investigation drops to 50% at 1 mm and to 10% at 3.5 mm depth, requiring full
contact with a smooth surface. The sensor operates at a frequency of 2 kHz and has
the same resolution (2 ×10–6 SI on 0.1 range) and slightly larger measuring time
(1.2 s at 1.0 setting) than the coil sensor.
The MS2E1 sensing surface is at the end of a ceramic tube and is protected by a
thin ceramic (aluminum oxide) plate that must be in immediate contact with the
surface of investigation during the measurement. The tube is mounted on a metal
enclosure that houses the electronic circuitry. Soft or wet cores may be protected
by a thin plastic film of a thickness less than 0.05 mm. This also prevents the
pickup of potentially contaminating material that could create inaccuracies.
This sensor will be implemented on either the archive-half or working-half core
logging system. Both systems are in the design stage.
4—10 PP Handbook , Peter Blum , November, 1997
es
,
tes,
e
is
ific
at a
5. NATURAL GAMMA RADIATION
5.1. Principles
PHYSICAL BACKGROUND
Source of radiation Natural gamma radiation (NGR) is a useful lithologic parameter because the
“primeval” emitters are at secular equilibrium; i.e., radiation at characteristic
energies is constant with time (e.g., Adams and Gaspirini, 1970). Radioisotop
with sufficiently long life and that decay to produce an appreciable amount of
gamma rays are potassium (40K) with a half-life of 1.3 × 109 years, thorium
(232Th) with a half-life of 1.4 × 1010 years, and uranium (238U) with a half-life of
4.4 × 109 years. Minerals that fix K, U, and Th, such as clay minerals, are the
principal source of NGR. Other examples include arkosic silt and sandstones
potassium salts, bituminous and alunitic schists, phosphates, certain carbona
some coals, and acid or acido-basic igneous rocks (Serra, 1984).
Units Gamma rays are electromagnetic waves with frequencies between 1019 and 1021
Hz. They are emitted spontaneously from an atomic nucleus during radioactiv
decay, in packets referred to as photons. The energy transported by a photon
related to the wavelength λ or frequency ν by
E = hν = hc/λ (1)
where c is the velocity of light, and h is Planck’s constant (6.626 10–34 joule). The
energy is expressed in eV (electron-volts). For our purposes, the multiples KeV or
MeV are used. Each nuclear species (isotope) emits gamma rays of one or more
specific energies.
Activity, A, is the rate of radioactive decay and decreases exponentially according
to
A = λdN = λd N0 e-λdt (2)
where λd is the decay constant, and N and N0 are the number of atoms at times t
and t0, respectively. The original unit of activity was defined as the number of
disintegrations per second occuring in 1 g of 226Ra. In 1950, the Curie (Ci) was
redefined as exactly 3.7 × 1010 disintegrations per second. For most purposes, the
multiples mCi or µCi are used. Each radioactive species has an intrinsic spec
activity (ISA), which is the activity of a unit mass of the pure material (the
isotope). According to Adams and Weaver (1958), the relative activities of the
elements K, U, and Th, are 1, 1300, and 3600, respectively.
The well-logging industry created an arbitrary NGR activity scale, the GAPI
(gamma-ray, American Petroleum Industry) units. The GAPI scale is defined
5—1PP Handbook , Peter Blum , November, 1997
m
ition
n the
API
s a
no
aps
ndent
the
are
l
the
l
ld
n, a
1)
calibration pit at the University of Houston, Texas. The pit consists of three zones
of specific mixtures of Th, U, and K: two of low activity and one of high activity
(Belknap et al., 1959). The GAPI is defined as 1/200 of the deflection measured
between the high- and low-activity zones in the calibration pit. Limestones have
readings of 15–20 GAPI while shales vary from 75 to 150 GAPI, with maximu
readings of about 300 GAPI for very radioactive shales (Dewan, 1983). In add
to the master calibration in the test pit, secondary calibrations are carried out i
field.
Until recently, all commercial NGR logs, including the Schlumberger natural
gamma tool (NGT) logs generated during ODP operations, were reported in G
units. The MST NGR apparatus can obviously not be calibrated in the API
calibration pit, although Hoppie et al. (1994) suggested using downhole logs a
relative GAPI standard for core measurements. However, there appears to be
particular need or advantage to converting core measurements to GAPI, perh
because NGR core logging devices are not widely used. MST-NGR data are
therefore reported in counts per second (cps). This measurement unit is depe
on the device and the volume of material measured; i.e., the cps values from
same ODP cores are different if measured on a different instrument, and they
also different if measured in the ODP device but on different core diameters.
Perhaps the most useful absolute quantification of NGR is expressing the tota
activity in terms of the elemental concentrations of K, U, and Th. Quantifying
emitters is most useful for geologic interpretation. Because most well-logging
companies collect spectral NGR data these days, it is common for industry to
report the measurement in K, U, and Th concentrations. However, the spectra
analysis procedures are not standardized and the quality of the elemental yie
estimates may vary significantly. An ODP project is under way to manufacture
custom standards for the MST-NGR device that will allow elemental yield
estimates in the future.
Statistical Error Counting statistics play an important role in the measurement of radioactive
phenomena, which are random and discrete in nature. The Poisson distributio
simplified binomial distribution, is useful to discribe very small probabilities, p, of
individual observations (decay of one particle in our case) and a very large
number, n, of observations (number of particles in the sample). The parameterλ =
np then occurs for a given variable, X, with the probability, P(X;λ), defined by the
Poisson distribution:
P(X;λ) = (λX e–λ) / X!. (3)
In other words, P(X;λ) is the probability of observing X events when λ events are
expected. The distributions for λ = 4, 16, 49, and 100, where λ values represent
expected NGR count rates, are illustrated in Figure 5—1.
If λ >>1, the Poisson distribution approaches a normal distribution (Figure 5—
and is thus characterized by the mean, µ = λ, and the standard deviation, σ. The
important point is that for binomial distributions σ is related to µ, and for the
Poisson distribution:
σ = µ1/2. (4)
5—2 PP Handbook , Peter Blum , November, 1997
or, is
,
ing
re
for
e
0%.
tan-ror the
As a rule of thumb, the approximation to the normal distribution is adequate if µ Š
2σ; i.e., all but the left-most distribution in Figure 5—1 are adequate
approximations. For a normal distribution, the uncertainty, or the probable err
Perror = z σ , (5)
where z is the independent variable of the normal distribution function. We can
state that for about 68% of a large number of samples, the sample mean, y, will be
within the interval µ ± σ (z = 1); about 5% of the estimates will be outside the
interval µ ± 1.96σ (z = 1.96); etc.
In the case of NGR measurements, the sample mean y is the number of counts
observed, or
y = t N, (6)
where t is the sampling period (s) and N is the count rate (cps). The sample mean
y, is an unbiased estimator of µ. Because the value of µ is not known, we cannot
directly compute the error of the estimate N. However, statistical inference as
outlined here allows us to express the uncertainty as
t N ± z (t N)1/2 (7)
or
%error = z (t N)1/2 / t N × 100% = z / (t N)1/2 × 100%. (8)
Equation on page 3 states that the error decreases exponentially with increas
sampling period, t, increasing count rate, N, and decreasing level of confidence, z.
As a standard practice, z = 1. Standard deviations and relative statistical errors a
indicated for the example distributions in Figure 5—1. It should be noted that
the generally low NGR count rates, the sampling time t must be as long as th
measurement routine allows to reduce the statistical error significantly below 1
This is particularly true if spectral analyses are attempted.
Figure 5—1 Poisson distributions for four selected lambda values. One-sdard-deviation intervals are shown. The red line illustrates the relative erdecreasing exponentially with increasing count rate and also corresponds toPoisson distribution for λ = X.
0.00
0.10
0.20
0
10
20
30
40
50
0 20 40 60 80 100 120
P(X
,λ)
%error (one standard deviation)
X (estimated by observed counts, tN)
λ = µ = 16σ = 4
λ = µ = 100σ = 10
λ = µ = 4σ = 2
λ = µ = 49σ = 7
Relative error (%) for one standarddeviation (68% confidence)
5—3PP Handbook , Peter Blum , November, 1997
ents
is a
tially
s
rs.
well
and
e
ing
mic
ain
eV
out
e or
g a
NGR Total Counts Total counts refers to the integration of all counts over the photon energy range
between 0 and about 3.0 MeV (about 10 to 0.004 Angstrom wavelength). The total
count is a function of the combined contributions by K, U, and Th (particulary
from 0.5 to 3.0 MeV), matrix density resulting from Compton scattering
(particularly 0.1 – 0.6 Mev), and matrix lithology resulting from photoelectric
absorption (particularly 0 – 0.2 MeV).
The average total count rate from the MST-NGR device and terrigenous sedim
is about 30 cps. With a routine sampling time of 30 s, an average statistical
precision for one standard deviation of 900 ± 30, or 3%, may be achieved. This
good result for core-to-core correlation. However, it is practically impossible to
interprete the source of the radiation.
NGR Spectrometry The MST-NGR apparatus acquires 256-channel spectral data that could poten
be used for calculating elemental yields for K, Th, and U. NGR spectra of rock
and soils are composed of one emission peak of 40K, more than a dozen emission
peaks for the 238U series (mainly 214Bi), a similar number of 232Th series peaks
(mainly 208Tl and 228Ac), and background (Figure 5—2 and Figure 5—3). The
dominant background is produced by Compton scattering, photoelectric
absorption, and pair production, as well as by low-intensity, discrete emission
peaks of the 238U and 232Th series that disappear in the scatter. Spectral
background is a function of the abundance and distribution of primeval emitte
The goal of NGR spectrometry is to determine spectral components, peaks as
as parts of the background, which effectively estimate the abundance of K, U,
Th despite the odds of large scatter background and matrix effects.
NGR spectra have been analyzed over the past 30 years, mainly from wirelin
logging and airborne prospecting surveys. Various schemes of spectral stripp
have been proposed and evolved with time as electronic circuitry and sensor
performance improve. A basic concept was proposed by the International Ato
Energy Agency (IAEA, 1976) in which one interval is defined for each of the m
peaks of K, U, and Th, centered at the following characteristic energies: 1.46 M
for 40K, 2.62 MeV for 208Tl (Th), and 1.76 MeV for 214Bi (U) (Figure 5—2). The
problem with this concept is that the three main peak areas of K, Th, and U
represent only about 10% of the total spectrum in terms of counting rates. Ab
90% of the counts come from the low-energy part of the spectrum, which is
degraded by Compton scattering.
The subsequent trend in petroleum industry was to divide the spectrum into fiv
more contiguous windows and establish a calibration matrix that allows solvin
system of equations written as follows:
Wi = AiTh + BiU + CiK + ri, (9)
where Wi is the count rate from a predetermined energy window; Ai, Bi, and Ci are
the calibration coefficients derived empirically; and ri is a factor representing the
statistical error. The equations are then solved by minimizing r2 which is the sum
of all ri2. The initial limitation to five-channel data acquisition was related to
limitations in sensor efficiency and electronic circuitry.
5—4 PP Handbook , Peter Blum , November, 1997
gy
data.
asis.
ples
is
or of
e the
with
e of
rate
at
ents,
ical
be
30 s)
t area
mic
ure
re
e
An earlier version of the MST program collected spectral data in five energy
windows compatible with the Schlumberger NGT tool. The windows were (see
also Figure 5—2)
• Window 1: 0.2 – 0.5 MeV,
• Window 2: 0.5 – 1.1 MeV,
• Window 3: 1.1 – 1.59 KeV,
• Window 4: 1.59 – 2.0 MeV, and
• Window 5: 2.0 – 3.0 MeV.
Over the past few years, further improvements in downhole logging technolo
have allowed all survey companies to move to the acquisition of 256-channel
This makes any a priori spectral stripping unnecessary, as the optimum
information can be extracted from the spectra on a more rigorous statistical b
Blum et al. (1997) analyzed NGR spectra from the MST device using 2-hr sam
and calibrated the measurements with instrumental neutron acrivation analys
(INAA), inductively coupled plasma mass spectrometry (ICPMS), and X-ray
fluorescence (XRF) measurements on corresponding core specimens. The
abundance of K, U, and Th could be estimated with one standard deviation err
14%, 20%, and 25%, respectively. These conservative error estimates includ
error in the reference data. The next step for ODP is to obtain standard cores
known amounts of natural K, U, and Th and to derive a reliable calibration
coefficient through linear inversion that can be used to estimate the abundanc
K, U, and Th on a routine basis.
Spectral analysis requires significantly longer counting times than total count
sampling for a comparable precision. The work by Blum et al. (1997) shows th
the 256-channel spectrum can be subdivided into 11 relevant spectral compon
many of which have count rates of only a few counts per second. If the statist
error is to be kept at a few percent, a sampling period of several minutes will
required. In practice, this may be achieved by integrating shorter period (e.g.,
measurements taken a closer intervals (e.g., 10 cm) over a reasonably long
interval. Of course, the improved statistics will come with a reduced spacial
resolution.
ENVIRONMENTAL EFFECTS
Zero Background We refer to zero background as gamma radiation detected in the measuremen
without core material, which originates from a combinaton of high-energy cos
radiation, impurities in the NaI crystals, and soil contamination in the
measurement area. Zero background must be differentiated from spectral
background, which is a result of scattering within the core (Figure 5—2 and Fig
5—3). The value of zero background is easily determined by measuring a co
liner filled with distilled water, and the resulting spectrum is subtracted from th
total measured spectrum of a core sample.
5—5PP Handbook , Peter Blum , November, 1997
tem at
ow,
ude
or
t,
the
Figure 5—2 Natural gamma-ray spectrum acquired with the MST-NGR sys(from Blum et al., 1997). The inset shows high-energy portion of spectrumenlarged vertical scale. Counting time was 4 hr on a split core. W = windSCHLUM 1 through 5 are the five Schlumberger tool logging windows.
Core Volume Radiation counts are directly proportional to the volume of material in the
measurement area of the scintillation counters. The MST program can be
configured to avoid edge effects at the top and bottom of a core section. However,
voids within a core section, or narrow-diameter cores in general, are not corrected
for. The user can apply corrections based on core photographs or a high-resolution
volume proxy such as gamma-ray densiometry (e.g., Hoppie et al., 1994).
Pore volume may have some control on the NGR signal if variations in NGR
activity downcore are low. Porosity variations are proportional to the concentration
of the matrix, which may be proportional to the concentration of a radioactive
mineral in the formation. However, bulk density varies by less than a factor of two
in the natural materials with which we are concerned (1.4–2.7 g/cm3), whereas
concentration and activity of radioactive material can vary by 1 order of magnit
(e.g., clay-rich vs. carbonate-rich material).
USE OF NGR DATA
NGR measurements are used for three purposes: (1) correlation of core and/
downhole data sets in single or multiple holes, (2) evaluation of the clay/shale
content of a formation, and (3) abundance estimates for K, U, and Th. The firs
and to some degree the second, goals can be achieved by simply measuring
0
50
100
150
0
1000
2000
3000
0 500 1000 1500 2000 2500 3000
Calculated peak baselineMeasured zero backgroundMeasured countsSmoothed, corrected countsCalculated minima
Cou
nts/
chan
nel
Energy (KeV)
208 T
l (58
4 K
eV)
214 B
i (61
0 K
eV)
228 A
c (9
12 a
nd 9
66 K
eV)
214 B
i (11
20 K
eV)
214 B
i (17
64 K
eV)
208 T
l (26
15 K
eV)
B
2 3 4 5 6 9 11 14 15 160 12 1371 8 101 2 W3 W4 W5 W6 W108 9 W11 W12 W14 W15 16 17W0 W13W7
208 T
l (58
4 K
eV)
214 B
i (61
0 K
eV)
Interval
IAEA 1 IAEA 2 IAEA 3
SCHLUM 1 SCHLUM 2 SCHLUM 3 SCHLUM 4 SCHLUM 5
40K
(14
60 K
eV)
5—6 PP Handbook , Peter Blum , November, 1997
rou-o be7).
4),
T
de
le
ed
.
bulk emission (total counts) of the material. Elemental analysis is a more complex
process that requires spectral data acquisition and longer sampling times.
Figure 5—3 Schematic illustration of A. zero-background (to be subtracted tinely; B. zero-background corrected spectrum; and C. spectral background (tdiscriminated in spectral analysis if warranted). Modified from Blum et al. (199
5.2. MST-NGR System
EQUIPMENT
The MST-NGR device consists of four shielded scintillation counters arranged at
90° angles from each other in a plane orthogonal to the core track (Figure 5—
power supply and amplifiers, automated data acquisition control as part of MS
program, and independent PC with EG&G Maestro software for spectral data
acquisition and analysis. The scintillation counters contain doped sodium iodi
(NaI) crystals (3 × 3 in or 7.6 × 7.6 cm) and photomultipliers to produce countab
pulses. When a gamma ray strikes the crystal, a single photon of light is emitt
and strikes a photocathode made from cesium antimony or silver magnesium
Intensity(counts/channel)
Energy
Zero background-corrected spectrum
Window Wi
Intensity(counts/channel)
Energy interval Ii
Window Wi+1
Calculated minimum
Peak baseline
Selectedwindowboundary
A
B
Peak boundary
Background area Bi
Peak area Pi
Energy
C
Intensity(counts/channel)
Energy
Measured zero-background
Measured counts
5—7PP Handbook , Peter Blum , November, 1997
GR
Photons hitting the photocathode release bundles of electrons, which are
accelerated in an electric field to strike a series of anodes of successively higher
potential. A final electrode conducts a small current through a measure resistor to
give a voltage pulse, signaling that a gamma ray struck the NaI crystal. Analog
signals are converted to digital signals, and the peak height of each pulse is
measured and stored in the appropriate one of 256 channels. The tool response
depends on two factors: (1) detector efficiency or sensitivity; i.e., the number of
gamma-rays detected per unit concentration; and (2) energy response of the
detector; i.e., the resolution and conversion slope of volts input versus output. All
detector and electronics components of the MST-NGR device were supplied by
EG&G ORTEC, Inc. The apparatus was assembled onboard JOIDES Resolution in
March 1993.
Figure 5—4 Configuration of four natural gamma ray sensors in the MST-Nsystem.
CALIBRATION
Tuning the Amplifiers The NGR system contains four scintillation counters that must be tuned to all
return the same signal level for a particular emission energy. Amplification of
signals from the four counters may drift, and it is therefore necessary to adjust the
gain at least at the beginning of each leg. Currently, the independent MAESTRO
program is used to adjust the gain, and the ODP technician should perform the
tuning. The operator should be familiar with the general character of the potassium
and thorium spectra.
Using a potassium source, the MAESTRO display of the spectrum should show
one sharp peak. If more than one peak or a very broad peak are displayed, the
sensor gains must be adjusted. This is done by disconnecting three of the four
MST bench
MST core pass-through
Photo-multipliers
3 in. × 3 in. NaI crystals
Lead shielding
Cu tubing
CATWALK CORELAB
Sensor 1
Sensor 4 Sensor 3
Sensor 2
5—8 PP Handbook , Peter Blum , November, 1997
fourontrol
m
t be
the
ents
he
ps.
hout a
the
ot
th
ment
s with
sensors from the amplifiers and marking the peak of the connected sensor. Then,
the next counter is connected and all others disconnected and the gain is adjusted
until the peak falls exactly on the marker. The same is done with the remaining two
counters. The connections between sensor numbers, leads, and gain adjustment
knobs on the amplifiers are shown in Figure 5—5.
Figure 5—5 Schematic diagram of the gain control panel used to tune thesensor responses. The numbers indicate how lead connects relate to gain cknobs.
This procedure is tedious because each time the gain is adjusted a new counting
period must be initiated. A “hot” source, such as thorium, accelerates the
procedure some. However, there are several characteristic peaks in the thoriu
spectrum, and operators must be very confident that they can match the
appropriate ones.
Once the four scintillation counter gains are tuned, an energy calibration mus
performed.
Zero-Background Correction
Zero background is the radiation caused by impurities in the system, including
NaI crystal itself, and cosmic radiation by-passing the lead shielding. The
background is measured with a water-filled core liner in the system. Counting
times of 1 min and more provide accurate values. Many background measurem
in 1993 and 1994, some taken with counting times of a few hours, show that t
values are constant throughout the day and over a period of weeks at 8 to 9 c
Standard deviations are less than 1 cps. Background measurements taken wit
water core in the device tend to be higher by 1 to 2 cps, presumably because
water core helps to shield the sensor from external radiation.
The zero background is relatively constant and frequent measurements are n
required. A daily control measurement to check on potential contamination wi
soil is sufficient. The ODP standard query uses the latest background measure
in the database taken prior to the core measurement.
Energy Calibration Radioactive decay events are recorded by 256 channels according to photon
energy. These channels must be calibrated for energy by measuring standard
characteristic emission peaks at known energies. A linear regression yields
3 2
4 1
FINE GAINOne full turn to the right moves apeak several tens of KeV to theright
COARSE GAIN:Leave the following settings:Position 1: gain 2Position 2: gain 2Position 3: gain 4Position 4: gain 2
POSITION NUMBER:Corresponds to sensor number
3
2
4
1
LEADS:Disconnect three offour leads to isolateone sensor response
5—9PP Handbook , Peter Blum , November, 1997
sed
sed. D.
calibration coefficients that are used by the ODP standard query to convert channel
numbers to energy intervals.
At present, potassium and thorium standards are used with main emission peaks at
1.46 MeV and 2.62 MeV for 40K and 232Th, respectively. These peaks are the most
suitable ones because they span the energy spectrum of general interest. The low-
energy, high-count spectrum may be somewhat distorted because of the non-
linearity of the detection and recording system.
The physical standard illustrated in Figure 5—6 is part of a project that awaits
resource allocation. At present, sources of convenience for K (core filled with
KCl) and Th (Schlumberger calibration pad or small flask with Th oxide) are u
for the calibration.
Figure 5—6 Schematic of NGR energy calibration. A. Physical standard u(To be implemented). B. Measurement geometery. C. Calibration principleApplication of calibration to core measurement.
Channel number i
Known energy ofcharacteristic peak (KeV)
m0 (KeV)
m1(KeV)
NATURAL GAMMA RADIATION: I. ENERGY SPECTRUM
Energy of ith channel = m0 + m1 × i
K ~ 5%; U ~ 10 ppm; Th ~ 20 ppm
Distilled water
Approx. concentrations 1):
Core liner
1)To be determined exactly in the laboratory from aliquots of the standard material.
40 cm long
dcore
Core liner
Homogeneous mixture of natural K, U, and Th, and epoxy matrix
Scintill
ation
det
ecto
r
A
B C
D (Tcps)’core = Σ (cps)channel ii = b
i = a
• Tcps = Total counts per second• a = (0 KeV - m0) / m1 [a ≥ 1]; b = (3000 KeV - m0) / m1 [b ≤ 256]• For standard report: dcore = dstd (= 66 mm for ODP)
(Tcps)core = (Tcps)’core × dcore2 / dstd2
256
5—10 PP Handbook , Peter Blum , November, 1997
R
,
the
ad
s not
ation
the
sors:
cm
ayers
rent
Elemental Yield Calibration
Elemental yield calibration is required only if the goal is to estimate the abundance
of K, U, and Th from spectral analysis. The sampling time must be sufficiently
long for this purpose (at least several minutes). Currently, the calibration standards
required to obtain a reliable estimation matrix do not exist. They have been
specified and will be purchased when funds are available.
PERFORMANCE
Precision Because radioactive emissions are random and discrete, they follow the Poisson
distribution, which in turn allows calculating the measurement precision from the
number of accumulated counts. Equations on page 2 through on page 3 and Figure
5—1 in this chapter explain the principles.
Accuracy Accuracy estimates for K, U, and Th elemental abundance obtained from NG
measurements depend on the accuracy and precision of of the reference data
calibration, and the spectral analysis and statistical procedures used to obtain
abundance estimates. Blum et al. (1997) found that K, U, and Th estimates h
total errors of 16%, 30%, and 20%, respectively. This is a conservative error
estimate that includes the uncertainty in the reference values (3%–7%), and i
based on the best possible optimization procedures. Custom-fabricated calibr
standards and more rigorous inversion methods should lead to more accurate
abundance estimates in the future.
Spatial Resolution The diameter of the NaI crystals is 7.6 cm (3 in) and represents the intrinsic
resolution of the system. However, actual spatial resolution is limited because
geometry of the device allows a longer piece of core to be exposed to the sen
the total response curve has a width of about 40 cm. The HMFW is about 12
and represents perhaps the most reasonable measure of spatial resolution. L
thinner than that can be detected only if they have NGR emissions vastly diffe
from the surrounding core.
MEASUREMENT
NGR is logged downcore automatically.
5—11PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database Model
Notes: NGR Ctrl 1 are control measurements run the same way as a core section. NGR Ctrl 3 are routine measurements on standards mounted on core boat (pure water, essentially a background measurement). NGR Background is for longer (precise) measurements of the background radiation due to cosmic radiation (imperfect shielding) and contamination of the system (crystal impurities, accumulated dirt). Recommended data acquisition period is 10 min (more for special studies with longer counting times on core material). Spectral data are available over the network on the ship. After the leg, they are transferred to off-line media and made available on request.
Standard Queries
Table 1—1 NGR database model.
NGR section NGR control 1 NGR control 3 NGR calibration
ngr_id [PK1] ngr_ctrl_1_id [PK1] ngr_ctrl_3_id [PK1] energy_calibration_id [PK1]
section_id run_number run_number calibration_date_time
run_number run_date_time run_date_time run_number
run_date_time core_status requested_daq_period system_id
core_status liner_status energy_calibration_id channel_energy_m0
liner_status requested_daq_interval standard_id channel_energy_m1
requested_daq_interval requested_daq_period energy_background_id channel_energy_mse
requested_daq_period energy_calibration_id actual_daq_period comments
energy_calibration_id standard_id
energy_background_id energy_background_id NGR calibration data
mst_ngr_ctrl_3_id energy_calibration_id [PK1] [FK]
channel [PK2]
NGR section data NGR control 1 data isotope
ngr_id [PK1] [FK] ngr_ctrl_1_id [PK1] [FK] energy
mst_top_interval [PK2] mst_top_interval [PK2]
mst_bottom_interval mst_bottom_interval NGR background
actual_daq_period actual_daq_period energy_background_id [PK1]
core_diameter core_diameter run_number
total_counts_sec total_counts_sec run_date_time
standard_id
liner_status
requested_daq_period
energy_calibration_id
total_counts_sec
actual_daq_period
NGR spectra data NGR con. 1 spectra data NGR con. 3 spectra data NGR background spectra
ngr_id [PK1] [FK] ngr_ctrl_1_id [PK1] [FK] ngr_ctrl_3_id [PK1] [FK] energy_background_id [PK1]
mst_top_interval [PK2] [FK] mst_top_interval [PK2] [FK] roi_start_channel [PK2] roi_start_channel [PK2]
roi_start_channel [PK3] roi_start_channel [PK3] roi_length_channel roi_length_channel
roi_length_channel roi_length_channel meas_counts actual_daq_period
meas_counts meas_counts meas_counts
Table 1—2 NGR report.
Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [NGR Section] section_idDepth User-selected depth type Link through [NGR Section] section_idTotal counts Zero-background-corrected total counts = [NGR Section data] total_counts_sec -
[NGR Background] total_counts_sec
5—12 PP Handbook , Peter Blum , November, 1997
B (optional): Parameters and measurementsRun Run number [NGR Section] run_numberDate/Time Run date/time [NGR Section] run_date_timeCore Status HALF or FULL [NGR Section] core_statusLiner Status NONE, HALF or FULL [NGR Section] liner_status Req. Interval User-defined sampling interval (cm) [NGR Section] requested_daq_intervalReq. Period User-defined sampling period (s) [NGR Section] requested_daq_periodPeriod Actual sampling period (s0 [NGR Section Data] actual_daq_periodDiameter Core diameter (default + 6.6 cm) [NGR Section Data] core_diameterCounts Total counts (cps) [NGR Section Data] total_counts_secCal. Date/Time Calibration date/time [NGR Calibration] calibration_date_timeCal. m0 Calibration intercept (KeV) [NGR Calibration] channel_energy_m0Cal. m1 Calibration slope (KeV/channel) [NGR Calibration] channel_energy_m1Cal. mse Calibration mean squared error [NGR Calibration] channel_energy_mseBkgd Background total counts (cps) [NGR Background] total_counts_sec
Table 1—2 NGR report.
Table 1—3 NGR control 1 measurements (to be implemented).
Short description Description DatabaseTotal counts = [NGR Ctrl 1 data] total_counts_sec -
[NGR Background] total_counts_secRun Run number [NGR Ctrl 1] run_numberDate/Time Run date/time [NGR Ctrl 1] run_date_timeCore Status HALF or FULL [NGR Ctrl 1] core_statusLiner Status NONE, HALF or FULL [NGR Ctrl 1] liner_status Req. Interval User-defined sampling interval (cm) [NGR Ctrl 1] requested_daq_intervalReq. Period User-defined sampling period (s) [NGR Ctrl 1] requested_daq_periodStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Interval Interval top [NGR Ctrl 1 Data] mst_top_intervalPeriod Actual sampling period (s) [NGR Ctrl 1 Data] actual_daq_periodDiameter Core diameter (default + 6.6 cm) [NGR Ctrl 1 Data] core_diameterCounts Total counts (cps) [NGR Ctrl 1 Data] total_counts_secCal. Date/Time Calibration date/time [NGR Calibration] calibration_date_timeCal. m0 Calibration intercept (g/cm3) [NGR Calibration] channel_energy_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_m1
Cal. mse Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_mse
Bkgd Background total counts (cps) [NGR Background] total_counts_sec
Table 1—4 NGR control 3 measurements (to be implemented).
Short description Description DatabaseTotal counts =[NGR Ctrl 3] total_counts_sec -
[NGR Background] total_counts_secRun Run number [NGR Ctrl 3] run_numberDate/Time Run date/time [NGR Ctrl 3] run_date_timeReq. Period User-defined sampling period (s) [NGR Ctrl 3] requested_daq_periodPeriod Actual sampling period (s0 [NGR Ctrl 3] actual_daq_periodCounts Total counts (cps) [NGR Ctrl 3] total_counts_secStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Cal. Date/Time Calibration date/time [NGR Calibration] calibration_date_time
5—13PP Handbook , Peter Blum , November, 1997
Cal. m0 Calibration intercept (g/cm3) [NGR Calibration] channel_energy_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_m1
Cal. mse Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_mse
Bkgd Background total counts (cps) [NGR Background] total_counts_sec
Table 1—4 NGR control 3 measurements (to be implemented).
Table 1—5 NGR calibration data (to be implemented).
Short description Description DatabaseDate/Time Calibration date/time [NGR Calibration] calibration_date_timeRun Run number [NGR Calibration] run_numberCal. m0 Calibration intercept m0 (MeV) [NGR Calibration] channel_energy_m0Cal. m1 Calibration slope m1 (MeV/channel) [NGR Calibration] channel_energy_m1Cal. mse Calibration mean squared error [NGR Calibration] channel_energy_mseComments Comments [NGR Calibration] commentsChannel Channel number [NGR Calibration Data] channelIsotope Characteristic isotope emitting at peak [NGR Calibration Data] isotopeEnergy Energy of emission at peak [NGR Calibration Data] energy
Table 1—6 NGR zero background (to be implemented).
Short description Description DatabaseDate/Time Date/time of background meas. [NGR Background] run_date_timeRun Run number [NGR Background] run_numberLiner Status NONE, HALF or FULL [NGR Background] liner_statusReq. Period User-defined sampling period (s) [NGR Background] requested_daq_periodPeriod Actual sampling period (s) [NGR Background] actual_daq_periodCounts Total background counts (cps) [NGR Background] total_counts_secCal. Date/Time Calibration date/time [NGR Calibration] calibration_date_timeCal. m0 Calibration intercept (g/cm3) [NGR Calibration] channel_energy_m0
Cal. m1 Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_m1
Cal. mse Calibration slope ([g/cm3)]/cps) [NGR Calibration] channel_energy_mse
5—14 PP Handbook , Peter Blum , November, 1997
s
e
.
nce
6. P-WAVE VELOCITY
6.1. Principles
PHYSICAL BACKGROUND
The basic relationship for sonic velocity is
v = d / t, (1)
where d is the distance traveled through the material (in meters) and t is the travel
time through the material (in seconds). The ODP user can choose among four
measurement systems, each using a piezoelectric transducer pair. The basic
equation is adapted to reflect the particular measurement condition.
The PWL system is mounted on the whole-core MST and measures d and t
horizontally through the whole core, with or without the core liner. The
measurements are anywhere in the x-y plane in the conventional core orientation
system (Figure 6—1). PWS1 and PWS2 transducer pairs are designed to be
inserted into the soft and semiconsolidated sediment of split cores. The two
systems are mounted orthogonal to each other to measure along the core axi
(PWS1, z-direction), and perpendicular to the axis and within the split plane
(PWS2, y-direction). The core liner is not involved in these measurements. Th
PWS3 system allows measurements on split cores in the x-direction, with or
without the core liner. In addition, other directions can be measured with the
PWS3 system on cubic or cylindrical, consolidated or lithified core specimens
Total travel time measured between the transducers includes three types of
“delays”:
• delay related to transducer faces and electronic circuitry (tdelay),
• delay related to the peak detection procedure (tpulse), and
• transit time through the core liner, if applicable (tliner).
These delays are explained in detail in the “Calibration” sections. Travel dista
measurements must also be corrected for the liner wall thickness, dliner, if core
liners are involved.
For routine measurements on whole cores in core liners (PWL system):
vcore = (d’core – 2dliner) / (t0 – tpulse – tdelay – 2tliner) × 1000, (2)
where
vcore = corrected velocity through core (km/s),
d’core = measured diameter of core and liner (mm),d1iner = liner wall thickness (mm), andt0 = measured total travel time (µs).
6—1PP Handbook , Peter Blum , November, 1997
user.
iscrete
r
ere
ields
n
ered
s
ands
ted
voids
very.
d to
o
ore
r
other
e at
at
The
”
ly, gas
ding
om
For the PWS3 system measuring the transit time through the core material and a
split liner, Equation on page 1 is modified only by the factor of 2 for the core liner
correction:
vcore = (d’core – dliner) / (t0 – tpulse – tdelay – tliner) × 1000. (3)
The liner wall correction is applied by default. If rock cuboids or cylinders are
measured with the PWS3 system, the liner correction must be disabled by the
For the PWS1 and PWS2 systems as well as the PWS3 system measuring d
core specimens:
vcore = d’core / (t0 – tpulse – tdelay) × 1000. (4)
With the PWL and PWS3 systems, d’core is determined for each measurement. Fo
the PWS1 and PWS2 systems, the constant d’core is obtained through calibration in
water.
ENVIRONMENTAL EFFECTS
Core Quality Core quality strongly affects the data quality or the ability to aquire P-wave
velocity data. Even if good acoustic coupling with the core liner is achieved, th
may be insufficient coupling between the core material and the core liner. A
typical observation is that the uppermost sediment (seafloor to 10–50 mbsf) y
good data, presumably because the high porosity and limited elastic expansio
maintains cohesion in the soft sediment. Below this and to depth of a few hund
meters, the signal is often strongly attenuated. This effect is more severe if ga
escape is observed on the core cutting platform. Free gas in the sediment exp
greatly upon recovery and may create voids and microcracks that make P-wave
measurements impossible. Once the sediment becomes sufficiently consolida
and lithified, measurements tend to be more successful.
Signal Strength and Attenuation
The measured signal degenerates because of incompletely filled core liner or
or because of attenuation caused by microcracks that formed during core reco
This degeneration is partly reflected by the gain (signal strength) factor applie
the original signal by the automated gain control. However, signal strength als
represents the grain size of the sediment, and low-strength signals can theref
not simply be interpreted as proportional to attenuation. If a filter is applied fo
data reduction, the relative decrease in signal strength from one sample to the
should be taken into consideration as well as the absolute signal strength.
Temperature Equilibrium
P-wave velocity in water is sensitive to temperature. Cores should therefore b
equilibrium when they are measured. Cores are routinely left to equilibrate for
least 4 hr (ODP technicians monitor the temperature with a thremistor probe).
core temperature should be entered by the operator (see “Data Specifications
section; currently this is a manual operation).
In Situ vs. Core Measurements
Measurements on sediment or rock cores differ from in situ measurements
because cores expand on recovery because of lithostatic rebound and, possib
expansion and other factors. Calibration of the measurements with correspon
sonic well logs is recommended. Velocities from sediment cores originating fr
6—2 PP Handbook , Peter Blum , November, 1997
1
sonic
ution
the
—1).
than
s
in
ition,
ich is
more than a few hundred meters below seafloor typically are compatible with
downhole measurements to within less than 3%, whereas shallower core
measurements tend to be up to 5% lower than corresponding downhole
measurements.
USE OF P-WAVE VELOCITY DATA
P-wave velocity varies with the lithology, porosity, and bulk density of the
material; state of stress, such as lithostatic pressure; and fabric or degree of
fracturing. In marine sediments and rocks, velocity values are also controlled by
the degree of consolidation and lithification, fracturing, occurence and abundance
of free gas and gas hydrate, etc. Together with density measurements, sonic
velocity is used to calculate acoustic impedance, or reflection coefficients, which
can be used to estimate the depth of reflectors observed in seismic profiles and to
construct synthetic seismic profiles. Core measurement should be calibrated with
in situ measurements wherever possible.
6.2. MST (Whole-Core) P-Wave Logger (PWL)
EQUIPMENT
The PWL system was purchased from GEOTEK Ltd. (UK) and modified for the
specific requirements of ODP routine core logging. The core travels between two
piezoelectric transducers mounted in epoxy and stainless-steel housings. The two
transducers are used as a transmitter and receiver. Acoustic coupling is through an
epoxy resin surface and is enhanced by a water film supplied by an automated
sprinkler system. Firm contact is ensured through spring-loaded transducer
housings. Two serially mounted linear variable-displacement transformers (LVDT)
measure the diameter of the core (plus liner). A hydraulic piston system displaces
the transducers by several millimeters at the beginning and end of a core section
log to prevent the end caps from catching on to the transducers.
A 500-kHz pulse (2-µs wave period; 120 V) is produced at a repetition rate of
kHz. The pulse is sent to the transmitter transducer, which generates an ultra
compressional pulse at about 500 kHz. Pulse timing is measured with a resol
of 50 ns. The P-wave propagates through the core, is received by the receiver
transducer, and is amplified by an automatic gain control amplifier to produce
received signal. A delay pulse is generated after the transmit pulse (Figure 6
The delay time must be set (thumbwheel control) to a few microseconds less
the arrival of the signal. A 20-µs gate pulse follows the delay pulse; during thi
period a peak detector senses the peak voltage of the received signal after ga
control. A threshold detector is used for automatic peak detection: it is set low
when a preset fraction of the peak level (the threshold level) is crossed. In add
a zero-crossing detector detects all zero voltage crossings. A count pulse, wh
displayed on the instrument unit in microseconds, is generated at a time
6—3PP Handbook , Peter Blum , November, 1997
e
e
tion
Use e
t (or the s).
corresponding to the first zero crossing after the first low-threshold event. The
resulting pulse detection delay is one wavelength (2 µs) if the the first peak is
positive and 1.5 wavelengths (3 µs) if the first peak is negative, because the
threshold is always detected on the negative signal. By detecting the travel tim
using a zero-crossing technique, the travel time recorded is independent of th
signal amplitude.
Figure 6—1 Schematic diagram of pulse timing and threshold peak detec(modified after GEOTEK manual).
CALIBRATION
Pulse Detection Pulse detection settings are checked by ODP personnel on a regular basis and
should not require any adjustment by the user. It may become necessary to adjust
the pulse from time to time (e.g., when equipment is replaced or materials of
different geometry are measured). The oscilloscope normally connected for the
user to monitor the received signal can be used for adjusting pulse timing and
threshold detection. The following procedure is modified after the GEOTEK
manual:
1. Place a water core (large signal) between the transducers. Ensure a good (wet) coupling. The “Level” indicator should be high. A clear received pulse should be visible on channel 1 and the delay pulse on channel 2.the thumbwheel to adjust the delay time such that it ends just prior to thstart of the received signal (approximately 35 µs).
2. Check that the count pulse occurs at the first zero-crossing after the firssecond, if wired in the opposite sense) negative excursion. If this is notcase, the threshold voltage level requires adjustment (procedure follow
Transmitter pulse (2 µs)
Delay pulse (0-999.9 µs)Gate pulse (0-999.9 µs)
Oscilloscope trigger
Threshold level
Threshold detector
Zero-crossing detector
Received signal
Count pulse
First zero crossing after first threshold interval
tpulse
6—4 PP Handbook , Peter Blum , November, 1997
irst
ent
nge
t in
t at ond,
tire
ult of
rent
ere.
ows a
lay”,
at are
the
the
ver,
t
shold
e 6—
o be
ucer
cted.
ment
the
sses
3. Remove the water core and observe the signal through air (small signal; “Level” indicator low). Check again that the count pulse occurs at the fzero-crossing after the first (or second, if wired in the opposite sense) negative excursion. If this is not the case, the threshold voltage level requires adjustment (procedure follows).
4. Check that the travel time shown on the LCD is in approximate agreemwith the travel time shown on the oscilloscope.
If the threshold level requires adjustment:
1. The purpose is to ensure that that the threshold operating level Vop consistently picks first negative excursions over as wide an amplitude raas possible.
2. Place the water core (large signal) between the transducers. Adjust “Sehigh” so that the threshold operates just on the first (or second, if wiredthe opposite sense) negative excursion.
3. With a very small signal (through air with the transducers at their closesposition), adjust the threshold operating voltage using “Set low” such ththe threshold operates above the noise level but detects the first (or secif wired in the opposite sense) real negative excursion.
4. Repeat this procedure until the threshold operates correctly over the enrange of signal levels.
Pulse Time The pulse time is a time constant included in the total time measured as a res
the threshold peak detection procedure used. This constant may not be appa
with peak detection or calibration procedures different from those described h
ODP subtracts this constant from raw measurements of time because (1) it all
more precise monitoring of system performance (pulse time and “hardware de
discussed in the following section) and (2) it renders measured time values th
independent of a particular peak detection procedure.The constant tpulse is
therefore subtracted from the raw measurement of time t0 so that
t’0 = t0 – tpulse. (5)
The important thing to note is that the pulse time value changes depending on
wiring of the system. If the first received peak voltage is positive (Figure 6—1)
pulse time will be one wavelength, or 2 µs, for the 500 kHz transducers. Howe
if the wiring is in the opposite direction, as was the case for the ODP system a
least for some time, the pulse time is 1.5 wavelength, or 3 µs, because the thre
detection is always on the negative signal.
Transducer Displacement and Traveltime Delay
These two calibrations are performed simultaneously in one procedure (Figur
2). They should be executed once per leg on a routine basis. They should als
performed when changes or replacement of equipment have occurred, transd
surfaces have experienced extraordinary wear, or if other problems are suspe
Variation in the thickness of the sediment-filled core liner (d’core) is measured
using an LVDT connected to the spring-loaded transducer housings. Displace
measured in volts must be calibrated to give millimeters. At least three of the
available standard acrylic cylinders are measured. A linear least-squares fit to
points defined by the voltage readings (x-axis) and the known standard thickne
6—5PP Handbook , Peter Blum , November, 1997
itry
s
is
l
y
tions
ss
in millimeters (y-axis) yields the linear coefficients md1 (mm/V) and md0 (mm).
Then, for any calibration standard or core measurement, respectively:
dcal = md0 + md1V0 (6)
d’core = md0 + md1V0 , (7)
where V0 is the voltage reading.
As previously mentioned, the total travel time (t0) measured between the
transducers includes three types of “delays”:
• delay related to the peak detection procedure (tpulse; see “Pulse Time” section),
• transit time through the core liner, if applicable (tliner; see “Liner Correction” section), and
• undifferentiable delay related to transducer faces and electronic circu(tdelay), which is determined with this procedure.
Although it is not necessary for the routine logging of sediment cores, ODP
differentiates between these types of delay because it allows for more rigorou
system monitoring and more flexibility in measurements. The constant tpulse is
subtracted from the raw measurement of time t0 so that
tcal = t0 – tpulse. (8)
The “hardware delay” of tdelay is then determined from another least-squares
regression. Here, the x-axis is defined by the dcal values of the standards
determined previously, and the y-axis is tcal. The linear coefficient, m1 (µs/mm), is
the inverse of the velocity of the standards (1/vstandard), and the intercept, m0 (µs),
is tdelay (Figure 6—2). Thus, the corrected transit time through a core is
t’core = t0 – tpulse – tdelay. (9)
If no core liner correction must be applied (i.e., if the material to be measured
directly in contact with the transducers), the velocity is calculated as
v’core = d’core / t’core. (10)
Liner correction In most cases (i.e., when logging whole cores in core liners), measured trave
distance and time must be corrected for twice the liner thickness. The liner
calibration is a measurement of thickness and transit time through core liner
material and is performed by ODP personnel. The liner correction is applied b
default (unless disabled by the user), using a constant liner thickness, dliner, and
sonic velocity for the liner material, vliner:
dcore = d’core – 2dliner (11)
tcore = t’core – 2dliner/vliner (12)
vcore = dcore / tcore. (13)
At present, we have no means of routinely measuring and correcting for varia
in liner wall thickness during logging. Vendor specifications for the wall thickne
are 5.64 to 4.70 mm, and we use 5.17 mm, or 2dliner = 1.03 cm.
6—6 PP Handbook , Peter Blum , November, 1997
ea-ion
own
d
Figure 6—2 Schematic of PWL calibration. A. Physical standard used. B. Msurement geometery. C. and D. Calibration principle. E. Application of calibratto core measurement.
PERFORMANCE
Precision Measurements on standard materials (e.g., water, acrylic calibration standards) are
repeatable within ±1 km/s. (Systematic evaluation required.)
Accuracy Accuracy can be evaluated by measuring pure water at varying and exactly kn
temperatures. Past experience shows that for a properly calibrated system an
good acoustic coupling, the disagreement with published v(T) values is less than ±2
km/s. (Systematic evaluation needed.)
MEASUREMENT
P-wave velocity is logged downcore automatically.
dcal (mm)
tcal (µs)
m0 = tdelay(µs)
m1 = vcal-1
(µs/mm)
Potential (V)
dcal (mm)
mV0 (mm)
mV1 (mm/V)
V = potentials from transducer LVDTdcal = known standard thicknesses
dcal = known standard thicknessestcal = t0 - tpulse, (tpulse = λ = 2 µs)
Acrylic cylinders
d1d2 d3
S1 S3S2
Core liner
Ultrasonic transducer pairand pneumatic caliper
dcore
d’core
A
B D
C
E
P-WAVE LOGGER (FULL-CORE)
• dliner and vliner are determined separately• option v’core: no liner correction (e.g., direct rock measurements)
v’core = d’core
= V0 × md1 + md0
t’core t0 - tpulse - tdelayvcore =
dcore =
d’core - 2dlinertcore t’core - 2dliner/vliner
6—7PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database Model
Notes: PWL Ctrl 1 are control measurements run the same way as a core section. PWL Ctrl 3 are control measurements from a standard mounted on the core boat (pure water).
Standard Queries
Table 6—1 PWL Database Model.
PWL section PWL control 1 PWL control 3 PWL calibration
pwl_id [PK1] pwl_ctrl_1_id [PK1] pwl_ctrl_3_id [PK1] pwl_calibration_id [PK1]section_id run_number run_number calibration_date_time
run_number run_date_time run_date_time run_numberrun_date_time core_status req_daqs_per_sample system_idcore_status liner_status pwl_calibration_id req_daqs_per_sample
liner_status liner_correction standard_id acoustic_signal_thresholdliner_correction requested_daq_interval acoustic_signal_threshold pwl_frequency
liner_standard_id req_daqs_per_sample core temperature pulse_time_correctionrequested_daq_interval pwl_calibration_id core_status separation_m0req_daqs_per_sample standard_id liner_status separation_m1
pwl_calibration_id acoustic_signal_threshold meas_separation_mean separation_mseacoustic_signal_threshold core temperature meas_separation_sd delay_m0
core temperature standard_liner_id meas_time_mean delay_1_m1mst_pwl_ctrl_3_id meas_time_sd delay_mse
acoustic_signal_mean commentsattempted_daqs
PWL section data PWL control 1 data valid_daqs PWL calibration data
pwl_id [PK1] [FK] pwl_ctrl_1_id [PK1] [FK] liner_thickness pwl_calibration_id [PK1] [FK]mst_top_interval [PK2] mst_top_interval [PK2] standard_liner_id standard_id [PK2] [FK]
mst_bottom_interval mst_bottom_interval mst_top_intervalmeas_separation_mean meas_separation_mean mst_bottom_intervalmeas_separation_sd meas_separation_sd standard_length
meas_time_mean meas_time_mean meas_separation_meanmeas_time_sd meas_time_sd meas_separation_sd
acoustic_signal_mean acoustic_signal_mean meas_time_meanattempted_daqs attempted_daqs meas_time_sdvalid_daqs valid_daqs acoustic_signal_mean
liner_thickness liner_thickness attempted_daqsliner_standard_id valid_daqs
Table 6—2 PWL report.
Short description Description DatabaseA: resultsSample ID ODP standard sample designation Link through [PWL Section] section_idDepth User-selected depth type Link through [PWL Section] section_idVelocity Calculated P-wave velocity = ([PWL Section Data] meas_separation_mean
- 2* [PWL Section Data] liner_thickness)/ ([PWL Section Data] meas_time_mean- {2* [PWL Section Data] liner_thickness/ [PP Std Data] liner_velocity}- [PWL Calibration] delay_m0)
B (optional): Parameteres and measurementsRun Run number [PWL Section] run_numberDate/Time Run date/time [PWL Section] run_date_timeCore Status HALF or FULL [PWL Section] core_statusLiner Status NONE, HALF or FULL [PWL Section] liner_status
6—8 PP Handbook , Peter Blum , November, 1997
Liner correction Liner correction (Yes/No) [PWL Section] liner_correctionReq. Interval User-defined sampling interval (cm) [PWL Section] requested_daq_intervalReq. Sample Requested DAQS per sample [PWL Section] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Section] acoustic_signal_thresholdCore Temp Core temperature [PWL Section] core_temperatureSep. Mean Mean of transducer separation [PWL Section Data] meas_separation_meanSep. S.D. Standard deviation of transducer separation [PWL Section Data] meas_separation_sdTime Mean Mean of transit tme [PWL Section Data] meas_time_meanTime std. dev. Standard deviation of transit time [PWL Section Data] meas_time_sdSignal Mean of acoustic signal [PWL Section Data] acoustic_signal_mean Attempted DAQS Attempted numnber of data acquisitions [PWL Section Data] attempted_daqsValid DAQS Valid number ofdata acquisitions [PWL Section Data] valid_daqsLiner Thickness Liner thickness (entered manually) [PWL Section Data] liner_thicknessStandard name Standard name [Phys. Properties Std.] standard_nameStandard Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Cal. Date/Time Calibration date/time [PWL Calibration] calibration_date_timeCal. Separ. m0 Intercept of transducer separation calibration [PWL Calibration] separation_m0Cal. Separ. m1 Slope of transducer separation calibration [PWL Calibration] separation_m1Cal. Separ. mse Mean squared errorof transducer separation cal. [PWL Calibration] separation_mseCal. Time m0 Intercept of transit time calibration [PWL Calibration] delay_m0Cal. Time m1 Slopeof transit time calibration [PWL Calibration] delay_m1Cal. Time mse Mean squared error of transit time calibration [PWL Calibration] delay_mse
Table 6—2 PWL report.
Table 6—3 PWL control 1 measurements (to be implemented).
Short description Description DatabaseVelocity Calculated P-wave velocity = ([PWL Ctrl 1 Data] meas_separation_mean
- 2* [PWL Ctrl 1 Data] liner_thickness)/ ([PWL Ctrl 1 Data] meas_time_mean- {2* [PWL Ctrl 1 Data] liner_thickness/ [PP Std Data] liner_velocity}- [PWL Calibration] delay_m0)
Run Run number [PWL Ctrl 1] run_numberDate/Time Run date/time [PWL Ctrl 1] run_date_timeCore Status HALF or FULL [PWL Ctrl 1] core_statusLiner Status NONE, HALF or FULL [PWL Ctrl 1] liner_status Liner Corr. Liner correction (Yes/No) [PWL Ctrl 1] liner_correctionReq. Interval User-defined sampling interval (cm) [PWL Ctrl 1] requested_daq_intervalReq. Sample User-defined DAQs per sample [PWL Ctrl 1] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Ctrl 1] acoustic_signal_thresholdCore Temp Core temperature [PWL Ctrl 1] core_temperatureInterval Interval top [PWL Ctrl 1 Data] mst_top_intervalSep. Mean Separation mean [PWL Ctrl 1 Data] meas_separation_meanSep. S.D. Separation standard deviation [PWL Ctrl 1 Data] meas_separation_sdTime Mean Time mean [PWL Ctrl 1 Data] meas_time_meanTime S.D. Time standard deviation [PWL Ctrl 1 Data] meas_time_sdSignal Acoustic signal mean [PWL Ctrl 1 Data] acoustin_signal_meanDaqs Attempt Attempted data acquisitions [PWL Ctrl 1 Data] attempted_daqsDaqs Valid Valid data acquisitions [PWL Ctrl 1 Data] valid_daqsLiner Thick Liner thickness (entered manually) [PWL Ctrl 1 Data] liner_thicknessStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Cal. Date/Time Cal. date/time [PWL Calibration] calibration_date_timeCal. Separ. m0 Cal. separation intercept m0 [PWL Calibration] separation_m0
Cal. Separ. m1 Cal. separation slope m1 [PWL Calibration] separation_m1
6—9PP Handbook , Peter Blum , November, 1997
Cal. Separ. mse Cal. separation mean squared error [PWL Calibration] separation_mseCal. Time m0 Cal. time intercept m0 [PWL Calibration] delay_m0
Cal. Time m1 Cal. time slope m1 [PWL Calibration] delay_m1
Cal. Time mse Cal. time mean squared error [PWL Calibration] delay_mse
Table 6—3 PWL control 1 measurements (to be implemented).
Table 6—4 PWL control 3 measurements (to be implemented).
Short description Description DatabaseVelocity Calculated P-wave velocity = ([PWL Ctrl 3] meas_separation_mean
- 2* [PWL Ctrl 3] liner_thickness)/ ([PWL Ctrl 3] meas_time_mean- {2* [PWL Ctrl 3] liner_thickness/ [PP Std Data] liner_velocity}- [PWL Calibration] delay_m0)
Run Run number [PWL Ctrl 3] run_numberDate/Time Run date/time [PWL Ctrl 3] run_date_timeCore Status HALF or FULL [PWL Ctrl 3] core_statusLiner Status NONE, HALF or FULL [PWL Ctrl 3] liner_status Req. Interval User-defined sampling interval (cm) [PWL Ctrl 3] requested_daq_intervalReq. Sample User-defined DAQs per sample [PWL Ctrl 3] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Ctrl 3] acoustic_signal_thresholdCore Temp Core temperature [PWL Ctrl 3] core_temperatureSep. Mean Separation mean [PWL Ctrl 3] meas_separation_meanSep. S.D. Separation standard deviation [PWL Ctrl 3] meas_separation_sdTime Mean Time mean [PWL Ctrl 3] meas_time_meanTime S.D. Time standard deviation [PWL Ctrl 3] meas_time_sdSignal Acoustic signal mean [PWL Ctrl 3] acoustic_signal_meanDaqs Attempt Attempted data acquisitions [PWL Ctrl 3] attempted_daqsDaqs Valid Valid data acquisitions [PWL Ctrl 3] valid_daqsLiner Thick Liner thickness (entered manually) [PWL Ctrl 3] liner_thicknessStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_valueCal. Date/Time Cal. date/time [PWL Calibration] calibration_date_timeCal. Separ. m0 Cal. separation intercept m0 [PWL Calibration] separation_m0
Cal. Separ. m1 Cal. separation slope m1 [PWL Calibration] separation_m1
Cal. Separ. mse Cal. separation mean squared error [PWL Calibration] separation_mseCal. Time m0 Cal. time intercept m0 [PWL Calibration] delay_m0Cal. Time m1 Cal. time slope m1 [PWL Calibration] delay_m1
Cal. Time mse Cal. time mean squared error [PWL Calibration] delay_mse
Table 6—5 PWL calibration data (to be implemented).
Short description Description DatabaseDate/Time Cal. date/time [PWL Calibration] calibration_date_timeRun Cal. run number [PWL Calibration] run_numberReq. Sample User-defined DAQs per sample [PWL Calibration] requested_daqs_per_sampleSignal Acoustic signal threshold [PWL Calibration] acoustic_signal_thresholdFrequency PWL frequency [PWL Calibration] pwl_frequencyPulse Time Pulse time correction [PWL Calibration] pule_time_correctionSepar. m0 Cal. separation intercept m0 [PWL Calibration] separation_m0
Separ. m1 Cal. separation slope m1 [PWL Calibration] separation_m1
Separ. mse Cal. separation mean squared error [PWL Calibration] separation_mseTime m0 Cal. time intercept m0 [PWL Calibration] delay_m0Time m1 Cal. time slope m1 [PWL Calibration] delay_m1
6—10 PP Handbook , Peter Blum , November, 1997
6.3. PWS1 and PWS2 Insertion Probe Systems
EQUIPMENT
The current equipment has replaced the Digital Sonic Velocimeter (DSV)
developed at Dalhousie University and the Bedford Institute of Oceanography,
Nova Scotia, which was first used on ODP Leg 138 in 1991 (PWS1 and PWS2).
The principles remain the same, but hardware and computer control have been
improved significantly, and calibration and measurement procedures are simplified
at this upgraded station.
A vise-like frame holds the two transducers pairs. Their use is limited
approximately to the depth range of APC cores (i.e., a maximum depth of 50 to
300 mbsf), depending on the lithology.
A Tectronix signal generator, differential amplifier, and oscilloscope are used to
transmit and receive signals from all three transducer pairs and to digitize analog
waveform data. The instrument can record two voltage inputs with a minimum
sampling time of 5 ns and a digitizing signal to noise ratio of 50 dB.
An external digital thermometer is used to record core temperature. The values are
recorded in the database but are not used for shipboard reporting. Correction
algorithms must be researched, selected, and applied by the user.
CALIBRATION
Delay The distance d between the transducers is measured with calipers once every few
days (or even once per leg) and then assumed to be constant. The distance between
the probe surfaces does not exactly correspond to the distance between the
transducers. In addition, there is some electrical delay. The total “delay” tdelay is
Time mse Cal. time mean squared error [PWL Calibration] delay_mseComments Cal. comments [PWL Calibration] commentsTime mse Cal. time mean squared error [PWL Calibration] delay_mseInterval Interval top [PWL Calibration Data] mst_top_intervalStd. Length Length of standard [PWL Calibration Data] standard_lengthSepar. Mean Mean of transducer separation [PWL Calibration Data] separation_meanSepar. S.D. Standard deviation of transducer separation [PWL Calibration Data] separation_sdTime Mean Mean of transit time [PWL Calibration Data] time_meanTime S.D. Standard deviation of transit time [PWL Calibration Data] time_sdSignal Acoustic signal mean [PWL Calibration Data] acoustic_signal_meanDaqs Attempt Attempted data acquisitions [PWL Calibration Data] attempted_daqsDaqs Valid Valid data acquisitions [PWL Calibration Data] valid_daqsStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Table 6—5 PWL calibration data (to be implemented).
6—11PP Handbook , Peter Blum , November, 1997
the
determined in this calibration by inserting the probes into a container filled with
distilled water of known temperature and therefore of known sound velocity vwater.
Because d is known, transit time in water, twater, can be computed as
twater = vwater × d. (14)
The measured total transit time, t, is:
t = twater + tdelay. (15)
Combining these equations, the delay can be expressed as:
tdelay = t – vwater × d. (16)
This calibration should be performed when the operator suspects a change in
distance between the probes because of heavy use or from other reasons.
PERFORMANCE
No performance evaluation data exist at present.
MEASUREMENT
An on-line guide is available at the neasurement station.
DATA SPECIFICATIONS
Data Mode
l
Notes: Control 1 measurements are run like core measurements, using a standard of known velocity.
Table 6—6 PWS1 and PWS2 data model.
PWS1/2 section PWS1/2 control 1 PWS1/2 calibration
pws_id [PK1] pws_ctrl_1_id [PK1] pws_calibration_id [PK1]section_id run_number calibration_date_timerun_num run_date_time run_num
run_date_time system_id system_idsystem_id standard_id water_temperaturepws_calibration_id pws_calibration_id standard_velocity
direction direction measured_timecore_temperature core_temperature delayraw_data_collected raw_data_collected freq
transducer_separation commentsmeasured_time
PWS1/2 section data
pws_id [PK1 [FK]pp_top_interval [PK2]measurement_no [PK3]
pp_bottom_intervaltransducer_separationmeasured_time
PWS1/2 raw data PWS1/2 control 1 raw datapws_id [PK1 [FK] pws_ctrl_1_id [PK1 [FK]
pp_top_interval [PK2] [FK] voltage [PK2]measurement_no [PK3] [FK] timevoltage [PK4]
time
6—12 PP Handbook , Peter Blum , November, 1997
Standard queries
Table 6—7 PWS1 or PWS2 report
Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [PWS1/2 Section] section_idDepth User-selected depth type Link through [PWS1/2 Section] section_idDirection Direction (PWS1 = z; PWS2 = y) [PWS1/2 Section] directionVelocity Calculated P-wave velocity = [PWS1/2 Section Data] transd_separation
/ ([PWS1/2 Section Data] measured_time- [PWS1/2 Calibration] delay)
B (optional): Measurement parameters and raw dataRun Run number [PWS1/2 Section] run_numberDate/Time Run date/time [PWS1/2 Section] run_date_timeCore Temperature Core temperature [PWS1/2 Section] core_temperatureRaw Data Raw data collected flag (yes/no) [PWS1/2 Section] raw_data_collectedMeas. No Measurement number [PWS1/2 Section Data] measurement_noSeparation Transducer separation [PWS1/2 Section Data] transducer_separationTime Measured time [PWS1/2 Section Data] measured_timeCal. Date/Time Cal. date/time [PWS1/2 Calibration] calibration_date_timeCal. Delay Cal. delay [PWS1/2 Calibration] delay
Table 6—8 PWS1 or PWS2 control 1 measurements (to be implemented).
Short description Description DatabaseVelocity Calculated P-wave velocity = [PWS1/2 Ctrl 1] transd_separation
/ ([PWS1/2 Ctrl 1] measured_time- [PWS1/2 Calibration] delay)
Run Run number [PWS1/2 Ctrl 1] run_numberDate/Time Run date/time [PWS1/2 Ctrl 1] run_date_timeDirection Direction (PWS1 = z; PWS2 = y) [PWS1/2 Ctrl 1] directionCore Temp Core temperature [PWS1/2 Ctrl 1] core_temperatureRaw Data Raw data collected flag (yes/no) [PWS1/2 Ctrl 1] raw_data_collectedSeparation Transducer separation [PWS1/2 Ctrl 1] transducer_separationTime Measured time [PWS1/2 Ctrl 1] measured_timeCal. Date/Time Cal. date/time [PWS1/2 Calibration] calibration_date_timeCal. Delay Cal. delay [PWS1/2 Calibration] delayStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Table 6—9 PWS1 or PWS2 calibration data (to be implemented).
Short description Description DatabaseDate/Time Cal. date/time [PWS1/2 Calibration] calibration_date_timeRun Cal. run number [PWS1/2 Calibration] run_numberWater Temperature Water temperature [PWS1/2 Calibration] water_temperatureVelocity Velocity of water at given temperature. [PWS1/2 Calibration] standard_velocityTime Measured time [PWS1/2 Calibration Data] measured_timeDelay Delay time derived from calibration. [PWS1/2 Calibration Data] delayFrequency Transducer frequency [PWS1/2 Calibration Data] freqComments Comments [PWS1/2 Calibration Data] commentsStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
6—13PP Handbook , Peter Blum , November, 1997
6.4. PWS3 Contact Probe System
EQUIPMENT
The current equipment has replaced the Hamilton Frame used on the ship since the
beginning of ODP. The principles remain the same but the hardware and computer
control have been improved significantly, and calibration and measurement
procedures are simplified at this upgraded station. The PWS3 is equipped with a
digital scale unit that allows rapid, precise determination of sample thickness and
enters the value into the database.
A pressure gauge is built into the monitor, and pressure is applied to the sample
when lowering the transducer onto the specimen or split core in the liner. In the
split core (logging) mode, the core section liner rests on the bottom transducer and
the upper transducer is lowered manually (procedure to be automated soon) onto
the core surface. In the specimen mode, the sample is placed directly between the
two transducers in the desired orientation.
CALIBRATION
Delay This calibration procedure is equivalent to the one employed for the P-wave logger
on the MST. Delay time tdelay is obtained by measuring a standard material of
different thicknesses d1, d2, . . . dn, and total transit times t1, t2, . . . tn. The
coefficient m0 (intercept) obtained from a linear least-squares fit represents the
delay tdelay. The inverse of the coefficient m1 (slope) of that regression is the
velocity of the standard material.
PERFORMANCE
No performance evaluation data exist at present.
Table 6—10 PWS1 or PWS2 wave form data (to be implemented).
Short description Description DatabaseSample ID ODP standard sample designationMeasurement Measurement number [PWS1/2 Raw Data] measurement_noVoltage Voltage [PWS1/2 Raw Data] voltageTime Time [PWS1/2 Raw Data] time
Table 6—11 PWS1 or PWS2 wave form control 1 data (to be implemented).
Short description Description DatabaseDate/Time Run date/time [PWS1/2 Ctrl 1] run_date_timeVoltage Voltage [PWS1/2 Ctrl 1 Raw Data] voltageTime Time [PWS1/2 Ctrl 1 Raw Data] time
6—14 PP Handbook , Peter Blum , November, 1997
MEASUREMENT
An on-line guide is available at the neasurement station.
DATA SPECIFICATIONS
Database Model
Notes: Control 1 measurements are run like core measurements, using a standard of known velocity.
Standard Queries
Table 6—12 PWS3 database model.
PWS3 section PWS3 control 1 PWS3 calibrationpws_id [PK1] pws_ctrl_1_id [PK1] pws_calibration_id [PK1]
section_id run_num calibration_date_timerun_num run_date_time run_numberrun_date_time system_id system_id
system_id standard_id delay_1_over_m1pws_calibration_id pws_calibration_id delay_m0direction direction delay_mse
core_temperature core_temperature freqliner_correction standard_liner_id commentsraw_data_collected raw_data_collected
standard_liner_id transducer_separationmeasured_time
PWS3 section data contact_pressure PWS3 calibration data
pws_id [PK1] [FK] liner_thickness pws_calibration_id [PK1] [FK]pp_top_interval [PK2] standard_id [PK2] [FK]measurement_no [PK3] transducer_separation
pp_bottom_interval measured_timetransducer_separation contact_pressuremeasured_time
contact_pressureliner_thickness
PWS3 raw data PWS3 control 1 raw datapws_id [PK1] [FK] pws_ctrl_1_id [PK1] [FK]pp_top_interval [PK2] [FK] voltage [PK2]
measurement_no [PK3] [FK] timevoltage [PK4]time
Table 6—13 PWS3 report.
Short description Description DatabaseA: ResultsSample ID ODP standard sample designation Link through [PWS3 Section] section_idDepth User-selected depth type Link through [PWS3 Section] section_idVelocity IF (liner_correction = TRUE) = ([PWS3 Section Data] transducer_separation
- [PWS3 Section Data] liner_thickness)/ ([PWS3 Section Data] measured_time- [PWS3 Section Data] liner_thickness/ [PP Std Data] liner_velocity}- [PWS3 Calibration] delay_m0)
Velocity IF (liner_correction =FALSE) = ([PWS3 Section Data] transducer_separation/ ([PWS3 Section Data] measured_time
6—15PP Handbook , Peter Blum , November, 1997
- [PWS3 Calibration] delay_m0)B (optional): Measurement parameters and raw dataRun Run number [PWS3 Section] run_numberDate/Time Run date/time [PWS3 Section] run_date_timeDirection Direction (PWS1 = z; PWS2 = y) [PWS3 Section] directionCore Temp Core temperature [PWS3 Section] core_temperatureLiner Corr. Liner correction required (yes/no) [PWS3 Section] liner_correctionRaw Data Raw data collected flag (yes/no) [PWS3 Section] raw_data_collectedStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Meas. No Measurement number [PWS3 Section Data] measurement_noSeparation Transducer separation [PWS3 Section Data] transducer_separationTime Measured time [PWS3 Section Data] measured_timePressure Contact pressure applied [PWS3 Section Data] contact pressureLiner Thick Liner thickness [PWS3 Section Data] liner thicknessCal. Date/Time Cal. date/time [PWS3 Calibration] calibration_date_timeCal. m0 Cal. time intercept m0 [PWS3 Calibration] delay_m0
Cal. 1/m1 Cal. time inverse of slope 1/m1 [PWS3 Calibration] delay_1_over_m1
Cal. Time mse Cal. time mean squared error [PWS3 Calibration] delay_mse
Table 6—13 PWS3 report.
Table 6—14 PWS3 control 1 measurements (to be implemented).
Short description Description DatabaseVelocity IF (liner_correction = TRUE) = ([PWS3 Ctrl 1] transducer_separation
- [PWS3 Ctrl 1] liner_thickness)/ ([PWS3 Ctrl 1] measured_time- [PWS3 Ctrl 1] liner_thickness/ [PP Std Data] liner_velocity}- [PWS3 Calibration] delay_m0)
Velocity IF (liner_correction =FALSE) = ([PWS3 Ctrl 1] transducer_separation/ ([PWS3 Ctrl 1] measured_time- [PWS3 Calibration] delay_m0)
Run Run number [PWS3 Ctrl 1] run_numberDate/Time Run date/time [PWS3 Ctrl 1] run_date_timeDirection Direction (PWS1 = z; PWS2 = y) [PWS3 Ctrl 1] directionCore Temperature Core temperature [PWS3 Ctrl 1] core_temperatureLiner Correction Liner correction required (yes/no) [PWS3 Ctrl 1] liner_correctionRaw Data Raw data collected flag (yes/no) [PWS3 Ctrl 1] raw_data_collectedSeparation Transducer separation [PWS3 Ctrl 1] transducer_separationTime Measured time [PWS3 Ctrl 1] measured_timePressure Contact pressure applied [PWS3 Section Data] contact pressureLiner Thick Liner thickness [PWS3 Section Data] liner thicknessCal. Date/Time Cal. date/time [PWS3 Calibration] calibration_date_timeCal. m0 Cal. time intercept m0 [PWS3 Calibration] delay_m0
Cal. 1/m1 Cal. time inverse of slope 1/m1 [PWS3 Calibration] delay_1_over_m1
Cal. Time mse Cal. time mean squared error [PWS3 Calibration] delay_mseStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Table 6—15 PWS3 calibration data (to be implemented).
Short description Description DatabaseDate/Time Cal. date/time [PWS3 Calibration] calibration_date_timeRun Cal. run number [PWS3 Calibration] run_numberCal. m0 Cal. time intercept m0 [PWS3 Calibration] delay_m0
6—16 PP Handbook , Peter Blum , November, 1997
Cal. 1/m1 Cal. time inverse of slope 1/m1 [PWS3 Calibration] delay_1_over_m1
Cal. Time mse Cal. time mean squared error [PWS3 Calibration] delay_mseFrequency Transducer frequency [PWS3 Calibration] freqComments Comments [PWS3 Calibration] commentsSeparation Transducer separation [PWS3 Calibration Data] transducer_separationTime Measured time [PWS3 Calibration Data] measured_timePressure Contact pressure [PWS3 Calibration Data] contact_pressureStandard Standard name [Phys. Properties Std.] standard_nameStd. Set Standard set name [Phys. Properties Std.] standard_set_nameStd. Expected Expected value (range) (g/cm3) [Phys. Prop. Std. Data] property_value
Table 6—15 PWS3 calibration data (to be implemented).
Table 6—16 PWS3 wave form data (to be implemented).
Column Head Description DatabaseSample ID ODP standard sample designationMeasurement Measurement number [PWS3 Raw Data] measurement_noVoltage Voltage [PWS3 Raw Data] voltageTime Time [PWS3 Raw Data] time
Table 6—17 PWS3 wave form control 1 data (to be implemented).
Short description Description DatabaseDate/Time Run date/time [PWS3 Ctrl 1] run_date_timeVoltage Voltage [PWS3 Ctrl 1 Raw Data] voltageTime Time [PWS3 Ctrl 1 Raw Data] time
6—17PP Handbook , Peter Blum , November, 1997
tic
e
he
ro
,
7. REFLECTANCE SPECTROPHOTOMETRY AND COLORIMETRY
7.1. Principles
PHYSICAL BA CKG ROUND
Color is the human eye’s perception of reflected radiation in the visible region of
the electromagnetic spectrum (400–700 nm). It originates from electromagne
energy changes in electron orbitals, caused by the absorption of photons, in th
transition elements contained in the crystal structure of minerals (e.g., Burns,
1970).
One of the most objective ways to measure color is to use diffuse-reflected
spectrophotometry. Light reflected from the material is collected in an integration
sphere, normalized to the source light of the reflectance, and calibrated with t
measurement of a pure white standard (100% reflection) and a black box (ze
reflection) over the entire wavelength spectrum of visible light. For material
studies, near-ultraviolet (250–400 nm) and near-infrared (700–850 nm) have been
shown to be useful.
Reflectance spectra are related to color using established international conventions.
According to the Commission Internationale d'Eclairage (CIE) (1986) method
tristimulus values are derived from the color reflectance spectra as follows:
For 2° standard observer (CIE, 1931) and 400 ≤ λ ≤ 700 (nm): (1)
X = K ∑ S(λ) x(λ) R(λ), (2)
Y = K ∑ S(λ) y(λ) R(λ), (3)
Z = K ∑ S(λ) z(λ) R(λ), (4)
K = 100/ ∑ S(λ) y(λ). (5)
For 10° standard observer (CIE 1964) and 400 ≤ λ ≤ 700 (nm): (6)
X10 = K ∑ S(λ) x10(λ) R(λ), (7)
Y10 = K ∑ S(λ) y10(λ) R(λ), (8)
Z10 = K ∑ S(λ) z10(λ) R(λ), (9)
K = 100/ ∑ S(λ) y10(λ), (10)
where λ is the wavelength at a 10-nm pitch; S(λ) is the relative spectral power
distribution of the illuminant; x(λ), x10(λ), y(λ), y10(λ), z(λ), and z10(λ) are color-
matching functions; and R(λ) is the spectral reflectance of the specimen.
7—1PP Handbook , Peter Blum , November, 1997
for
es the
zed
Several color spaces have been defined based on the tristimulus values X, Y, Z,
such as the Yxy, L*a*b* and its derivative L*C*H°, Lab, and L*u*v* systems. The
L*a*b* system is presented here in more detail, and its use is recommended
sediment and rock color analyses. It is far superior to and therefore supersed
Munsell color system traditionally used in earth science.
The L*a*b* Color System
The L*a*b* system is also referred to as the CIELAB system. It can be visuali
as a cylindrical coordinate system in which the axis of the cylinder is the lightness
variable L* , ranging from 0% to 100%, and the radii are the chromaticity variables
a* and b*. Variable a* is the green (negative) to red (positive) axis, and variable b*
is the blue (negative) to yellow (positive) axis.
The variables are defined as follows (CIE, 1986; related references: ASTM, 1985a;
ASTM, 1985b; ISO, 1984; DIN, 1980):
• If (X/Xn), (Y/Yn), (Z/Zn) > 0.008856:L* + 116(Y/Yn)1/3 - 16, (11)
a* = 500[(X/Xn)1/3 - (Y/Yn)1/3], (12)
b* = 200[(Y/Yn)1/3 - (Z/Zn)1/3]. (13)
• If (X/Xn), (Y/Yn), (Z/Zn) < 0.008856:L* + 903.29(Y/Yn), (14)
a* = 500{7.787[(X/Xn) + 16/116] - 7.787[(Y/Yn) + 16/116]}, (15)
b* = 200{7.787[(Y/Yn) + 16/116] - 7.787[(Z/Zn) + 16/116]}, (16)
where X, Y, and Z are tristimulus values for the 2° or 10° observer of the specimen,
and Xn, Yn, and Zn are tristimulus values for the 2° or 10° observer of a perfect
reflecting diffuser.
Derived Parameters Various standard parameters can be calculated from the L*a*b* system variables.
If L* , a*, b* are the specimen data, and L* t, a*t, b*t are the target color data,
differences are defined as:
∆L* = L* - L* t' (17)
∆a* = a* - a*t' (18)
∆b* = b* - b*t , (19)
and the color difference between two points is
∆E*ab = [(∆L* )2 + (∆a*)2 + (∆b*)2]1/2. (20)
In the L*C*H° system, the metric chroma parameter, C*, and the metric hue-angle,
H°, are defined as:
C* = [(a*)2 + (b*)2]1/2 (21)
H° = tan–1(b*/a*) (degrees), 0° ≤ H° ≤ 360°. (22)
Differences between specimen and target color are
∆L* = L* - L* t' (23)
∆C* = C* - C* t, = [(∆a*)2 + (∆b*)2]1/2 - [(∆at*)2 + (∆bt*)
2]1/2, (24)
and the metric hue difference, ∆H*, between two points is defined as
∆H* = [(∆E*ab)2 - (∆L* )2 + (∆C*)2]1/2 = [(∆a*)2 - (∆b*)2 + (∆C*)2]1/2 (25)
7—2 PP Handbook , Peter Blum , November, 1997
ell
lues
s:
nor
tting
uite
s.
lide.
nd
d by
s to
y, it
m
re is
p the
ments
cal
ter
rs
ed
Munsell Colors There is no international standard for converting tristimulus values to Munsell
HVC (hue, value, and chroma) notation. Tables have been established to relate
Munsell colors to Yxy data, and the L*C*H° parameters can be related to Muns
colors using such tables. Interpolation is used if necessary to approximate va
not available in the tables. The use of Munsell colors has many disadvantage
different conditions of illumination and viewing angle and variability in human
eye’s response and sensitivity. It is a procedure that is neither highly objective
can the data be analyzed quantitatively. Munsell color classification should
therefore not be used.
ENVIRONMENTAL EFFECTS
Measuring Color of Split-Core Surfaces
Measuring split-core surfaces poses some potential problems affecting the
measurement:
• moisture, or uncontrolled drying of the material,
• surface roughness,
• particle size,
• oxidation, and
• use of protective plastic wrap.
Cores are split with a wire if they are soft enough, and with a saw once wire cu
is no longer effective. The problem is that wire cuts of very soft sediment and q
stiff sediment from farther downhole may create different surface roughnesse
This can affect color reflectance significantly. The problem is mostly solved by
“cleaning” the core surface with a sharp edge such as a knife blade or glass s
The process is tedious and time-consuming, however.
Moisture content affects color reflectance significantly. Automated shipboard
measurements at very small sampling intervals require that the material is
measured at whatever moisture content is present. Both uncontrolled drying a
oxidation that begin as soon as the core is split “lighten” material characterize
organic matter or iron compounds. The oxidation of iron compounds also tend
increase reflectance, particularly at the red end of the spectrum. Unfortunatel
cannot be assumed that the changes caused by drying or oxidation are unifor
over the entire spectrum of visible light. Absolute spectral characteristics for
sediment and rock colors must therefore be established with dry powders. The
no practical solution to the discrepancy between dry and wet material
measurement, and it must be accepted as the inherent analytical error. To kee
variation as systematic as possible, it is a good practice to take color measure
at constant periods after the core has been split, such as about 1 hr.
Nagao and Nakashima (1991) examined the difference between wet and dry
measurements of marine cores in L*a*b* color space. They found that for typi
pelagic sediment of the uppermost meter below seafloor, L* is up to 20% higher in
dried specimens and a* and b* are higher by approximately 1. Fortunately,
however, color parameter profiles, such as L*a*b*, do not change their charac
as a function of moisture content (Nagao and Nakashima, 1992). These autho
also examined and discussed the effects of grain size, addition of water to dri
samples, and oxidation. They concluded that L* values are controlled mainly by
7—3PP Handbook , Peter Blum , November, 1997
als
ed
rain
ts
when
ere
art,
water content with a small grain-size and homogenization effect; a* and b* values
are controlled by water content, oxidation of greenish materials, and grain size.
Pore-water composition may have an effect, as shown by the difference in
measurements of samples remolded with pure water from original values.
Balsam et al. (1997) performed factor analyses on spectral data from Leg 155
cores obtained with the shipboard Minolta CM-2002 on wet split cores and with a
Perkin-Elmer Lambda 6 on corresponding dried and powdered samples. Shipboard
data yield a four-factor solution to explain 99% of the cumulative variance,
whereas shore-based data produced at least seven interpretable factors to explain
99% of the cumulative variance. The four factors identified in the ship data are
also present in the shore data. This and other statistics indicate that measurements
from dried sediments are significantly more sensitive to subtle variations in the
data set than measurements on wet cores, which appear to contain less
information. However, differences in instrumentation and the fact that wet cores
were measured through a film of Saran Wrap may also have affected the sensitivity
of these measurements.
USE OF COLOR DATA
The two most common uses of color reflectance data are (1) color parameters such
as L*a*b* provide detailed time series of relative changes in the composition of
the bulk material and are frequently used to correlate sections from core to core or
hole to hole and to analyze the cyclicity of lithologic changes; and (2) spectral data
can be used to estimate the abundance of certain compounds. The first type of
investigation, referred to as colorimetry, is simple and straightforward. Spectral
analysis of visual light spectra (VIS) provides semiquantitative estimates of
hematite and goethite with a sensitivity that is at least 1 order of magnitude better
than from XRD analysis (Deaton and Balsam, 1991). Carbonate, opal, organic
matter, chlorite, and some combinations of clay minerals can also be detected,
although near-ultraviolet (NUV) and near-infrared (NIR) data (which cannot be
obtained with the Minolta CM-2002) should or must be included for at least some
of these analysis (Balsam and Deaton, 1991, 1996; Nakashima et al., 1992; Balsam
and Wolhart, 1993; Balsam and Otto-Bliesner, 1995).
The spectra of marine sediments are typically smooth and show small peaks and
valleys. A common statistical method to enhance relative changes is to use the first
derivative of the measurement intervals. This “boundary hunting” method reve
the maximum rate of change in the original spectrum or the shoulders of the
original absorption peaks, which occur at characteristic wavelengths. The add
advantage of using first derivatives is that problems inherent in core surface
measurement (moisture, oxidation, use of plastic wrap, surface texture and g
size, etc.) or the difference between measurements using different instrumen
(Balsam et al., 1997) are minimized. Yet, several effects must be considered
using first derivatives for quantitative estimation: matrix composition has a sev
effect on peak height and the exact wavelength of a peak depends on the
concentration of a component (Deaton and Balsam, 1991; Balsam and Wolh
1993) and the grain size of a component may influence the reflectivity and
absolute band intensity (Gaffey, 1986).
7—4 PP Handbook , Peter Blum , November, 1997
t for
mm.
s in a
alog
acy
y, the
the
y
n 8°
ign
d
does
ta
al
me
ed
f the
t into
en
Results from many ODP legs have shown that the correlation between L* and
carbonate content is usually the best and most obvious one and also is similar at
many sites. Parameters a* and b* do not seem to yield much characteristic
information or cyclic variations. They are, however, sensitive to clay mineralogy,
nannofossil content, etc. Ratios such as a*/b*, or parameters of the L*C*H°
system, may distinguish these variations better than a* and b*.
7.2. Minolta CM-2002 System
EQUIPMENT
The Minolta Photospectrometer CM-2002 is a compact, hand-held instrumen
measuring the spectral reflectance of surfaces with a diameter of more than 8
The instrument combines measurement, data processing, and display function
single unit. Ultracompact spectral sensors developed by Minolta, hybrid IC an
circuitry, and a 32-bit, 16-MHz microcomputer provide high-speed, high-accur
measurements of spectral reflectance from 400 to 700 nm. To ensure accurac
CM-2002 uses a double-beam feedback system, monitoring the illumination on
specimen at the time of measurement and automatically compensating for an
changes in the intensity or spectral distribution of the light.
Objects are illuminated diffusely with a pulsed xenon arc light and viewed at a
angle to the normal to the object’s surface (standard observer, Commission
Internationale d'Eclairage, CIE). The width of the viewing beam is 7.4°. This
geometry meets the specification for diffuse illumination and 0° viewing angle
(CIE, 1986) as well as the specification for diffuse illumination and 8° viewing
angle (ISO, 1984; DIN, 1980). In addition, the instrument’s geometry and des
allow for the specular component to be included (SCI setting) or to be exclude
(SCE setting). The SCE setting is the recommended mode of operation for
sediments in which the light reflected at a certain angle (angle of specular
reflection) is trapped and absorbed at the light trap position on the integration
sphere. Specular reflectance is perfect reflectance, or glare, and including it
provides a better estimate of color as seen by the human eye. However, glare
not contribute to the spectrum, and Minolta recommends the SCE setting for
general purposes (the SCI setting is useful for color mixing or computer color
matching). Also, the SCE setting is favored for comparison with laboratory da
based only on diffuse light (Balsam et al., 1997).
Light reflected from the surface of the specimen at an angle of 8° to the norm
enters the optical fiber cable and is transmitted to spectral sensor 1. At the sa
time, the light inside the integration sphere illuminating the specimen is
transmitted to spectral sensor 2. The light from each optical fiber cable is divid
by wavelength at a 10-nm pitch (400–700 nm) before striking the segments o
silicon photodiode array of the spectral sensors. The sensors convert the ligh
electrical currents proportional to the intensity of the light. The currents are th
passed to the analog control circuits and converted into digital signals.
7—5PP Handbook , Peter Blum , November, 1997
rver
lts
or
xy,
r
ut
y-
t the
his
n a
rs.
to
e
by
ated
bjects
be
ration
ion
Measurements can be calculated based on either the 2° or 10° standard obse
and any of 11 illuminants (CIE standard illuminants A, C, D50, and D65 and
fluorescent illuminants F2, F6, F7, F8, F10, F11, and F12). Measurement resu
can then be displayed in a variety of ways: graphically as spectral reflectance
color difference, or as numerical absolute and/or difference values for XYZ, Y
L*a*b*, L*C*H°, Hunter Lab, or L*u*v* color spaces; metamerism index,
Munsell notation; CMC (2:1) or (1:1); FMC-2; whiteness index (ASTM E 313 o
CIE); or yellowness index (ASTM D 1925 or ASTM E 313). The standard outp
for the ODP database includes the full, 31-channel spectra, X, Y, Z; L*a*b*
parameters; and Munsell notation.
Do not expose this instrument to heat > 55°C (e.g., lights, direct sunlight) or
magnetic fields (e.g., speakers).
CALIBRATION
Loading Calibration Data
A white ceramic attachment (cap) is supplied with the Minolta CM-2002 as a
standard accessory. The cap is a transfer calibration standard that was factor
calibrated over 31 intervals of 10-nm length between 400 and 700 nm agains
primary standard consisting of pressed BaSO4 (ISO 7724/2) at the National
Physical Laboratory in the United Kingdom. The calibration coefficients from t
primary calibration are supplied with the Minolta CM-2002 memory card. Whe
new camera or standard is purchased, the calibration data must be loaded by
installing the new memory card. The data remain in memory until they are
changed.
The life time of the lithium battery on the memory card is approximately 2 yea
Zero Calibration Zero calibration is performed to compensate for the effects of stray light owing
the flare characteristics of the optical system. Flare characteristics may chang
over time because of dust, stains, etc., in the optical system. In addition, zero
calibration may also eliminate variations resulting from changes in ambient or
internal temperature. At the time of shipment, zero calibration data measured
Minolta are stored in an EEPROM in the CM-2002. These data should be upd
routinely.
The calibration is performed by removing the protective cap or any other
attachment from the aperture and aiming the aperture into the air so that no o
are within 1 m and no light source is aimed at. A zero-calibration box can also
used, but it is not available on the ship.
Zero calibration must be performed under the same conditions as the
measurements are taken (SCE setting, ambient temperature, etc.). Zero-calib
data will remain in memory even if the power is switched off. If a zero calibrat
is performed it must be followed immediately by a white calibration.
White Calibration The white calibration sets the maximum reflectance to 100%. Each time the
camera is switched on, or after a zero calibration has been performed, white
calibration must be performed before measurements are taken. In addition,
changes in ambient or internal temperature may affect the accuracy of the
7—6 PP Handbook , Peter Blum , November, 1997
t
nse
g
e
t.
face
e
ased.
any x
hree
ted
ic
ts
measurement. The white calibration should therefore be performed regularly,
meaning every few hours if the instrument is used around the clock.
White calibration must be performed under the same conditions as the
measurements are taken (SCE setting, ambient temperature, etc.). The calibration
data for the cap have been obtained at temperatures of 23° ± 1°C. For highes
accuracy, the instrument should always be operated at this temperature.
Do not apply plastic wrap to the standard. Although this may make relative se
on the ship, such a calibration would not allow comparison with correspondin
data obtained in other laboratories. Always cover the standard with a protectiv
cap when not in use because the color may change, even in normal room ligh
The calibration standard can be cleaned with lens-cleaning fluid. Wipe the sur
clean with a soft cloth moistened with water and let dry before use. If the whit
calibration cap becomes scratched or stained, a new standard must be purch
In this case, the new calibration data must be loaded (refer to the preceding
“Loading Calibration Data” section, and the manufacturer’s manual).
Calibration Procedure
Before measuring a new core:
1. Switch the instrument off and on. This brings you automatically to the calibration mode.
2. Remove any attachment from the aperture. Aim the aperture away fromlight sources and at least 1 m away from any object (zero-calibration boshould be available in the future).
3. Press ZERO CALIB. Wait until three measurements have been taken (tlight flashes, ~10 s). The CM-2002 automatically returns to calibration mode.
4. Attach the white calibration cap. Do not cover the white calibration padwith the plastic wrap because this reduces its validity as a factory-calibratransfer standard.
5. Press MEAS WHITE CALIB. Wait until three measurements have beentaken (three light flashes, ~10 s). The CM-2002 automatically returns toMENU mode.
6. Select DATA OUT mode from the menu. This sets up the system for measurements controlled by the external computer.
7. It is recommended that a control measurement be taken with the whitecalibration cap on.
8. Remove the calibration cap and start core measurement.
Automation of color measurements in the near future may allow semiautomat
calibration at the beginning of each core, and automatic control measuremen
would monitor drift at the beginning and the end of each section scan.
PERFORMANCE
Precision (repeatability)
Spectral reflectance: standard deviation within 0.1%.
Chromaticity value: ýE*ab within 0.03.
7—7PP Handbook , Peter Blum , November, 1997
-
ing hite n).
s;
hour
s
r as from
e
l
e ning ram
s and
-
Accuracy Accuracy depends strongly on the calibration routine. The error should be less than
1% if calibration is performed regularly. The calibration data for the white
calibration cap were obtained at a temperature of 23° ± 1°C. For the highest
accuracy, the instrument should always be operated at this temperature.
MEASUREMENT
At present, the Minolta CM-2002 is operated manually, using an external data
capture program. Thus, data quality depends largely on the operator.
1. Perform a zero calibration followed by a white calibration before measura new core (see procedure in “Calibration” section). Perform zero and wcalibrations at least once per shift (every 12 hr; see “Calibration” sectio
2. Use a consistent time lag after core splitting for the color measurementabout 1 hr is standard. Surface moisture variations from splitting and subsequent exposure of the split surface are largest during the first half to hour.
3. Cover the split-core with GLAD Cling Wrap crystal clear polyethylene, which transmits light uniformly over the spectrum of visible light and haminimal effect on the spectra (Balsam et al., 1997).
4. Do not use the optional granular-materials cover (part CM-A40).
5. Exclude the specular component (SCE setting). Although the specular component, which is essentially glare, provides a better estimate of coloseen by the human eye, it does not contribute to the spectrum reflectedsediment. Using the SCE setting should reduce the effect of varying moisture on the core surface.
6. Set the number of measurements per position. One measurement per position is sufficiently precise, but three measurements are better. Denssampling should not be compromised for multiple measurements.
7. Set the appropriate core identifier and sampling interval on the externacomputer program.
8. Take a control measurement using the white calibration attachment. Thcurrent program expects you to take a control measurement at the beginand the end of a core, which are written to the data file. (The future progwill write these measurements to a separate file.)
9. Set the photospectrometer gently on the split-core surface, and hold it orthogonal to the core surface.
10. Avoid measuring cracks because the measurement result will be useleswill degrade the value of the color profile.
11. Press the measurement button and wait until a flash occurs.
12. Set the instrument at the next interval; the program increments the prespecified interval automatically.
13. Take another control measurement when the core is measured.
14. Repeat steps 2 through 12.
15. Regularly clean the protective glass cover on the aperture.
7—8 PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
“Spectralog” Output File
Spectralog is the original ODP data acquisition program for the Minolta CM-2002
which outputs the spectral measurements as well as scores of calculated
parameters. The list below shows the ODP-customized Spectralog output,
consisting of space-delimited records written to one file per hole. Column headers
are not written by that program. During past legs, the output has produced three
columns at the end of the file which are not defined.
The Spectralog program is being replaced by an updated version which will also
acquire the tristimulus values X, Y, Z. The database model is designed accordingly.
Table 7—1 Spectralog (expected) output.
Short description Description Output file designationLeg Leg [Spectralog 1-4] legSite Site [Spectralog 8-11] siteHole Hole [Spectralog 13] holeCore Core [Spectralog 15-17] coreCore type Core type [Spectralog 19] core_typeSection Section [Spectralog 21-22] section_or_stdTop Interval top (cm) [Spectralog 24-28] interval_topBottom Interval bottom (cm) [Spectralog 30-34] interval_bottom(Depth) Empty for depth [Spectralog 36-42]L* Calculated L* [Spectralog] l_stara* Calculated a* [Spectralog] a_starb* Calculated b* [Spectralog] b_starMunsell HVC Calculated Munsell hue-value/chroma [Spectralog] munsell_hvc395-405 395–405 nm bin [Spectralog] 395–405_nm405–415 405–415 nm bin [Spectralog] 405–415_nm415–425 415–425 nm bin [Spectralog] 415–425_nm... etc. etc.695–705 695–705 nm bin [Spectralog] 695-705_nm
Undefined valueUndefined valueUndefined value
7—9PP Handbook , Peter Blum , November, 1997
Database Model
Standard Queries
Table 7—2 RSC database model
RSC Section RSC Control RSC Run RSC Calibrationsection_id [PK1] [FK] standard_id [PK1] [FK] leg [PK1] [FK] rsc_calib_date_time [PK1]
leg [PK2] [FK] leg [PK2] [FK] rsc_run_num [PK2] rsc_commentrsc_run_num [PK3] [FK] rsc_run_num [PK3] [FK] rsc_num_meas rsc_illumination_condition
rsc_run_date_time rsc_num_meas
rsc_calib_date_time rsc_observer_anglersc_reflectance_corr
RSC Run Data rsc_specular_statusleg [PK1] [FK] rsc_zero_calib_flagrsc_run_num [PK2] [FK] system_id
top_interval [PK3]bottom_interval
rsc_cielab_l_starrsc_cielab_a_starrsc_cielab_b_star
rsc_heightrsc_height_assumed_flag
rsc_munsell_hvcrsc_tristimulus_xrsc_tristimulus_y
rsc_tristimulus_zrsc_first_channel
rsc_last_channelrsc_channel_incrementrsc_spectra
Table 7—3 RSC report.
Short description Description DatabaseA: Colorimetry resultsSample ID ODP standard sample designation Link through [RSC Section] section_idDepth User-selected depth type Link through [RSC Section] section_idL* First L*a*b* parameter [RSC Run Data] rsc_cielab_l_stara* Second L*a*b* parameter [RSC Run Data] rsc_cielab_a_starb* Third L*a*b* parameter [RSC Run Data] rsc_cielab_b_starMunsell Munsell hue, value, chroma [RSC Run Data] rsc_munsell_hvcX Tristimulus value X [RSC Run Data] rsc_tristimulus_xY Tristimulus value Y [RSC Run Data] rsc_tristimulus_yZ Tristimulus value Z [RSC Run Data] rsc_tristimulus_zB (optional): Spectral resultsSpectrum String of 31 spectral reflectance values (% intensity) [RSC Run Data] rsc_spectraC (optional): Measurement parametersRun Run number on a leg [RSC Run] rsc_run_numberDate/time Run data and time [RSC Run] rsc_run_date_timeNo. of Meas. Number of measurements for each data point [RSC Run] rsc_num_measCal. date/time Date and time of last calibration [RSC Run] rsc_calib_date_timeHeight Distance between aperture and core surface [RSC Run Data] rsc_heightFirst lambda Wavelength of first channel [RSC Run Data] rsc_first_channelLast lambda Wavelength of last channel [RSC Run Data] rsc_last_channelIncrement lambda Wavelength increment between channels [RSC Run Data] rsc_channel_increment
7—10 PP Handbook , Peter Blum , November, 1997
Table 7—4 RSC control 1 data (to be implemented).
Short description Description DatabaseRun Run number on a leg [RSC Run] rsc_run_numberDate/time Run data and time [RSC Run] rsc_run_date_timeNo. of Meas. Number of measurements for each data point [RSC Run] rsc_num_measCal. date/time Date and time of last calibration [RSC Run] rsc_calib_date_timeHeight Distance between aperture and core surface [RSC Run Data] rsc_heightFirst lambda Wavelength of first channel [RSC Run Data] rsc_first_channelLast lambda Wavelength of last channel [RSC Run Data] rsc_last_channelIncrement lambda Wavelength increment between channels [RSC Run Data] rsc_channel_incrementL* First L*a*b* parameter [RSC Run Data] rsc_cielab_l_stara* Second L*a*b* parameter [RSC Run Data] rsc_cielab_a_starb* Third L*a*b* parameter [RSC Run Data] rsc_cielab_b_starMunsell Munsell hue, value, chroma [RSC Run Data] rsc_munsell_hvcX Tristimulus value X [RSC Run Data] rsc_tristimulus_xY Tristimulus value Y [RSC Run Data] rsc_tristimulus_yZ Tristimulus value Z [RSC Run Data] rsc_tristimulus_zSpectrum String of 31 spectral reflectance values (% intensity) [RSC Run Data] rsc_spectra
Table 7—5 RSC calibration data (to be implemented).
Short description Description DatabaseDate/time Calibration date and time [RSC Calib] rsc_calib_date_timeComments Number of measurements for each data point [RSC Calib] rsc_commentIllumination Illumination type/condition [RSC Calib] rsc_illumination_conditionNo. of Meas. Number of measurements averaged [RSC Calib] rsc_num_measObserver angle Observer angle [RSC Calib] rsc_observer_anglelCorrection Reflectance correction applied or not [RSC Calib] rsc_reflectance_corrSpecular Specular components measured or not [RSC Calib] rsc_specular_statusZero calib. Zero (black) calibration performed or not [RSC Calib] rsc_zero_calib_flag
7—11PP Handbook , Peter Blum , November, 1997
a
uring
ces
),
rve
s that
ent
ge
en the
ary
f the
o
the
d on
8. THERMAL CONDUCTIVITY
8.1. Principles
PHYSICAL BACKGROUND
The coefficient of thermal conductivity, k [W/(m·K)], is a measure of the rate q
(W) at which heat flows through a material. It is the coefficient of heat transfer
across a steady-state temperature difference (T2 – T1) over a distance (x2 – x1), or
q = k (∆T/∆x). (1)
Thermal conductivity can be measured by transient heating of a material with
known heating power generated from a source of known geometry and meas
the temperature change with time. The method assumes isotropic materials.
Theoretical discussion for measuring thermal conductivity with cylindrical sour
is found in Blackwell (1954), Carslaw and Jaeger (1959), De Vries et al. (1958
Von Herzen and Maxwell (1959), Kristiansen (1982), and Vacquier (1985).
For a full-space needle probe, the length L can be assumed to be infinite and the
problem is reduced to two dimensions. Given the resistance R of a looped wire in a
needle, the generated heat is
q = 2 i2 R / L, (2)
where R/L is the resistance of the needle per unit length. At any time t after heating
has started, the temperature T is related to the thermal conductivity k by
T = (q / 4πk) ln(t) + C, (3)
where q is the heat input per unit length and unit time and C is a constant. A simple
way of calculating the thermal conductivity coefficient k is picking T1 and T2 at
times t1 and t2, respectively, from the temperature versus time measurement cu
(see also ASTM, 1993):
ka(t) = q / 4π [ln(t2) - ln(t1)] / (T2 - T1). (4)
ka(t) is the apparent thermal conductivity because the true conductivity, k, is
approached only by a sufficiently large heating duration. This method assume
the measurement curve is linear and ignores the imperfections of the experim
expressed in the constant C.
In practice, the correct choice of a time interval is difficult. During the early sta
of heating, the source temperature is affected by the contact resistance betwe
source and the surrounding material. During the later stage of heating, bound
effects of the finite length of the source affect the measurement. The position o
optimum interval generally differs from measurement to measurement. The tw
systems presently available on the ship employ different procedures to select
time interval: the older Thermcon-85 system relies on operator judgment base
visual examination of the ln(t) vs. T plot; the newer TK04 system uses an
8—1PP Handbook , Peter Blum , November, 1997
32
e rear
. In
and
; ns”
algorithm that automatically finds the optimal time interval (Erbas, 1985). More
information is provided about each in the following sections.
ENVIRONMENTAL EFFECTS
In situ thermal conductivity is a function of in situ temperature and pressure
conditions. Corrections may be applied to laboratory measurements on cores,
based on in situ information and theoretical and empirical relationships. Data in
the ODP database are not corrected for in situ conditions.
USE OF THERMAL CONDUCTIVITY
Thermal conductivity is an intrinsic material property for which the values depend
on the chemical composition, porosity, density, structure, and fabric of the material
(e.g., Jumikis, 1966). In marine geophysics, mainly thermal conductivity profiles
of sediment and rock sections are used, along with temperature measurements, to
determine heat flow. Heat flow is not only characteristic of the material, but an
indicator of type and age of ocean crust and fluid circulation processes at shallow
and great depths.
8.2. Thermcon-85 System
EQUIPMENT
The Thermcon-85 system consists of the following components:
• Thermcon-85 unit,
• calibrated needle probes,
• personal computer,
• TC-PC control and data reduction program, and
• calibration file for TC-PC.
The Thermcon-85 unit was purchased from Woods Hole Oceanographic
Institution. It is under the control of PROM-based programming, and an RS-2
serial interface is available. One to five needle probes can be connected to th
panel. An eight-channel multiplexer selects the appropriate input for each
measurement. See the Thermcon-85 manual for more details.
The needle probes are either assembled at ODP or purchased preassembled
either case, they contain factory-calibrated thermistors.
The TC-PC program was developed at ODP in 1991 using Quick Basic (v. 4.5)
runs on a PC clone. The following programs are involved:
• TCMENU: controls the overall data acquisition process;
• COLLECT: communicates with the Thermcon-85; performs drift studycollects raw data and writes raw data file; monitors “bad data conditio(warnings not written to data file);
8—2 PP Handbook , Peter Blum , November, 1997
on
bes le.
SS
tered
.
e of
ar
he
rsion
sible
o re-
on
d as a
d the
ed
ed
nts.
• PROCESS: allows selection of probe positions; allows for optional correction for temperature drift at drift study termination; allows selectiof “optimal” interval; reduces the raw data and calculates thermal conductivity; writes to a results file; and
• PROBES: used to enter thermistor calibration coefficients for new proand “secondary” probe calibration constants into the PROBES.DAT fi
The user normally runs TCMENU. Interaction with the COLLECT and PROCE
programs is accomplished via menu selection. The calibration data must be en
into the PROBES.DAT file when appropriate.
CALIBRATION
Power Supply, Digital Volt Meter, and Heater Current
Calibration must be periodically performed by an ODP Electronics Technician
Refer to the Thermcon-85 manual for details.
Needle Probe Resistance
The thermistors in each needle probe are calibrated at the factory over a rang
temperatures (usually 15° to 75°C) and fit to an equation of the form
T-1 = alpha + beta ln(R) + gamma (ln(R))3, (5)
where T is the temperature in degrees Kelvin, R is the thermistor resistance in
ohms, and alpha, beta, and gamma are constants. The error in this procedure is f
smaller than the general uncertainty in thermal conductivity measurements. T
constants are available to the data reduction program and are used for conve
of measured resistance into temperature. Electronics Technicians are respon
for entering the constants of a new resistor into the program. Do not attempt t
calibrate the thermistors—a specialized facility is required.
Needle Probe Secondary Calibration
ODP procedure with the Thermcon-85 system includes a calibration of each
needle probe using standard materials of “known” thermal conductivity values
(Table 8—1). These values were established on Legs 127, 129, and 131 and
subsequent legs using this same instrument. This calibration should be viewe
relative one that makes ODP shipboard data a little more consistent.
The standard measurements must be entered into a separate spreadsheet an
liner coefficients (slope, intercept) determined. The coefficients are then enter
into the PROBES.DAT file using the PROBES program utility. The thermal
conductivity values returned by the PC-TC program are subsequently correct
using these coefficients.
Table 8—1 Standard materials used for calibrations and control measureme
Standard material Thermal conductivity [W/(m·K)]
Black rubber 0.54
Red rubber 0.96
Macor 1.61
8—3PP Handbook , Peter Blum , November, 1997
PERFORMANCE
Precision About 5%. (Systematic evaluation is required.)
Accuracy About 5%. (Systematic evaluation is required.)
MEASUREMENT
1. Bring cores to temperature equilibrium (about 4 hr). Hard-rock specimens should be placed in a water bath to equilibrate.
2. Soft sediment: drill holes into core liner. Also drill a small hole in semiconsolidated sediment if necessary. Apply thermal joint compound if necessary. Insert full-space probes carefully into sediment. Rocks: prepare smooth surface on a split-core specimen at least 5 cm long. Treat the needles gently, and store them properly when not in use.
3. Insert one probe into a standard material for a control measurement, to be used for later corrections if necessary.
4. Start the TCMENU program and follow the prompts for parameters. Default values are provided for each prompt.
5. Press the reset button on the Thermcon-85 unit to start the drift study. After a couple of minutes, the drift data will be displayed. The drift study is performed in phases of 25 minutes, the maximum time the box can be programmed. The drift study is terminated if all positions are equilibrated or if the user overrides the drift study.
6. Press the reset button twice to start the process of heating, data acquisition, and creation of the raw data file. Messages will be displayed if there are data or hardware problems.
7. The user has the option of acquiring more data and processing batches of data later or processing the data collected immediately. It is recommended to process the data immediately.
8. Load the PROCESS program from the TCMENU screen. The run just completed will appear as the default run to be processed. Accept or change it.
9. Select the position to be processed and the drift correction. The ln(t) vs. T graph will be displayed.
10. Select the time interval to be processed by moving the cross hairs on the screen. For routine processing, use the same interval used for secondary probe calibration. Adjust if necessary. Press enter to calculate conductivity and the fit parameter. Warnings will come up if the nonlinear component is considered too large, the fit is poor, the segment is considered too short, etc.
11. Press enter twice to write the conductivity of a segment to the Results file.
DATA PROCESSING
Data reduction with the TC-PC program written for the Thermcon-85 system is
based on a least-squares fit of the measured temperatures to the following
equation, which is a variation of Equation XXX(107?):
T = (q / 4¼k) ln(t) + At + B. (6)
8—4 PP Handbook , Peter Blum , November, 1997
d for
s are
ssible.
sed,
re
the
t
and
The constant A is the temperature drift rate (also including edge effect, asymmetry,
nonzero epoxy conductivity, etc.) during measurement and is expressed in K/min.
The constant B represents other imperfections in the experiment. The unknowns in
this system are k, A, and B, so when more than three data pairs are acquired the
system is overdetermined. Using the previous equation for the rate of heating, the
coefficient k can be determined at any time increment dt as
k = [2 i2 R / L dln(t)] / 4¼ (dT - At - B)], or (7)
k = (i2 R / 2¼L) [dln(t) / (dT)]. (8)
The first group of terms in these equations is an instrument constant including
generated heat and needle geometry. The second group of terms is calculate
each measurement.
The optimum time segment for calculating thermal conductivity is selected
interactively by the user by placing cross hairs on a ln(t) vs. T plot of the data.
Information on the quality of the fit is updated on the screen as the cross-hair
moved. The curve-fit parameter is the root mean square of the temperature
deviation and should not exceed 0.04°C/min. However, it is more important to
choose a consistent sampling time than it is to reduce the drift as much as po
DATA SPECIFICATIONS
TC-PC Output Files At present, the TC-PC data are not integrated in the new ODP database. The
following two program output files are archived: the “Processed Data” or
“Results” (*.DAT) files and the “Raw Data” (*.TC) files.
Data in the *.DAT files are fixed format, mixed string, and numeric, with one
record (line) per position per TC run. If a given position on a run is not proces
then there is no entry in this file. However, if a given position is processed mo
than once, there are multiple lines in this file for that position. The file name is
hole identifier.
Data in the *.RAW files are free-format in which each line represents an outpu
string from the program. If a position was not used, some strings are omitted
some return zero values. The file name is a combination of hole ID and run
number.
Table 8—2 TC-PC “Processed Data” file.
Short description Description Data file designationsLeg Leg [TC-PC Results 1-4] legSite Site [TC-PC Results 8-11] siteHole Hole [TC-PC Results 13] holeCore Core [TC-PC Results 15-17] coreCore type Core type [TC-PC Results 19] core_typeSection Section [TC-PC Results 21-22] section_or_stdTop Interval top (cm) [TC-PC Results 24-28] interval_topBottom Interval bottom (cm) [TC-PC Results 30-34] interval_bottomSpace Space model [TC-PC Results 49] full_or_halfRun No. Run number [TC-PC Results 51-53] run_numberProbe Probe number [TC-PC Results 55-57] probe_numberPosition Position number [TC-PC Results 59] position_numberTC uncorr. Uncorr. thermal conductivity. [W/(m·K)] [TC-PC Results 61-67] calculated_tc
8—5PP Handbook , Peter Blum , November, 1997
Notes: The numbers following the file name (TC-PC Results . . .) are positions in the fixed-space format of the output file. Corrected thermal conductivity is corrected using the secondary probe calibration coefficients m1 and m0 obtained from standard measurements. Corrected thermal conductivity is added only if the user selects this option when specifying data reduction. If correction is not selected, the position numbers are reduced by 8 spaces starting with the “Standard error” field.
TC corr. Corr. thermal conductivity. [W/(m·K)] [TC-PC Results 69-75] corrected_tcR2 Standard error R2 [TC-PC Results 77-87] standard_error
Drift Calculated drift (°C/s) [TC-PC Results 89-97] calculated_driftLower end Lower end point used [TC-PC Results 99-100] lower_end_pointFirst time Time at lower end point (s) [TC-PC Results 102-104] time_at_first_pointUpper end Upper end point used [TC-PC Results 106-107] upper_end_pointLast time Time at upper end point (s) [TC-PC Results 109-111] time_at_last_pointDrift status Drift study status [TC-PC Results 113-126] drift_statusT drift Temp. at drift study termination (°C) [TC-PC Results 128-132] drift_temperatureDrift rate Drift rate at termination (°C/s) [TC-PC Results 134-142] drift_rateDrift fit Least-squares fit for drift [TC-PC Results 144-151] drift_fitRun status Run status (NORMAL, ...) [TC-PC Results 153-160] run_statusAlpha Probe alpha constant [TC-PC Results 162-180] probe_alphaBeta Probe beta constant [TC-PC Results 182-200] probe_betaGamma Probe gamma constant [TC-PC Results 202-220] probe_gammaResistance Probe wire resistance (ohm/cm) [TC-PC Results 222-227] probe_wire_resistanceHalf space Probe half-space flag (1 = true) [TC-PC Results 229-230] half_space_flagProbe m1 Probe secondary calibration slope [TC-PC Results 232=238] probe_m1Probe m0 Probe secondary calibration intercept [TC-PC Results 240-246] probe_m0Lower end Upper end point, probe calibration (s) [TC-PC Results 248-250] time_at_first_pointUpper end Lower end point, probe calibration (s) [TC-PC Results 252-254] time_at_last_pointDrift corr. Drift correction status [TC-PC Results 256-268] drift_correction_statusVersion Version of TC-PC program [TC-PC Results 270-274] tcpc_versionComment Comment [TC-PC Results 276-356] comment
Table 8—2 TC-PC “Processed Data” file.
Table 8—3 TC-PC “Raw Data” file (free format).
Short description Description Data file designationsRun parametersTitle Title string [TC-PC Raw 1] titleRun Run number [TC-PC Raw 2] run_numberPositions No. of positions used; length (min.) [TC-PC Raw 3] no_of_positions_lengthParameters for first positionSample ID ODP sample identification [TC-PC Raw 4] sample_idPiece Piece [TC-PC Raw 5] pieceSubpiece Subpiece [TC-PC Raw 5] sub_pieceSpace Space model [TC-PC Raw 7] full_or_halfPosition no. Position number [TC-PC Raw 8] position_numberAlpha Probe alpha constant [TC-PC Raw 9.1] probe_alphaBeta Probe beta constant [TC-PC Raw 9.2] probe_betaGamma Probe gamma constant [TC-PC Raw 9.3] probe_gammaResistance Probe wire resistance (ohm/cm) [TC-PC Raw 9.4] probe_wire_resistanceHalf space Probe half-space flag (1 = half) [TC-PC Raw 9.5] half_space_flagProbe m1 Probe secondary calibr. slope [TC-PC Raw 9.6] probe_m1
Probe m0 Probe secondary calibr. intercept [TC-PC Raw 9.7] probe_m0
Lower end Lower end point, probe calib. (s) [TC-PC Raw 9.8] time_at_first_pointUpper end Upper end point, probe calib. (s) [TC-PC Raw 9.9] time_at_last_pointComment Position-specific comment [TC-PC Raw 10] comment
Parameters repeated for other positionsa
Drift time Drift: no. of readings; length(s) [TC-PC Raw one line, two values]Drift study for first positionDrift t-T String of time-temperature pairs [TC-PC Raw one line, unlimited pairs]Drift end Temp., rate., fit, at end of drift study [TC-PC Raw one line, three values]
Drift study repeated for other positionsb
8—6 PP Handbook , Peter Blum , November, 1997
Notes: aThe probe parameters of lines 4–10 are written for subsequent positions only if the positions were used, otherwise the lines are omitted. bThe drift study data lines (two lines per position) are always written to the file regardless whether positions were used or not. If a position was not used, all values are zero. cData are written on one line for each measurement cycle. On each line, there are the following readings separated in time by 3 s (hard-coded in the program): (1) cycle number; (2) internal reference voltage; (3) to (7) up to five probe voltage readings (no reading for unused positions); (8) heater current. Total time for one cycle is (2 + <number of positions used>) times 3 s (2 stands for reference and heater current readings). It varies between 6 s (no position used) and 21 s (five positions used).
Database Model
Standard Queries The standard queries will be defined once the upload routine has been
implemented.
Drift status Drift status (OK; OVERRIDE) [TC-PC Raw one line, one alpha string]Data for positions 1–5Data Cycle #; ref. volt; I1 to I5; current [TC-PC Raw multiple lines, 3-8 values per line)
Data repeated for each meas. cyclec
Run status Run status (NORMAL...) [TC-PC Raw one line, one alpha string]
Table 8—3 TC-PC “Raw Data” file (free format).
Table 8—4 Database model
TCON section TCON probe proc. data TCON runtcon_id [PK1] [FK] tcon_id [PK1] [FK] tcon_id [PK1]
tcon_probe_num [PK2] [FK] tcon_probe_num [PK2] tcon_run_minutestop_interval tcon_comment tcon_run_numberbottom_interval tcon_meas_calib_m0 tcon_run_status
section_id tcon_meas_calib_m1tcon_meas_calib_time_first
tcon_meas_calib_time_last TCON cycleTCON control tcon_meas_drift_lsq_fit tcon_id [PK1] [FK]tcon_id [PK1] [FK] tcon_meas_drift_rate_final tcon_cycle_num [PK2]
tcon_probe_num [PK2] [FK] tcon_meas_drift_temp_final tcon_raw_heater_currentstandard_id [PK3] [FK] tcon_probe_alpha tcon_raw_heater_curr_time
tcon_probe_beta tcon_raw_rel_voltagetcon_probe_gamma tcon_raw_rel_voltage_time
TCON drift raw data tcon_probe_half_full
tcon_id [PK1] [FK] tcon_probe_specific_restcon_probe_num [PK2] [FK] tcon_proc_drift_corr_flag TCON probe cycle
tcon_raw_drift_time [PK3] tcon_proc_point_first tcon_id [PK1] [FK]tcon_raw_drift_temp tcon_proc_point_last tcon_cycle_num [PK2] [FK]
tcon_proc_thermcon tcon_probe_num [PK3]
tcon_proc_time_first tcon_raw_timetcon_proc_time_last tcon_raw_voltage
tcon_raw_drift_statustcon_raw_pos_num
8—7PP Handbook , Peter Blum , November, 1997
for
s on
e
L
-
on
nt
nd a
cle.
uired ed, er
8.3. TK04 System
EQUIPMENT
ODP purchased the TK04 system in late 1995 and deployed it permanently on the
ship on Leg 168 (1996). The system was to replace the ailing Thermcon-85 device,
built at the Woods Hole Oceanographic Institution (WHOI) and in service on the
ship for many years. Currently, both systems are available to the user on the ship.
The TK04 was built by the Berlin company Teka based on an apparatus that had
been developed at the Technische Universität Berlin. It was used successfully
thousands of measurements on material from the Continental Deep Drilling
Program (KTB). The TK04 consists of
• automatic self-test, heating, and measurement unit TK04,
• full-space (VLQ) and half-space (HLQ) needle probes,
• vice and manual hydraulic pump for half-space contact measurementrocks, and
• Macor standards for both types of needle probes.
The TK04 measuring system features a self-test at the beginning of each
measuring cycle (including probe number validation), registration of the sourc
temperature and its drift, and calculation of the heating power used.
The following executable programs are used to operate the system:
• TKMEAS.EXE to acquire time-temperature data series (creating *.DWfiles),
• TKEVA for standard (<5% uncertainty) or special (<2% uncertainty) reevaluation of data, creating short *.DAT or long *.ERG lists and parameter files, and
• TKGRAPH to display all solutions and assess the quality of the calculated solutions.
In addition, the following parameter files are used:
• TKMEAS.MNU, a list of standard menu settings for TKMEAS.EXE,
• *.INI, list of parameters for probes, where “*” is the number engraved the probe, and
• TKEVA.INI, list of user-modifiable parameters required for TKEVA.EXE.
Multiple measurements can be taken under identical conditions. The instrume
cycles through the measurements automatically, creating files with the user-
defined root name (e.g., Core-Section-Interval; only six characters allowed) a
two-digit serial number incrementing by one for each measurement within a cy
The following files are created by the TK04 system:
• <Rootname-SerialNo>.DWL, (if “Save data” was selected); contains measurement parameters and temperature-time series (raw data), reqfor extended evaluations; it is not necessary, but strongly recommendto save the heating curves for routine evaluation. These files allow latextended evaluation and graphical display of the solutions.
• <Rootname->.LST, short list of results from evaluating one root-name-batch of *.DWL files using either the “special approximation method”
8—8 PP Handbook , Peter Blum , November, 1997
le is
ted
at
ch
alue.
res
time
the
ated
(SAM) or conventional (CON) method; contains evaluation parameters and the optimal calculated thermal conductivity value. This is the standard results file.
• TC-LIST.DAT, multiline short list (optional); contains the same information as previous file <Rootname->.LST but for multiple root names. This file is updated as new evaluations are performed. This ficreated only by the optional extended evaluation.
• <Rootname>.ERG, long lists of results from evaluating *.DWL files withthe SAM method; contains evaluation parameters and all valid calculathermal conductivity values. This file is optional and required only if graphical evaluation of all valid solutions is desired. It can be createdany time if the *.DWL files are saved. This file is created only by the optional extended evaluation.
CALIBRATION
No calibration is required. The unit conducts a self-test at the beginning of ea
measurement cycle. Macor standards are used to confirm the 1.65 W/(m·K) v
DATA PROCESSING
The Special Approximation Method (SAM)
The main advantage of the Teka data reduction program is the SAM that ensu
that only results of physical significance are considered. The critical choice of
interval for calculation of conductivity, selected manually by the user with the
Thermcon-85 system, is accomplished by an algorithm that automatically finds
optimal time interval. The solution can be judged in great detail and the data
reevaluated with different boundary parameters if warranted. The following
explanations are modified from the Teka user manual.
The first evaluation step is an approximation to the solution of a constantly he
line source (Kristiansen, 1982):
T(t) = A1 + A2ln(t) + A3[ln(t)/t] + A4(1/t). (9)
The coefficients Ai are calculated with the least-squares method. A1, A3, and A4 are
related to source geometry and thermal properties. A2 is calculated by
A2 = q / 4πk, (10)
where q is the heating power (Wm) and k [W/(m·K)] is the thermal conductivity. If
the coefficients Ai are determined, T(t) can be expressed analytically and the
apparent thermal conductivity Ka(t) can be calculated by differentiating Equation
on page 9 with respect to ln(t):
ka(t) = dT/dln(t) = q/4π {A2 + A3[1/t – ln(t)/t] + A4/t}. (11)
It can be shown that the desired value k is at ka(tmax), where tmax is the “extreme
time.” The requirement for the maximum is
d/dt[ka(tmax)] = 0, (12)
and tmax is
tmax = e(2A3–A4)/A3, A3 > 0. (13)
The logarithm of the extreme time (LET) becomes
8—9PP Handbook , Peter Blum , November, 1997
t be
g for
ng
T
LET = ln(tmax) = (2A3 - A4) / A3. (14)
The time-dependent terms in previous equation are:
T(tmax) = A2ln(tmax) + A3[ln(tmax)/tmax] + A4/tmax. (15)
A4 can be substituted with (previous) Equation (118?) to give
T(tmax) = A2ln(tmax) + 2A3[ln(tmax)/tmax]. (16)
This equation shows that the purely logarithmic dependence of the approximated
temperature (required by the theory) is stronger the larger tmax gets. For large tmax,
the second term in Equation on page 10 approaches zero.
The evaluation procedure approximates the heating curve in as many time
intervals as possible and examines each interval for its suitability for thermal
conductivity calculation using the following criteria:
1. ka(t) is located above a given value of time defined by LET,
2. standard deviation of the function for A2 is below a given value,
3. ka(t) is a maximum: A3 > 0, and
4. derivation ka(t) is continuous for t = tmax: A2tmax – A3 - 0.
If these criteria are met, thermal conductivity can be calculated as
k = q / (4πA2). (17)
The evaluation interval is restricted by the dimension of the line source. It mus
within the interval of 20 to 80 s to avoid boundary effects, and at least 25 s lon
a stable calculation of the coefficients. The input parameters for standard
evaluation are
• minimum duration of approximation interval: 25 s,
• start of first approximation interval: 20 s,
• end of last approximation interval: 80 s,
• lower limit for LET: 4, and
• maximum standard deviation of calculated temperature curve from measured heating curve: 0.0003.
With the default parameters, the heating curve is approximated for the followi
time intervals:
[20,45] [20,46] [20,47] . . . [20,78] [20,79] [20,80][21,46] [21,47] . . . [21,78] [21,79] [21,80][22,47] . . . [22,78] [22,79] [22,80]. . .[53,78] [53,79] [53,80][54,79] [54,80][55,80]
Among all time intervals that fulfill the listed criteria, the one with the largest LE
is used to calculate thermal conductivity. No solutions may be found if the
measurement is disturbed by poor sample condition or ambient temperature
changes.
Extended Evaluation An extended evaluation is required if
8—10 PP Handbook , Peter Blum , November, 1997
s to
.g., a
red.
for
th
r
ted),
e
th
alid
stem
ses,
in a
M
ure
• the valid solutions are to be plotted against the calculation parameterjudge the results graphically, or
• the measurements are to be reevaluated with different parameters (estronger criterion for the LET).
In both cases, the *.DWL files containing the temperature-time data are requi
The *.ERG files (long result lists) that can be created contain all valid solutions
the thermal conductivity, and a line entry in the TC-LIST.DAT file is created wi
the asymptotic (optimal) thermal conductivity value. There are three options fo
extended evaluation:
• single evaluation: typing <TKSAM> prompts for filename,
• batch mode with filename as parameter: typing <TKSAM filename> starts evaluation using the standard parameters (no *.ERG file is creaand
• Batch mode evaluating a sequence of data files: after typing TKSAM, type return instead of a filename; all *.DWL files in the directory will bevaluated.
The manufacturer’s manual should be consulted for details in regard to file pa
requirements, data quality issues, etc.
Graphical Evaluation The program TKGRAPH can be used to visualize and judge the quality of all v
SAM evaluation results for thermal conductivity. *.ERG files are required for
plotting. Four graphs are presented for each measurement:
• thermal conductivity vs. LET,
• thermal conductivity vs. interval duration,
• thermal conductivity vs. start of interval, and
• thermal conductivity vs. end of interval.
A series of files can also be viewed. Consult the manufacturer’s manual for sy
configuration, practical hints, guidance for the judgment of results, etc.
Evaluation with Conventional Method
Under certain experimental circumstances (e.g., porous material, high water
content) the SAM evaluation may not accept any results because the
measurements are too disturbed for the sensitive approximations. In these ca
results may be obtained using the conventional evaluation method in which
thermal conductivity is calculated from the inverse slope of the heating curve
section of logarithmic linearity. In general, a heating duration > 80 s becomes
necessary. Accuracy of conventional evaluations is not as good as that of SA
evaluations and the quality cannot be verified graphically.
The program TKCON.EXE is used for the conventional evaluation. The struct
and application is similar to the TKSAM.EXE program. The configuration file
TKCON.INI includes the following standard parameters:
• minimum duration of interval: 30 s,
• start time: 30 s,
• end time: 120 s, and
• standard deviation of fit: 0.003.
8—11PP Handbook , Peter Blum , November, 1997
ed
rmine
lds an
l
rial
ents
the
ns
if at
Existing data can be evaluated later with the conventional method (i.e., after the
SAM method has failed to yield solutions). Automatic Evaluation with TKCON
can be set by typing
TKMEAS/EVA=CON
or if the option
TKMEAS/DCL=20/EVA=CON
is entered. Calling TKMEAS without the /EVA option invokes evaluation with
TKSAM.EXE.
A short list of results is created by TKCON with similar structure as the file
created by TKSAM. The difference is that instead of LET the standard deviation is
reported. The evaluation method used (SAM; CON) is indicated in each line of the
file. A long list of results for each measurement can be produced by typing, prior
to starting TKMEAS:
set TKCON=ON
The long list includes the calculated values of thermal conductivity, standard
deviation, and the start, duration, and end of each interval.
Half-Space Measurements
For the half-space needle probe (HLQ) it is expected that the total amount of
produced heat penetrates into the sample. The thermal conductivity is thus
calculated with twice the heating power used for the full-space solution. This
assumption is justified if the thermal conductivity of the samples is not lower than
about 1 W/(m·K); at lower values an error arises because some of the produc
heat is penetrating the probe half-space, in which case it is necessary to dete
correction factors to compensate for the heat loss.
PERFORMANCE
Precision Extended evaluation, using special parameters adapted to circumstances, yie
uncertainty of less than 2%. This is clearly smaller than variations caused by
sample preparation and inhomogeneities in rocks and sediments, and specia
evaluations are appropriate only for standard materials and fundamental mate
investigations.
Accuracy Random variations of thermal conductivity in natural materials such as sedim
and rocks typically give an uncertainty of about 5%. Routine evaluation using
TKEVA.EXE has an accuracy of about 5% and is therefore appropriate.
MEASUREMENT
Standard Settings for Data Acquisition
1. Bring cores to temperature equilibrium (about 4 hr). Hard-rock specimeshould be placed in a water bath to equilibrate.
2. Soft sediment: drill holes into core liner. Also drill a small hole in semiconsolidated sediment if necessary. Apply thermal joint compoundnecessary. Insert full-space probes carefully into sediment. Hard-rocks:prepare smooth surface on a half-core specimen at least 5 cm long. Treneedles gently, store them properly when not in use.
8—12 PP Handbook , Peter Blum , November, 1997
3. On the computer, change to directory containing the TKMEAS.EXE file, press enter.
4. Type TKERG = ON, press enter.
5. Type the command tkmeas, press enter.
6. Set the parameters on the screen. Heating power should be about 5 W/m (adjust if necessary); measuring time should be about 80 s; enter Y to save time-temperature data.
DATA SPECIFICATIONS
TK04 Output Files Currently, TK04 data are not integrated in the new ODP database. The following
program output files are archived.
Table 8—5 TK04 “raw data file”: <Rootname-Serial>.DWL.
Short description Description Data file designationHeaderFilename Root name (custom sample id), serial [TK04 Raw Data] rootname_serialProbe Probe ID, TK04, date [TK04 Raw Data] probeComment Comment, used to identify sample [TK04 Raw Data] commentHeat Heating power (W/m) [TK04 Raw Data] heating_powerFit Slope, Std. dev., temperature [TK04 Raw Data] fit?Something ?’Reserved’ [TK04 Raw Data] ?something?Value1 ?Some (drift?) value 1 [TK04 Raw Data] ?value1?Value2 ?Some (drift?) value 2 [TK04 Raw Data] ?value2DataTemp Temperature (°C) [TK04 Raw Data] temperatureTime Time (s) [TK04 Raw Data] timeResistance Resistance (ohm) [TK04 Raw Data] resistance
Table 8—6 TK04 “results short list”: <Rootname>.LST (one rootname batch).
Short description Description Data file designationFilename Root name + serial (sample ID) [TK04 Results] rootname_serialTC Calculated thermal conductivity [TK04 Results] calculated_tcLET/STD LET (SAM) of std. dev. (CON) [TK04 Results] let_or_sdSolutions No. of solutions found [TK04 Results] solutionsStart time Start of approx. time interval (s) [TK04 Results] time_startTime Length of approx. time interval (s) [TK04 Results] time_lengthEnd time End of optimal time interval (s) [TK04 Results] time_endEval. Evaluation method (SAM or CON) [TK04 Results] eval_methodHints Comments (from *.DWL file) [TK04 Results] hints
Table 8—7 *TK04 “appended results short list”: <Rootname>.LST (all rootnames).
Short description Description Data file designationFilename Root name + serial (sample id) [TK04 Results] rootname_serialTC Calculated thermal conductivity [TK04 Results] calculated_tcLET/STD LET (SAM) of std. dev. (CON) [TK04 Results] let_or_sdSolutions Number of solutions found [TK04 Results] solutionsStart time Start of approximate time interval (s) [TK04 Results] time_startTime Length of approx. time interval (s) [TK04 Results] time_lengthEnd time End of optimal time interval (s) [TK04 Results] time_endEval. Evaluation method (SAM or CON) [TK04 Results] eval_methodHints Comments (from *.DWL file) [TK04 Results] hints
8—13PP Handbook , Peter Blum , November, 1997
Notes: *ERG files are optional. They are created by extended evaluation and are required only for graphical evaluation. They can be recreated from *.DWL files at any time.
Database Model A database model and integration into the database are difficult to implement
without writing an ODP sample identification routine linked to the TK04 output. A
better approach is to write an entirely new user interface for the system, preferably
for an upgraded version with multiple-channel capability.
Table 8—8 *TK04 “extended results file”: *.ERG files.
Short description Description Data file designationHeader: SAM Evaluation Parameters TKSAM.EXEFilename Root name + serial (sample ID) [TK04 Results] rootname_serialComment Comment, used to identify sample [TK04 Raw Data] commentTime Time interval minimum (s) [TK04 Results] eval_interval_minStart time Start of evaluation (s) [TK04 Results] eval_time_startEnd time End of optimal time interval (s) [TK04 Results] eval_time_endLET Nat. log. of time [TK04 Results] eval_letStd. Dev. Limit of std. dev. (optional; 0.0003) [TK04 Results] eval_limit_sd
Table 8—9 Valid solutions.
Short description Description Data file designationTC Calculated thermal conductivity [TK04 Results] calculated_tcLET Natural logarithm of time at max. therm.al condition [TK04 Results] letStart time Start of approx. time interval (s) [TK04 Results] time_startTime Length of approx. time interval (s) [TK04 Results] time_lengthEnd time End of optimal time interval (s) [TK04 Results] time_endStd. Dev. Standard deviation of fit [TK04 Results] std-deviation
8—14 PP Handbook , Peter Blum , November, 1997
to
ally
rent
y of
ngth
9. STRENGTH
9.1. Principles
PHYSICAL BACKGROUND
Definition of Sediment Strength
Most soils and rocks are visco-elastic materials. Well-developed mathematical
theories are available only for linear visco-elasticity, whereas soils and rocks have
highly nonlinear stress-strain-time behavior. Therefore, time-independent elasto-
plastic theory is often used to describe the stress-strain relationships of natural
materials: the material is linearly elastic up to the yield point, and then it becomes
perfectly plastic (Holtz and Kovacs, 1981). Some materials are brittle and exhibit
little stress when strained (rocks); others are work-hardening (e.g., compacted
clays and loose sands) or work-softening. The latter model is particularly
applicable to clayey, soft, saturated, marine sediments, such as those usually
measured with the instruments described in this chapter: stress decreases as the
sediment is strained beyond a peak stress. The sediment yields (fails) at the peak
stress, which can be defined as the sediment’s strength.
Mohr-Coulomb Failure Criterion
According to Mohr, the shear stress on a failure plane at failure reaches some
unique function of the normal stress on that plane, or
τƒƒ = ƒ(σƒƒ), (1)
where τ is the shear stress and σ is the normal stress. The first subscript ƒ refers
the failure plane and the second ƒ means “at failure.” This function can graphic
be expressed by the Mohr failure envelope, the tangent to Mohr circles at diffe
τ and σ at failure. The Mohr failure hypothesis states that the point of tangenc
the Mohr failure envelope with the Mohr circle at failure determines the
inclination of the failure plane.
Coulomb found that there was a stress-independent component of shear stre
and a stress-dependent component. He called the latter the internal angle of
friction, φ, and the former seems to be related to the intrinsic cohesion and is
denoted by the symbol c. The Coulomb equation is then
τƒ = σ tanφ + c, (2)
where τƒ is the shear strength of the soil, σ is the applied normal stress, and φand c
are the strength parameters. Both parameters are not inherent properties of the
material tested, but also depend on the test conditions.
The Mohr-Coulomb strength criterion is the combination Mohr failure envelope,
approximated by linear intervals over certain stress ranges, and the Coulomb
strength parameters:
τƒƒ = 域 tanφ + c. (3)
9—1PP Handbook , Peter Blum , November, 1997
This is the only failure criterion that predicts the stresses on the failure plane at
failure, which is relevant to potential sliding surfaces in geotechnical applicatons.
Drained and Undrained Shear
When sediment is sheared under a load or applied stress, excess pore pressure is
produced that may or may not escape depending on the permeability of the
sediment and the time available. If the pore pressure can dissipate, the sediment is
most likely work-hardened. Therefore, from an experimental standpoint (triaxial
testing), undrained shear (total stress analysis) or drained shear (effective stress
analysis) can be applied to the sediment.
In the undrained shear scenario, volume changes translate into pore pressure
changes, and the assumption is made that the pore pressure and therefore the
effective stress (= total stress minus pore pressure) are indentical to those in the
field. The total, or the undrained shear strength, is used for the stress analysis.
Tests must be conducted rapidly enough so that undrained conditions prevail if
draining is possible in the experimental setup.
In the second, drained scenario, shear stress is used in terms of effective stresses.
The excess hydrostatic pressure must be measured or estimated. Knowing the
initial and the applied (total) stresses, the effective stress acting in the sediment can
be calculated. The volume change depends on the relative density and the
confining pressure. This approach is philosphically more satisfying because pore
water cannot carry any shear stress; i.e., shear strength is thought to be controlled
by the effective stresses (Holtz and Kovacs, 1981). Drained shear can ordinarily be
determined only in the laboratory and the procedure is not popular because there
are serious practical problems. Particularly in low-permeability material, the rate
of loading must be sufficiently slow to avoid the development of excessive pore
pressure, which can cause a test to take many days or weeks, and valve, seal, and
membrane leaks may become a problem.
Testing for Shear Strength
There are three limiting conditions of consolidation (happens before shear) and
drainage (happens during shear) that model real field situations: consolidated-
drained (CD), consolidated-undrained (CU), and unconsolidated-undrained (UU).
Unconsolidated-drained is not a meaningful condition because drainage would
occur during shear and the effects of confining pressure and shear could not be
separated. A special case of the UU test is the unconfined compression (labeled
here informally as UUU) test, where the confining pressure equals zero
(atmospheric pressure). This is by far the most common laboratory strength test
used in geotechnical engineering today (Holtz and Kovacs, 1981). The effective
stress at failure, and therefore the strength, is identical for the UU and UUU tests.
In practical terms, the following conditions must be satisfied for this to be true:
1. 100% saturation,
2. specimen (core interval) must be intact and homogenous,
3. material must be fine-grained (clay), and
4. specimen must be sheared rapidly to failure to avoid draining and evaporation.
Direct shear test and triaxial tests are the common laboratory shear strength tests.
Addiitonal special tests are for direct simple shear, ring shear, plain strain, and true
9—2 PP Handbook , Peter Blum , November, 1997
the
here
d
ngs
ure
t of
triaxial test. These tests allow independent control and measurement of at least
principle stresses, σ1 and σ3, and changes in void ratio and pore pressure. The
results can be analyzed in the σ-τ diagram (Mohr circle), p-q diagram (stress
path), and other methods (e.g., Lambe and Whitman, 1979; Holtz and Kovacs,
1981). However, all these tests are too complex to be conducted in the shipboard
laboratory. Instead, ODP provides two rapid and simple tests, the vane shear tests
and the penetrometer test. These tests should be used as a guide only because t
are many reasons why the results are only approximate (e.g., Lambe and Whitman,
1979). Particularly the influence of pore pressure changes during the undraine
experiment cannot be estimated.
Vane Shear Test Undrained shear strength can be determined using a vane that is inserted into soft
sediment and rotated until the sediment fails. The torque, T, required to shear the
sediment along the vertical and horizontal edges of the vane is a relatively direct
measure of the shear strength. It must be normalized to the vane constant, K, which
is a function of the vane size and geometry:
τƒ ~ su = T / K, (4)
where su is a common notation for the vane shear strength (e.g., Lambe and
Whitman, 1979). Shear strength has the units of pascals (= N/m2), torque has the
units of newton·meters (N·m), and K has the units of meters cubed (m3). Two
systems are available onboard JOIDES Resolution to determine vane shear
strength. The automated vane shear system measures angular deflection of spri
that were calibrated for torque. The hand-held Torvane directly returns a measure
of shear strength from calibrated springs.
Penetrometer Test Failure can be defined as the maximum principal stress difference, which is the
same as the (unconfined) compressive strength of the specimen, σ1 – σ3. At a
prescribed strain, shear strength, τƒ, is related to compressive strength, ∆σƒ , by
τƒ ~ τmax = (σ1 – σ3) / 2 = ∆σƒ / 2. (5)
If ∆σƒ is determined in a UUU test by reading off the vertical strain, such as with
the pocket penetrometer, the value must be divided by 2 to obtain the shear
strength.
ENVIRONMENTAL EFFECTS
If there is visible core disturbance, measurements should not be taken. Moist
loss while the split core is being processed affects the shear strength
measurements.
USE OF SHEAR STRENGTH
Shear strength, or shear resistance, of sediments is the most important aspec
slope stability. However, the shear strength values obtained onboard do not alone
allow any slope stability analysis. They represent merely a relative strength profile.
9—3PP Handbook , Peter Blum , November, 1997
e
e a
e
e
For clay-rich marine sediments, the stress-strain behavior is greatly dependent on
the stress history of the sample. The latter can be estimated in a semiquantitative
way by the ratio of measured shear strength to in situ overburden stress, σov:
h = su / σov. (6)
For normally consolidated, fine-grained, cohesive soils, h has a value of about
0.25. Larger values indicate overconsolidaion, smaller values indicate
underconsolidation. Marine sediments are typically overconsolidated in the
uppermost few to several meters and slightly or strongly underconsolidated in th
subjacent 100–200 m and deeper.
9.2. Automated Vane Shear (AVS) System
EQUIPMENT
Vane shear strength, Su, of soft sediment at laboratory conditions is determined
using a motorized miniature vane shear apparatus, following the ASTM D 4648-87
procedure (ASTM, 1987). A four-bladed vane is inserted into the split core and
rotated at a constant rate of 90°/min to determine the torque required to caus
cylindrical surface to be sheared by the vane. The difference in rotational strain
between the top and bottom of a linear spring is measured using digital shaft
encoders. Maximum spring deflection at peak strength is determined by the AVS
program and can easily be verified or adjusted by the user.
Undrained shear strength is
Su = T / K = (∆ / B) / K, (7)
where Su is in pascals (N/m2), T is torque (N·m), K is the vane constant (m3), ∆ is
the maximum torque angle at failure (°), and B is the spring constant that relates
the deflection angle to the torque (°/[Nm]). This simple relationship applies only if
all the terms have been converted to SI units; otherwise, conversion factors must be
used appropriately.
Potential sources of error using the motorized vane shear device are fracturing,
particularly at Su greater than 100–150 kPa, sand- and gravel-sized material (e.g.,
ice-rafted debris in glacial sediments), and surface drying of the core.
The moderately destructive measurements are done in the working half, with th
rotation axis parallel to the bedding plane. Typical sampling rates are one per cor
section until the sediment becomes too firm for instrument penetration.
The motorized vane shear apparatus and springs were purchased from Wykeham
Farrance Engineering, Ltd.
The vanes are usually manufactured by ODP.
9—4 PP Handbook , Peter Blum , November, 1997
.
in
an
ion.
on
DP
CALIBRATION
No routine calibration is performed by the user. However, spring constant B and
vane geometry K are important coefficients that must be verified and measured if
new specimens are purchased or manufactured.
Vane Calibration When a new AVS blade is produced or purchased, the vane blade constant K must
be determined. ODP personnel are responsible for this occasional calibrationK is
a geometrical factor and is calculated as
K = πD2 H/2 (1 + D / 3H) ×10–9, (8)
where D and H are the vane diameter (maximum width of two wings) and height
millimeters and K has the units of cubic meters. The procedure is as follows:
1. Take multiple measurements of vane height and diameter, and enter them in the program utility available at the AVS station.
2. Press “Calibrate” in the calibration utility; the program calculates the mevalue, standard deviation, number of measurements, and vane constant. The new constants are automatically used by the measurement program.
3. Initiate upload of the calibration statistics and vane constant into the ODP database.
Spring Calibration The springs used to measure torque must be calibrated to the angles of rotat
ODP personnel are responsible for this occasional calibration. The spring constant,
B, is defined as
B = ∆/T, (9)
where T is the torque (provided in kg·cm by the manufacturer) and ∆ is the
corresponding deflection angle. ODP personnel enter the data into a calibrati
utility that converts the data to N·m and determines the regression slope that
corresponds to B. The conversion is
T (N·m) = 0.0981 × T (kg·cm). (10)
The calibration procedure is as follows
1. Enter the factory-supplied angle and torque data in the program utility available at the AVS station.
2. Press “Calibrate” in the calibration utility; the program calculates the regression coefficients.
3. Update the spring constant for the measurement program.
4. Initiate upload of the calibration statistics and spring constant into the Odatabase.
In 1995, the following springs and constants were used (they are presumably based
on regression of torque values in kg-1cm-1):
1. 0.0092109,2. 0.018857,3. 0.030852, and4. 0.045146.
9—5PP Handbook , Peter Blum , November, 1997
PERFORMANCE
Precision Repeatability of torque measurement in the exactly same material is estimated to
be better than 5%.
Accuracy This depends on the reference method used (e.g., common triaxial test) and the
material measured (e.g., sand vs. soft clay) and includes uncertainties resulting
from pore pressure developed during the measurement and the lack of confining
pressure. For large vane shear field tests, Lambe and Whitman (1979) estimated
that results are accurate to 20% at best.
MEASUREMENT
The user is guided through the measurements by the AVS program. The position of
the measurement in the core section is entered automatically in the program.
Measured strain is plotted against calculated torque. The principal measurement
steps are
1. Choose and mount the appropriate spring and vane and ensure that the corresponding identifiers are selected in the program.
2. Insert the vane until it is completely immersed in the sediment and start the program. It is crucially important for the relative precision and accuracy of the measurement that the vane is always inserted completely.
3. When the run has terminated, withdraw the vane and clean it.
9—6 PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database Model
Notes: All values in the database should be in SI units (general rule). Vane and spring constants should be converted during the calibration procedure so that conversion factors do not have to be applied in standard queries.
Standard Queries
Table 9—1 AVS database model.
AVS section AVS vane calibration AVS spring calibrationavs_id [PK1] vane_calibration_id [PK1] spring_calibration_id [PK1]
section_id calibration_date_time calibration_date_timerun_num vane_id spring_id
run_date_time vane_constant spring_constant_m1system_id diameter_mean spring_m0spring_calibration_id diameter_sd spring_mse
vane_calibration_id number_of_dia_meas commentsdirection height_mean
rotation_rate height_sd AVS spring calibr. dataraw_data_collected number_of_height_meas spring_calibration_id [PK1] [FK]
comments torque_angle [PK2]
AVS section data pp_torqueavs_id[PK1] [FK]
pp_top_interval [PK2]pp_bottom_intervalmax_torque_angle
residual_torque_angle
AVS raw dataavs_id [PK1] [FK]pp_top_interval [PK2] [FK]
avs_record_number [PK3]torque_angle [PK4]
strain_angle
Table 9—2 AVS query A (results, measurements, and parameters) (to be implemented).
Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idSu Shear strength Su = [AVS Section Data] max_torque_angle
/ [AVS Spring Calibration] spring_constant_m1/ [AVS Vane Calibration] vane_constant
Max. Angle Maximum torque angle (at failure) [AVS Section Data] max_torque_angleRes. Angle Residual torque angle [AVS Section Data] residual_torque_angleRun Run number [AVS Section] run_numberDateTime Date and time of measurement [AVS Section] run_date_timeDirection Direction of measurement (usually x) [AVS Section] directionRaw Data Flags if raw data were saved [AVS Section] raw_data_collectedVane Vane identification [AVS Vane Calibration] vane_idSpring Spring identification [AVS Spring Calibration] spring_id
Table 9—3 AVS query B (raw data) (to be implemented).
Short description Description DatabaseTorque Torque angle [AVS Raw Data] torque_angleStrain Strain angle [AVS Raw Data] strain_angleSample ID ODP standard sample designation Link through [Sample]sample_id
9—7PP Handbook , Peter Blum , November, 1997
9.3. Torvane
EQUIPMENT
The Torvane is a hand-held instrument with attachments calibrated to shear
strength for different ranges (stiffness of sediment; Table on page 8). It is rarely
used because the automated vane shear device available has a larger range, better
precision, and presumably superior accuracy.
Table 9—4 AVS query C (vane calibration) (to be implemented).
Short description Description DatabaseDateTime Calibration date/time [AVS Vane Calibration] calibration_date_timeVane ID Vane identification [AVS Vane Calibration] vane_idVane Const. Vane constant [AVS Vane Calibration] vane_constantDia. mean Diameter, mean of measurements [AVS Vane Calibration] diameter_meanDia. s.d. Diameter, std. dev. of measurements [AVS Vane Calibration] diameter_sdDia. n Diameter, no. of measurements [AVS Vane Calibration] number_of_dia_measHeight mean Height, mean of measurements [AVS Vane Calibration] height_meanHeight s.d. Height, std. dev. of measurements [AVS Vane Calibration] height_sdHeight n Height, no. of measurements [AVS Vane Calibration] height_of_dia_measComments Comments [AVS Vane Calibration] comments
Table 9—5 AVS query D (spring calibration) (to be implemented).
Short description Description DatabaseDateTime Calibration date/time [AVS Spring Calibration] calibration_date_timeSpring ID Spring identification [AVS Spring Calibration] spring_idSpring m1 Spring m1 (spring constant; slope) [AVS Spring Calibration] spring_constant_m1
Spring m0 Spring m0 (intercept) [AVS Spring Calibration] spring _m0
R square Mean squared error (mse) [AVS Spring Calibration] spring _mseComments Comments [AVS Spring Calibration] comments
Table 9—6 AVS query E (spring calibration data) (to be implemented).
Short description Description DatabaseAngle Angle [AVS Spring Calibration] torque_angleTorque Calibration torque at angle [AVS Spring Calibration] pp_torqueDateTime Calibration date/time [AVS Spring Calibration] calibration_date_timeSpring ID Spring identification [AVS Spring Calibration] spring_id
Table 9—7 Specifications of Torvane attachments.
Diameter (mm) Height of vanes (mm) Maximum τƒ (kPa)
19 3 250
25 5 100
48 5 20
9—8 PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database Model
Standard Queries
9.4. Pocket Penetrometer
EQUIPMENT
The penetrometer is a flat-footed, cylindrical probe that is pushed 6.4 mm deep
below the split-core surface. The resulting resistance is the unconfined
compressive strength or 2Su. The mechanical scale is in units of kilograms per
square centimeter, which are converted into units of kilopascals by
2τƒ (kPa) = 98.1 × 2τƒ (kg/cm2). (11)
The maximum τƒ that can be measured with the pocket penetrometer is 220 kPa.
Table 9—8 Database model.
TOR section data TOR sample datator_id [PK1] tor_id [PK1] [FK]
sys_id pp_top_interval [PK2]section_id measurement_no [PK3]
run_date_time pp_bottom_intervaldirection strength_readingcore_temperature comments
rangecomments
Table 9—9 AVS query A (results and more) (to be implemented).
Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idStrength Strength reading (at failure) [TOR Sample Data] strength_readingDateTime Date and time of measurement [TOR Section Data] run_date_timeDirection Direction of measurement (usually x) [TOR Section Data] directionRange Sensitivity range [TOR Section Data] rangeComments Comments [TOR Sample Data] comments
9—9PP Handbook , Peter Blum , November, 1997
DATA SPECIFICATIONS
Database Model
Standard Queries
Table 9—10 Database model.
PEN section data PEN sample datapen_id [PK1] pen_id [PK1] [FK]
sys_id pp_top_interval [PK2]section_id measurement_no [PK3]
run_date_time pp_bottom_intervaldirection strength_readingcore_temperature comments
adapter_usedcomments
Table 9—11 AVS query A (results and more) (to be implemented).
Short description Description DatabaseSample ID ODP standard sample designation Link through [Sample]sample_idDepth User-selected depth type Link through [Sample]sample_idStrength Strength reading (at failure) [PEN Sample Data] strength_readingDateTime Date and time of measurement [PEN Section Data] run_date_timeDirection Direction of meas (usually x) [PEN Section Data] directionAdaptor Adaptor used (sensitivity range) [PEN Section Data] adapter_usedComments Comments [PEN Sample Data] comments
9—10 PP Handbook , Peter Blum , November, 1997
y
e
nce
shore-
nt
REFERENCES
Adams, J.A.S., and Gaspirini, P., 1970.Gamma ray spectrometry of rocks. Meth. Geochem.
Geophys., 10, Elsevier.
Adams, J.A., and Weaver, C.E., 1958. Thorium to uranium ratios as indicators of
sedimentary processes; example of concept of geochemical facies. Bull. Am. Assoc.
Pet. Geol., 42.
ASTM, 1985. Standard method for computing the colors of objects by using the CIE
system. ASTM Publication, E 308.
ASTM, 1985. Standard method for calculation of color differences from instrumentally
measured color coordinates. ASTM Publication, D 2244.
ASTM, 1987a. Standard test method for laboratory miniature vane shear test for saturated
fine-grained clayey soil. Annual Book of ASTM Standards, ?.
ASTM, 1987b. Standard test method for determination of water (moisture) content of soil
by the microwave oven method. Annual Book of ASTM Standards, D 4643-87.
ASTM, 1990. Standard method for laboratory determination of water (moisture) content of
soil and rock. Annual Book of ASTM Standards, D 2216-90 (revision of 2216-63,
2216-80).
ASTM, 1993. Standard test method for determination of thermal conductivity of soil and
soft rock by thermal needle probe procedure. Annual Book of ASTM Standards, Vol.
04.08, Publication D 5334-92.
Balsam, W.L., and Deaton, B.C., 1991. Sediment dispersal in the Atlantic Ocean: evaluation
by visible light spectra. Rev. Aquatic Sci., 4: 411–447.
Balsam, W.L., and Deaton, B.C., 1996. Determining the composition of late Quaternar
marine sediments from NUV, VIS, and NIR diffuse reflectance spectra. Marine
Geology, 134, 31-55.
Balsam, W.L., and Otto-Bliesner, B., 1995. Modern and last glacial maximum eolian
sedimentation patterns in the Atlantic Ocean interpreted from sediment iron oxid
content. Paleoceanography, 10: 493–507.
Balsam, W.L., and Wolhart, R., 1993. Sediment dispersal in the Argentine Basin: evide
from visible light spectra. Deep-Sea Res., 40: 1001–1031.
Balsam, W.L., Damuth, J.E., and Schneider, R.R., 1997. Comparison of shipboard vs.
based spectral data from Amazon-Fan cores: implications for interpreting sedime
composition. In Flood, R.D., Piper, D.J.W., Klaus, A., and Peterson, L.C. (Eds.), Proc.
ODP, Sci. Results, 155: College Station, TX (Ocean Drilling Program), ?-?.
References—1PP Handbook , Peter Blum , November, 1997
rrill,
th
y
the
rmal
um-
Belknap, W.B., Dewan, J.T., Kirkpatrick, C.V., Mott, W.E., Pearson, A.J., and Rabson,
W.R., 1959. API calibration facility for nuclear logs. Drill. and Prod. Prac., ?
Blackwell, J.H., 1954. A transient-flow method for determination of thermal constants of
insulating materials in bulk, Part I—theory. J. Appl. Phys., 25: 137–144.
Blum, P., Allan, J., Coyne, J., Hagelberg, T., MacLeod, C. Mato, C., deMenocal, P., Me
R., Mithal, R., Rhinehart, B., Weaver, P., Wilkens, R., and Coarser, G., 1995. Dep
data acquisition, processing and archiving in the Ocean Drilling Program. Results and
recommendations from the ODP/TAMU Depth Workshop.
Blum, P., Rabaute, A., Gaudon, P., andAllan. J.F., 1997. Analysis of natural gamma ra
spectra obtained from sediment cores with the shipboard scintillation detector of
Ocean Drilling Program: Example form Leg 156. In Shipley, T.H., Ogawa, Y., Blum,
P. and Bahr, J.M. (Eds.), Proc. ODP, Sci. Results, 156: College Station, TX (Ocean
Drilling Program), ?-?.
Burns, R.G., 1970. Mineralogical Applications of Crystal Field Theory: Cambridge
(Cambridge University Press).
Carslaw, H.S., and Jaeger, J.C., 1959. Conduction of Heat in Solids: Oxford (Oxford
University Press).
Commission Internationale d'Eclairage (CIE), 1986. CIE Colorimetry (2nd ed.), Publication
15.2.
CGPM, 1960. 11th Conférence Générale des Poids et Mesure.
CGPM, 1971. 14th Conférence Générale des Poids et Mesure.
Deaton, B.C., and Balsam, W.L., 1991. Visible spectroscopy—a rapid method for
determining hematite and goethite concentration in geological materials. J. Sediment.
Petr., 61: 628–632.
De Vries, D.A., and Peck, A.J., 1958. On the cylindrical probe method of measuring the
conductivity with special reference to soils. Australian J. Phys., 11: 255–271.
DIN, 1980. Farbmessung; Farbmasszahlen. Publication, 5033: part 3.
Erbas, K., 1985. Bestimmung der Wärmeleitfähigkeit von Festkörpern mit einer Halbra
Linienquellen-Apparatur [MS? thesis]. Institute fur Angewandte Geophysik,
Technische Universität Berlin.
Evans, 1965. GRAPE—A device for continuous determination of material density and
porosity. SPWLA, 6th Ann. Symposium, 2: 25.
Evans, H.B., and Lucia, J.A., 1970. Natural gamma radiation scanner. In Peterson, M.N.A.,
Edgar, N.T., et al., Init. Repts. DSDP, 2: Washington (U.S. Govt. Printing Office),
458–460.
References—2 PP Handbook , Peter Blum , November, 1997
mal
ehus
rnal
arine
f
I.
Gaffey, S.J., 1986. Spectral reflectance of carbonate minerals in the visible and near-
infrared (0.35–2.55 microns): calcite, aragonite, and dolomite. American
Mineralogist, 71: 151–162.
Harms, J.C. and Choquette, P.W., 1965. Geologic evaluation of a gamma-ray porosity
device. 6th Annual SPWLA Logging Symp., Dallas, Texas, C1-C37
Holtz, R.D., and Kovacs, W.D., 1981. Geotechnical engineering: where? (Prentice Hall).
ISO, 1984. Paints and varnishes—Colorimetry—Part 1: principles. Publication, 7724/1.
Jumikis, A.R., 1966. Thermal Soil Mechanics: New Brunswick, NJ (Rutgers University
Press).
Kristiansen, J.I., 1982. The transient cylindrical probe method for determination of ther
parameters of earth materials [Ph.D. diss.]. Geoskrifter, 18, Dept. of Geology, Aa
University.
Lambe, T.W., and Whitman, R.V., 1979. Soil Mechanics (SI version): New York (John
Wiley).
Lyman, J., and Fleming, R.H., 1940. Composition of seawater. J. Marine Res., 3: 134–146.
Mesri, G., 1975. New design procedure for stability of soft clays. Discussion. ASCE Jou
of the Geotech. Eng. Div., 101, 409-412.
Mesry, G., 1989. A reevaluation of su(mob) = 0.22σ’p using laboratory shear tests. Can.
Geotch. J., 26, 162-164.
Millero, F.J., and Poisson, A., 1981. Internationalone-atmosphere equation of state of
seawater. Deep-Sea Research, 28A: 625–629.
Millero, F.J., Chen, C.-T., Bradshaw, A., and Schleicher, K., 1980. Deep-Sea Research,
27A: 255–264.
Nagao, S., and Nakashima, S., 1991. A convenient method of color measurement of m
sediments by colorimetry. Geochemical Journal, 25: 187–197.
Nagao, S., and Nakashima, S., 1992. The factors controlling vertical color variations o
North Atlantic Madeira Abyssal Plain sediments. Marine Geology, 109: 83–94.
Nakashima, S., Miyagi, I., Nakata, E., Sasaki, H., Nittono, S., Hirano, T., Sato, T., and
Hayashi, H., 1992. Color measurement of some natural and synthetic materials—
Rep. Res. Inst. Natural Resources, Mining College, Akita Univ., 57: 57–76.
Serra, O., 1984. Fundamentals of Well Log Interpretation: Amsterdam (Elsevier).
Sverdrup, H.U., Johnson, M.W., and Fleming, R.H., 1942. The Oceans: Their Physics,
Chemistry, and General Biology: where? (Prentice Hall, Inc.).
References—3PP Handbook , Peter Blum , November, 1997
f
Thompson, R., and Oldfield, F., 1986. Environmental Magnetism: where? (Allen and
Unwin).
Tittman, J, and Wahl, J.S., 1965. The physical foundations of formation density logging
(gamma-gamma). Geophysics, 30: 284–294.
Vacquier, V., 1985. The measurement of thermal conductivity of solids with a transient
linear heat source on the plane surface of a poorly conducting body. Earth and Plan.
Sci. Letters, 74: 275–279.
Von Herzen, R., and Maxwell, E.A. (1959). The measurement of thermal conductivity o
deep sea sediments by a needle probe method. J. Geophys. Res., 64: 1557–1563.
Weast, R.C., Astle, M.J., and Beyer, W.H., 1985. CRC Handbook of Chemistry and Physics:
Boca Raton, FL (CRC Press).
References—4 PP Handbook , Peter Blum , November, 1997
e SI
ut for
the
eter,
e
in
ies.
n
A-1
APPENDIX I. PHYSICAL UNITS
Unit systems
A coherent system of physical units is based on a certain set of base units (e.g.,
meter, kilogram, second) that are well defined in terms of actual physical
phenomena (e.g., length, mass, and time, respectively). Derived units in a coherent
system are formed as products of powers of base units without introducing
numerical factors. Their algebraic expressions in terms of the base units can be
replaced by special names and their symbols (e.g., 1 N = 1 mkgs–2). Derived units
can themselves be used to form other derived units and their symbols (e.g., 1 Pa =
1 Nm–2). Values of dimensionless quantities are expressed by pure numbers. The
corresponding unit is the ratio of a unit to itself, or the dimensionless unit of the
coherent system, and may be expressed by the number 1 (Weast et al., 1985).
There are two commonly known coherent systems of units: the Système
International d’Unités (SI) and the centimeter-gram-second (CGS) system. Th
is the only internationally recommended system and should be used througho
ODP physical measurements and analyses.
The obsolescent “electrostatic CGS” and “electromagnetic CGS” units cannot
strictly be compared to the corresponding units of the SI. This is because the
electromagnetic CGS system is a three-dimensional system of units in which
electric and magnetic quantities are considered to be derived from the centim
gram, and second as base units, whereas the SI has four dimensions for thes
quantities (meter, kilogram, second, and ampere). The complexities involved
such conversions are well known to those working with rock magnetic propert
SI UNITS
The SI name was adopted by the Conference des Poids et Mesure (CGPM) i
1960. It is based on the seven base units (CGPM 1960, 1971) listed in Table
(see Weast et al., 1985, for definitions).
Table Appendix—1SI base units.
Base quantity Name Symbol
Length meter m
Mass kilogram kg
Time second s
Electric current ampere A
Thermodynamic temperature kelvin K
Amount of substance mole mol
Luminous intensity candela cd
Appendix—1PP Handbook , Peter Blum , November, 1997
I
nits
s of
ity,
the
The base unit of mass is the only one with a name that, for historical reasons,
contains a prefix. Several subsystems of the SI are used in different fields of
science (e.g., the meter-kilogram-second [MKS] system in mechanics).
Derived SI units are listed in Table A-2.
The radian and steradian actually belong to a third class of “supplementary S
units” for which the CGPM (1960) declined to state whether they were base u
or derived units.
In addition to the set of formal derived units listed in Table A-2, there are score
additional SI derived units and unit symbols for other quantities (volume, dens
velocity, magnetic field strength, etc.). These are either trivial or defined within
Table Appendix—2Derived SI units.
Quantity Name Symbol Base unit Other SI
Plane angle radian rad m m-1
Solid angle steradian sr m2 m-2
Frequency hertz Hz s-1
Force newton N m kg s-2 J/m
Pressure,stress
pascal Pa m-1 kg s-2 N/m2
Energy,work,quantity of heat
joule J m2 kg s-2 N m
Power, radiant flux
watt W m2 kg s-2 J/s
Quantity of electricity,electric charge
coulomb C s A A s
Electric potential, potential difference, electromotive force
volt V m2 kg s-3 A-1 W/A
Capacitance farad F m-2 kg-1 s4 A2 C/V
Electric resistance ohm W m2 kg s-3 A-2 V/A
Conductance siemens S m-2 kg-1 s3 A2 A/V
Magnetic flux weber Wb m2 kg s-2 A-1 V s
Magnetic flux density tesla T kg s-2 A-1 Wb/m2
Inductance henry H m2 kg s-2 A-2 Wb/A
Luminous flux lumen lm cd sr
Illuminance lux lx m-2 cd sr
Activity becquerel Bq s-1
Absorbed dose gray Gy m2 s-2 J/kg
Appendix—2 PP Handbook , Peter Blum , November, 1997
appropriate context. Other units exactly defined in terms of SI units, but not part of
the SI, are listed in Table A-3.
Similarly, the use of certain decimal fractions and multiples of SI units, including
those listed in Table A-4, is considered appropriate.
OBSOLESCENT UNITS
Table A-5 lists a selection of units used in the ODP and which are to be abandoned.
The conversion to SI units is also listed.
Table Appendix—3Units exactly defined in terms of SI units.
Quantity Name Symbol Base unit
Time minute min 60 s
hour h 3,600 s
day d 86,400 s
Angle degree ° (π/180) rad
minute ’ (π/10,800) rad
second ’’ (π/648,000) rad
Temperature degree Celsius °C = T(K) - 273.15 K
Table Appendix—4 Accepted decimal multiples and fractions of SI units.
Quantity Name Symbol SI base unit
Length ångström A 10-10 m
Cross section barn b 10-28 m2
Volume liter lL 10-3 m3
Mass tonne t 103 kg
Pressure bar bar 105 Pa
Table Appendix—5Obsolescent units and their conversion to SI units.
Quantity Name Symbol SI base unit
Length inch in 2.54 x 10-2 m
foot ft 0.3048 m
mile mi 1609 m
Mass pound lb 0.453592 kg
short ton - 907.2 kg
Force kilogram-force kgf 9.80665 N
pound lb 4.448 N
kilo-pound kip 4.448 kN
Pressure kg/m2 kg/m2 9.8067 Pa
dyne/cm2 dyne/cm2 0.1 Pa
atmosphere atm 1.0133 x 105 Pa
mm Hg (0°C) mm Hg 1.3332 x 102 Pa
Appendix—3PP Handbook , Peter Blum , November, 1997
pounds per square inch psi 6.8948 x 103 Pa
pounds per square foot psf 47.880 Pa
tons per square foot tsf 9.5761 x 104 Pa
Magnetic flux density gauss G 10-4 T = 10-4 kgs-2A-1
electromag. units/cm3 emu/cm3 1.257 10-3 T = 10-3 Am2
Magnetic force oersted oe equivalent to 10-4 T
Table Appendix—5Obsolescent units and their conversion to SI units.
Appendix—4 PP Handbook , Peter Blum , November, 1997