PACIFIC deliverable 1€¦ · gold, porphyry copper, nonconformity uranium, and Mississippi Valley-type (MVT) deposits also can ... Nafe-Drake curve (grey) for common rocks at a standard

PACIFIC

Passive seismic techniques for environmentally friendly and cost efficient mineral exploration

D1.3– Report comparing best practice in

active and passive exploration methods

Grant agreement number 776622 Due date of Deliverable 30/11/18

Start date of the project 01/06/2018 Actual submission date 13/12/18

Duration 36 months Lead Beneficiary DIAS

Description

Comparison of active and passive seismic methods for mineral exploration; definition of best practice to be applied during passive seismic exploration.

Dissemination Level

PU Public x

CO Confidential, only for members of the consortium (including the Commission Services)

D1.3– REPORT COMPARING PRACTICE IN ACTIVE AND PASSIVE EXPLORATION METHODS

Public © PACIFIC consortium Page 2 / 19

Table of content

List of figures ......................................................................................................................................... 3

List of tables........................................................................................................................................... 4

Executive Summary ............................................................................................................................... 5

1 Introduction ................................................................................................................................... 6

2 Active vs. passive seismic methods ............................................................................................... 8

2.1 Use of the active seismic method in mineral exploration ..................................................... 8

2.2 Use of the passive seismic method in mineral exploration................................................... 9

2.3 General comparison of active and passive methods........................................................... 11

3 Conclusion ................................................................................................................................... 13

Bibliography ......................................................................................................................................... 14

Annexes ............................................................................................................................................... 16

A.1 Ambient noise surface-waves studies processing workflow .................................................... 16

A.2 Ambient noise body-waves studies processing workflow ........................................................ 18



List of figures

Figure 1. Illustration of active versus passive and inline versus offline waves adopted from http://www.parkseismic.com/. Blue arrows show the active surface-waves, red arrows show the passive waves, and black lines/dashed lines show body-waves. Indexes “i” and “o” define the direction of the waves corresponding to the direction of array (“i” means in line and “o” means offline). .................................................................................................................................................. 6

Figure 2. Sketch of cross-correlations between the station-pair in ambient noise method adopted from Weaver (2011). ............................................................................................................................. 7

Figure 3. Nafe-Drake curve (grey) for common rocks at a standard confining pressure of 200 MPa (Z) adopted from Salisbury and Snyder (2007). Also shown are values for ore minerals such as pyrite (Py), pentlandite (Pn), pyrrhotite (Po), chalcopyrite (Ccp), sphalerite (Sp), hematite (Hem), magnetite (Mgt), galena (Gn), and fields for host rock-ore mixtures. ................................................................... 8

Figure 4. Schematic representation of the data processing scheme for passive surface-wave tomography adopted from Bensen et al. (2007). Phase 1 shows the steps involved in preparing single-station data prior to cross-correlation. Phase 2 outlines the cross-correlation procedure and stacking, Phase 3 includes dispersion measurement and Phase 4 is the error analysis and data selection process. ............................................................................................................................................................. 10

Figure 5. Schematic representation of the data processing steps for extracting body-waves from noise data. ..................................................................................................................................................... 11



List of tables

Table 1. Comparing advantages and disadvantages of active seismic methods (mainly the reflection method) with passive seismic methods (surface-wave noise tomography and body-wave reflection retrieval). ............................................................................................................................................. 12



Executive Summary

Seismic methods provide high-resolution images of geologic structures hosting mineral deposits and,

in a few cases, can be used for direct targeting of deposits. Active reflection techniques have been

successfully used in the minerals sphere, especially for structural control on deep targets. Although

useful, a disadvantage of this methodology is that it is expensive and logistically difficult in locations

without easy access for source generation. In contrast to active seismology, passive methods exploit

ambient seismic noise and do not require specific seismic sources.

In this report, we compare active and passive seismic methods in general and discuss different data

processing sequences that have been used in previous passive seismic studies. The quality of the

results in passive seismic methods strongly depends on (1) the spatial-temporal properties of the

noise source distribution and (2) the number and disposition of seismic receiver pairs on which the

noise correlation is performed.

We then discuss how to apply these processing sequences to extract body-waves in the PACIFIC

project, with a view to developing reflection seismic images analogous to active reflection seismic

work.



1 Introduction

Across the globe, the mineral industry is seeking new technologies to replace or complement old

geophysical methods in order to improve exploration efficiency at depth. Seismic methods provide

high-resolution images of geologic structures hosting mineral deposits and, in a few cases, can be

used for direct targeting of mineral deposits (Malehmir et al, 2012; Salisbury and Snyder 2007; Figure

1). While active seismic reflection techniques can be used to explore for ore deposits at depth, they

are expensive, and the methodology can be both dangerous and environmentally destructive (since

explosives are often used to achieve an adequate signal-to-noise (S/N) ratio). This brings new

opportunities and a motivation for geophysicists to develop and introduce new, more efficient high-

resolution passive seismic methods.

Figure 1. Illustration of active versus passive and inline versus offline waves adopted from http://www.parkseismic.com/. Blue arrows show the active surface-waves, red arrows show the passive waves, and black lines/dashed lines show body-waves. Indexes “i” and “o” define the direction of the waves corresponding to the direction of array (“i” means in line and “o” means offline).

Over the last decade, seismic tomography based on interstation correlations of ambient noise has

developed into a standard tool for exploring and monitoring the Earth’s interior (Fichtner, 2015).

Ambient noise tomography is an example of wave-field interferometric imaging in which the goal is

to produce subsurface structural images by recording the ambient noise field of the Earth using

surface arrays of seismometers or geophones (Figure 1; Artman, 2006). In general this method is

based on the extraction of the surface-wave contribution to the seismic field from the cross-

correlation of seismic noise between the station pairs (Figure 1; Roux, 2009). Another application of

active seismic interferometry is to extract body-waves and retrieve the earth’s reflection response

from cross-correlations of seismic noise recordings (Draganov et al. 2007, 2009). Compared to

surface-wave extraction, body-wave extraction and reflection retrieval is a much greater challenge

because ambient noise is typically dominated by surface-wave energy (Nikata et al. 2015; Draganov

et al. 2009, 2007). The images produced with this technique are directly analogous to those produced

with conventional reflection seismic data (Artman, 2006).

http://www.parkseismic.com/



Figure 2. Sketch of cross-correlations between the station-pair in ambient noise method adopted from Weaver (2011).

In this report, we summarize the general issues and successful usage of active and passive methods

for imaging structures in the Earth interior and especially those used in mineral exploration. We then

discuss the state of data processing in passive seismic methods as it has developed since the first

publication of papers on the use of ambient noise to obtain surface-wave dispersion measurements.

In the last section of this report, we review the workflows that have been used in previous active and

passive seismic studies and discuss how to apply them in the PACIFIC project.



2 Active vs. passive seismic methods

2.1 Use of the active seismic method in mineral exploration

Salisbury and Snyder (2007) reported successful usage of 2-D and 3-D reflection seismic surveys for

the detection of (1) large massive sulphide (VMS) deposits, (2) massive sedimentary exhalative

(SEDEX) and (3) iron oxide copper gold (IOCG) deposits. However, other types of deposits like lode

gold, porphyry copper, nonconformity uranium, and Mississippi Valley-type (MVT) deposits also can

be detected.

In principle, most of the ores in all these deposits display higher acoustic impedances than their

common host rocks, in large part because of their high densities. They therefore plot to the right of

the Nafe-Drake curve (Figure 3), and it should be possible to detect them using high-resolution

reflection techniques if the deposits meet certain size and geometry requirements.

When the ore is in altered rock, as is commonly the case for hydrothermal ore deposits, then the target material may have lower density and lower seismic velocities due to the presence of hydrous or carbonate minerals. In some cases, the lower acoustic impedance may provide sufficient contrast to identify the ore zones.

Figure 3. Nafe-Drake curve (grey) for common rocks at a standard confining pressure of 200 MPa (Z) adopted from Salisbury and Snyder (2007). Also shown are values for ore minerals such as pyrite (Py), pentlandite (Pn), pyrrhotite (Po), chalcopyrite (Ccp), sphalerite (Sp), hematite (Hem), magnetite (Mgt), galena (Gn), and fields for host rock-ore mixtures.

Although the reflection method is mostly used to the direct detection of ore, there are reports on

successful usage of the method in detection of the structure that controls the ore (Ashton et al, 2018).

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2.2 Use of the passive seismic method in mineral exploration

Passive seismic methods exploiting ambient noise is a relatively new technique that uses continuously

recorded seismic noise propagating through the Earth to map variations in crustal seismic velocity

associated with structural and thermal contrasts (Saygin et al. 2013). The method of cross-correlation

of seismic ambient noise has recently emerged in seismology as an alternative technique to imaging

carried out with traditional sources, e.g. earthquakes, explosions, etc (Saygin et al. 2013).

There are two types of passive seismic methods: surface-wave tomography and reflection imaging

using extracted body-wave data. Passive surface-wave tomography has been used for regional

tomographic imaging (Kang and Shin 2006; Liang and Langston 2007; Saygin et al. 2013; Roux 2009),

to provide information on near-surface geological structures at a local scale (Picozzi et al. 2008;

Nagaoka et al. 2012), and for exploration and evaluation of mineral deposits and hydrocarbon fields

(Hollis et al. 2018; Saenger et al. 2009) at smaller scales (from a few km and to less than 1 km depth).

Cross-correlation of noise recordings can be also used to infer the impulse response between

receivers. Compared to surface-wave extraction, body-wave extraction is a much greater challenge

because ambient noise is typically dominated by surface-wave energy and because reflection

amplitudes decay more rapidly with distance. As a result, the demands on the distribution of the

ambient-noise sources are more severe (Nakata et al. 2015; Draganov et al. 2009). However, recent

seismic studies have reported successful body-wave extraction and used them to retrieve reflection

images (Nikata et al. 2015; Ryberg 2011; Draganov et al. 2009, 2007; Roux et al. 2005).

2.2.1 Passive seismic tomography using surface-wave extraction

Different studies (e.g. Bensen et al. 2007) suggested different workflows for the processing of

ambient noise data and extraction of surface-waves from cross correlation. In its current state, the

procedure comprises four principal phases that are applied roughly in order: (1) single station data

preparation, (2) cross-correlation and temporal stacking, (3) measurement of dispersion curves and

(4) quality control, including error analysis and selection of the acceptable measurements (Figure 4).

Based on the data properties and the purpose of the survey, other studies suggested other processing

sequences for surface-wave noise tomography with similar major steps as in Figure 4 but with more

detail and the addition some minor steps for a better resolution (refer to section A.1 in Annexes of

this report for more detail).



Figure 4. Schematic representation of the data processing scheme for passive surface-wave tomography adopted from Bensen et al. (2007). Phase 1 shows the steps involved in preparing single-station data prior to cross-correlation. Phase 2 outlines the cross-correlation procedure and stacking, Phase 3 includes dispersion measurement and Phase 4 is the error analysis and data selection process.

2.2.2 Passive seismic reflection using body-wave extraction

In reflection retrieval methods, body-wave data needs to be extracted in a pre-processing step.

Unfortunately body-waves derived from ambient noise have a systematic problem. The potential

non‐ideal distribution of the sources of the most prominent phase, i.e. surface-waves, causes specific

artefacts that travel at higher apparent velocities, arriving earlier than the predicted arrival time of

the surface-wave (Nakata et al. 2015). These artefacts cover a time window when the direct/

refracted body-wave is also expected. Fortunately, the main frequency content of the surface-waves

is much lower than that of the body-waves and can therefore be suppressed by simple band passing.

On the other hand, compared to surface-wave extraction, body-wave extraction is a much greater

challenge because ambient noise is typically dominated by surface-wave energy (Nakata et al. 2015).

Nonetheless, retrieving reflection noise data allows extraction of velocity information and

construction of depth images with higher resolution than for surface-wave tomography (Draganov et

al. 2009) and the images produced with this technique are comparable to those produced using active

seismic reflection methods. Nikata et al. (2015), Ryberg (2011), Draganov et al. (2009, 2007) and Roux

et al. (2005) suggested alternative procedures to extract body-waves from noise data but in general

the processing is similar to those for extraction of surface-waves except for some additional steps

(Figure 5; refer to section A.2 in Annexes of this report for more detail).



Figure 5. Schematic representation of the data processing steps for extracting body-waves from noise data.

2.3 General comparison of active and passive methods

The main differences between active and passive seismic methods are 1) the use of active sources in the former and their absence in the latter, 2) the time taken to acquire data, and 3) environmental impact and cost. Planning and designing the survey, data acquisition, quality of the results and difficulty in result interpretation are additional differences. Table 1 summarizes the comparison of advantages and disadvantages of active versus passive seismic method.

In passive methods, the processing steps strongly depend on the data and the target(s) of the project and a unique processing sequence does not exist. In active data, whilst the details of acquisition are also driven by the target, the basic acquisition geometry and processing steps are very mature - including the availability of industry standard software packages.

2.3.1 Best passive seismic practice for the PACIFIC project

Since a unique procedure does not exist for the passive seismic method and the processing steps strongly depend on the nature of the data, we will apply two different strategies in the PACIFIC project to find the best practice. First, we will build a realistic synthetic model and try to solve the problem and find the structures within that model by extracting the body-waves and retrieving the reflection image by applying the different procedures suggested in this report. Then, we will extract part of the real data acquired at the Marathon site and apply the steps suggested by different studies summarized in this report to see which one yields the best outcome.



Table 1. Comparing advantages and disadvantages of active seismic methods (mainly the reflection method) with passive seismic methods (surface-wave noise tomography and body-wave reflection retrieval).

Seismic Methods Comparison

Active seismic reflection

Passive surface-

wave noise

tomography

Passive body-wave

reflection

Typical Targets

Horizontal to shallow-

dipping units with density

contrasts; laterally

restricted targets such as

cavities or tunnels at

depths

Structures with

lateral and vertical

velocity contrasts

Horizontal to dipping

units with density and

velocity contrasts

Disadvantage

- Need for an active source

- High expense - Can potentially be

dangerous - Environmentally

destructive - Lots of pre-processing - Low signal to noise - Sometimes difficult to

interpret - Special array design

- Depends on noise source properties

- There are no general rules for processing

- Lower resolution

- Depends on noise source properties

- Data dominated by surface-wave energy

- There are no general rules for extracting the body waves

- Sometimes difficult to interpret

- Special array design

Advantage

- Can directly target the ore in favourable circumstances

- Clear protocols and industry-standard software tools are available

- Higher resolution - Can give high resolution

structural information

- Lower cost - Absence of an

active source - Can target the host

rock with velocity variation

- Lower environmental impact

- Lower cost - Absence of an active

source - Industry standard tools

can be used - Similar procedure to

active reflection methods

- Possible high resolution, if high frequencies exist



3 Conclusion

High-resolution active and passive seismic techniques show the greatest potential for exploration of

deep ore deposits. Successful application of both techniques for imaging crustal and especially near-

surface structures has been reported. Active seismic methods have been used for almost a decade in

mineral exploration whereas the passive seismic method (passive seismic tomography) has only

recently been applied in mineral deposit and hydrocarbon reservoir exploration (Hollis et al. 2018;

Mordret et al. 2013; Saygin et al. 2013; Saenger et al. 2009).

Advantages of the active seismic method are high resolution, identification of reflectors, processing

using industry-standard procedures; disadvantages are its high cost and significant environmental

impact. The absence of an active source in the passive seismic method yields lower cost and

environmental impact but traditional surface wave tomography has relatively low resolution (see

Table 1).

Although there are some similarities in the processing steps in active and passive seismic, the nature

of the data controls the main processing steps in passive methods.

Best practice in the PACIFIC project will be determined by applying different processing steps on both

the synthetic and pilot Marathon dataset and comparing the outcomes.



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Mordret, A., M. Landes, N. M. Shapiro, S. C. Singh, P. Roux, O. I. Barkved. 2013. Near-surface study at the Valhall

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Nagaoka, Y., K. Nishida, Y. Aoki, M. Takeo, T. Ohminato. 2012. Seismic imaging of magma chamber beneath an

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Nakata, N., J. P. Chang, J. F. Lawrence, and P. Boué. 2015. Body wave extraction and tomography at Long Beach,

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Annexes

A.1 Ambient noise surface-waves studies processing workflow

Surface-wave noise tomography is based on the extraction of the surface-wave contribution to the

seismic field from the cross-correlation of seismic noise between station pairs. The quality of the

results strongly depends on: (1) the spatial-temporal properties of the noise source distribution and

(2) the number of seismic receiver pairs on which the noise correlation is performed (Roux 2009).

A.1.1. Singer et al. (2017):

Processing sequence suggested by Singer et al. (2017) for surface-waves ambient noise method

includes 9 steps as follows:

1. Down-sample the record to 5 Hz.

2. Remove possible high-frequency spikes.

3. Deconvolve the instrument response and subdivide them into 2 h long segments.

4. Removal of the mean and long-term trend.

5. Band pass filtering between 0.01 and 2 Hz.

6. Running absolute mean normalization in the time domain in frequency band between 0.02 and

0.67 Hz.

7. Spectral whitening in the frequency domain.

8. Cross correlate 2 h long segments between all station pairs.

9. Stack the CC to a single time series per station pair.

A.1.2. Fichtner et al. (2017)

Based on Fichtner et al, (2017) typical processing includes:

(1) Averaging of causal and acausal correlation branches.

(2) Spectral whitening.

(3) Time-domain running averages.

(4) Frequency-domain normalization.

(5) 1-bit normalization.

(6) Phase-weighted stacking.

(7) Directional balancing.

(8) And various selection and suppression filters.



A.1.3. Mordret et al. (2013):

Mordret et al. (2013) proposed a pre-processing of the data and computing cross-correlation (CC) as

follows:

(1) Organize the data in 1-min duration segments.

(2) Remove the mean and the trend of the signal.

(3) Whitening of the amplitude spectrum between 0.4 and 30 Hz.

(4) One-bit normalization of the traces.

(5) Because all sensors were identical they did not remove the instrumental response.

(6) Finally, they correlate all sections for every interstation and intercomponent combination and

stacked the resulting correlations for each combination.

(7) They computed intercomponent combinations (ZZ, ZE, ZN, EZ, EE, EN, NZ, NE and NN, with Z-

vertical, N-north and E-east components).

A.1.4. Roux (2009)

Roux (2009) suggested the following processing steps:

(1) Noise pre-processing consists of eliminating high-amplitude seismic events by truncating the

recording amplitude at three times the standard deviation of the seismic noise. Then, equalization is

performed to whiten the noise spectrum in the chosen frequency interval.

(2) Frequency-incoherent beamforming using the N stations of the network to determine the average

velocity and the direction of the seismic noise.

(3) The nine-component noise-correlation tensor CAB (t) for each station pairs.

(4) Optimal Rotation Algorithm (ORA) to retrieve the surface-wave Green’s tensor (with both Rayleigh

and Love waves).

(5) Using the optimal noise-correlation tensor for surface-wave tomography inversion.

A.1.5. Bensen et al. (2008):

Based on what Bensen et al. (2008) suggested the following procedure:

(1) Data preparation.

(2) Removing instrument response correction for day-long time series.

(3) Performing time domain normalization.

(4) Apply temporal normalization weights between periods of 15 and 50 s.

(5) Additional spectral whitening is performed on all of the waveforms for each day.

(6) Performing cross-correlation on day-long time series for vertical-vertical, east-east, east-north,

north-east, and north-north components.



(7) Estimating the frequency dependent group and phase velocities from the Rayleigh and Love wave.

(8) Apply a minimum 3 wavelengths interstation distance constraint.

(9) Apply a selection criterion based on the period-dependent signal-to-noise ratio (SNR).

A.2 Ambient noise body-waves studies processing workflow

Reflection image retrieval based on the extraction of the body-waves from noise is more challenging than surface-waves suggested flows included.

A.2.1. Nakata et al. (2015)

Processing sequence suggested by Nakata et al. (2015) for body-waves extraction is as follow:

(1) Downsample the data to reduce the computational cost and focus on body-waves up to 15 Hz.

(2) Then apply seismic interferometry by computing power-normalized cross correlation (cross coherence) between receivers A and B in the frequency domain.

(3) Daily correlation functions.

(4) To isolate body-waves and mute surface-waves, apply a time window with Gaussian-shape tapers to each daily correlation and stacked correlation to suppress signals.

(5) Then compute a second cross correlation between each daily correlation and the corresponding trace in the bin of the appropriate distance.

(6) Using a noise suppression filter to further improve the SNR of body-waves. This step involves an adaptive covariance filter (ACF), which is designed for ambient-noise analysis.

A.2.2. Reberg (2011)

Reberg (2011) suggested the following procedure for extracting the body-waves from noise data:

(1) Split the data into one hour slices.

(2) Excluding those time windows with shots and earthquakes.

(3) One‐bit normalize the data.

(4) Cross‐correlations of station pairs in the frequency domain and subsequently stacked.

The correlation function, defined for positive and negative correlation times, represents seismic waves traveling from station 1 to station 2 and vice versa. When cross correlating one station against all other stations, positive and negative correlation times correspond to pseudo shot and pseudo receiver gathers, respectively.

(6) Applying a Normal‐Move-Out (NMO) correction to the data.

The NMO correction removes (“flattens”) the hyperbolic shape of the travel-time of a seismic reflection caused by a horizontal reflector.

A.2.3. Draganov et al. (2009)

(1) Obtain poststack time-migrated images of the subsurface by following a standard processing scheme consisting of statics correction.



(2) Common-midpoint sorting.

(3) Interactive velocity analysis.

(4) Normal-Move-Out (NMO) correction.

(5) Stacking.

(6) Phase-shift time migration.

A.2.4. Draganov et al. (2007)

In order to retrieve the response that would be recorded by the passive array of geophones, Draganov et al. (2009) used the following procedure:

(1) Energy-normalized each noise panel separately.

(2) Extracted the first trace from each noise panel and correlated this trace with all the other traces in the same panel.

(3) The summation result was then band-pass filtered between 2 and 10 Hz to obtain a so-called common-source gather with the retrieved source position corresponding to the location of the first geophone.

(4) The above procedure was repeated in such a way that they retrieved source positions at all the geophone positions of the passive array.

To improve the clarity of reflection arrivals in data sets, they performed the following additional processing steps:

(5) Resorted the traces in the retrieved common-source gathers into common-offset panels.

(6) The traces in each common-offset panel were summed and normalized for the number of summed traces, producing a single output trace per common-offset panel.

(7) The output traces from the different common-offset panels were sorted into a so-called common-offset stack panel.

(8) The resulting common-offset stack panel was further filtered in the frequency-wave number domain (f-k filtering) to eliminate the surface-waves and then band-pass filtered between 13 and 33 Hz.

A.2.5. Roux et al. (2005)

The processing performed on 1-day noise recording on one seismometer consists of:

(1) Eliminating high-amplitude events by truncating the recording amplitudes at three times the standard deviation of the ambient noise signal.

(2) Equalization of the noise spectrum in the frequency intervals.

(3) Applying plane wave beamforming.

(4) Select station pairs whose relative locations lie along lines having certain azimuths.

(5) Applying cross-correlation function.

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