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CLASSIFICATION OF MULTIBEAM SNIPPETS DATA USING
STATISTICAL ANALYSIS METHOD
LAU KUM WENG
UNIVERSITITEKNOLOGI MALAYSIA
CLASSIFICATION OF MULTIBEAM SNIPPETS DATA USING
STATISTICAL ANALYSIS METHOD
LAU KUM WENG
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Master of Science (Hydrography)
Faculty of Geoinformation and Real Estate
Universiti Teknologi Malaysia
NOVEMBER 2012
iii
I humbly dedicate this thesis to
My Grandmother
Wong Ksuin
My Beloved Parent
Lau Mun Hong & Hui Mew Ying
My Family Members
Lau Sook Fun
Lau Sook Yen
Lau Kum Fai
My Loved One
Lint LiXin
Buddies & Friends
Thanks for your support and encouragement.
Thanks for the blessing and best wishes.
Thanks for the sacrifice and patience.
THANK YOU SO MUCH
iv
ACKNOWLEDGEMENT
William Shakespeare said, “Life is but a stage, we pass through life just one
and nobody get a second chance.” I think it is necessary to appreciate the people who
make our lives easier and meaningful everyday. Honestly, the people who played a
central role through our lives have directly shaped our work and lives. They will
make a significant difference to our future.
Firstly, I wish to express my gratitude to my supervisor Prof. Dr. Mohd
Razali Mahmud for his guidance, suggestions, comments and corrections to my
thesis. I really appreciate his patience and guidance in support of my research and
education. During his supervision period, I gained a lot of new ideas and valuable
knowledge from him. I would like to extend my thanks and appreciation to Mr.
Bustami Berahim, Mr. Ghazalli Khalid, and Mr. Eranda Kanchana Gunathilaka for
their assistance during data collection and all HRTO researchers for sharing their
knowledge, expertise and opinions with me and guide me along the way allowing me
to complete this study successful.
Secondly, I would like to show my appreciation to my buddies and friends
who have always giving physically and mentally support to me. Without their
supports, I might fail to accomplish this study. I appreciated all the times that spent
with them in these years of study life in Universiti Teknologi Malaysia.
Finally, I would also like to extend my thanks to my family members for their
understanding, moral support and encouragement. Last but not least, I would like to
thank everyone who have contributed to the completion of my thesis titled
“Classification of Multibeam Snippets Data using Statistical Analysis Method”.
V
ABSTRACT
The multibeam snippets data, an acoustic backscatter data acquired by the
multibeam sonar systems, carries important information about the seafloor and its
physical properties, thus aid in seafloor classification. This acoustic backscatter
strength is highly dependent of incidence angle due to different mechanism of
scattering with different angular domains. Therefore, it is necessary to perform
certain corrections for the backscatter data before producing the hydrographic plan.
This is solved with the radiometric correction using CARIS HIPS & SIPS 7.0
software and geometric correction using Matlab programming. Radiometric
correction removed the Time Varied Gain from the data while geometric correction
corrected the data for local bottom slope, seafloor insonified area and angular
dependency. The seafloor can be classified using the produced distribution histogram
of the desired study area. It is found that the snippets intensities estimated from the
mean of snippets intensities provide an accurate measurement of the actual intensities
strength of the seafloor and play an important role in correcting the angular
dependency of the data. Besides that, the Gamma distribution model is found to be
fitting well with the distribution of snippets intensities. The parameters of the
Gamma distribution model, the scale and shape parameters are found to be dependent
on the incidence angles of data. Furthermore, the Kolmogorov-Smimoff test was
carried out to access the fitting of other statistical distribution models such as the
Rayleigh and Log-normal distribution models in fitting with the distribution of
snippets intensities. It is shown that the Rayleigh and the Log-normal distribution
models followed only with the head of the distribution of the experimental data but
not towards the tail of experimental distribution. Further experiment on comparing
the backscattering characteristics of snippets data that were collected from different
types of seafloor habitats is recommended for future research.
vi
ABSTRAK
Data pemerum gema berbilang alur snippets, suatu data sebaran akustik yang
diperolehi dengan sistem pemerum gema berbilang alur, membawa maklumat
penting mengenai dasar laut dan sifat-sifat fizikalnya serta membantu dalam
pengklasifikasian dasar laut. Kekuatan sebaran balik akustik ini amat bergantung
kepada sudut tuju sebaran disebabkan oleh mekanisme sebaran yang berbeza dengan
domain yang berlainan sudut. Oleh itu, pembetulan tertentu perlu dilakukan bagi data
sebaran balik sebelum menghasilkan pelan hidrografi. Ini dapat diselesaikan dengan
pembetulan radiometrik menggunakan perisian CARIS HIPS & SIPS 7.0 dan
pembetulan geometrik menggunakan pengaturcaraan Matlab. Pembetulan
radiometrik membetulkan data untuk Time Varied Gain manakala pembetulan
geometrik membetulkan data untuk kecerunan dasar laut, keluasan kawasan dan
pergantungan kepada sudut. Dasar laut boleh diklasifikasikan dengan menggunakan
histogram taburan untuk kawasan kajian tersebut. Adalah didapati bahawa keamatan
snippets yang dianggarkan daripada min keamatan snippets menyediakan suatu
ukuran tepat kekuatan keamatan sebenar dasar laut dan memainkan peranan penting
dalam membetulkan pergantungan sudut data. Selain itu, model taburan Gamma
didapati sesuai dengan taburan keamatan snippets. Parameter model taburan Gamma,
iaitu parameter skala dan bentuk didapati bergantung kepada sudut tuju data.
Tambahan pula, ujian Kolmogorov-Smimoff telah dijalankan untuk mengakses
penyesuaian model taburan statistik seperti Rayleigh dan Log-normal dengan taburan
keamatan snippets. Keputusan menunjukkan bahawa taburan model Rayleigh dan
Log-normal hanya mengikuti taburan data eksperimen di permulaan tetapi tidak ke
akhir taburan eksperimen. Eksperimen selanjutnya bagi perbandingan ciri-ciri
sebaran data snippets dari pelbagai dasar laut dicadangkan untuk kajian pada masa
hadapan.
vii
TABLE OF CONTENTS
APTER TITLE PAGE
TITLE PAGE i
AUTHOR’S DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xii
LIST OF FIGURES xiii
LIST OF ABRREVIATIONS xviii
LIST OF APPENDICES xx
1 INTRODUCTION
1.1 Background of Study 1
1.1.1 Acoustic Remote Sensing Technique in Seafloor 3
Study
1.1.1.1 Multibeam Side Scan (MB S S) 4
1.1.1.2 Seafloor Backscatter Envelopes (Snippets) 5
1.1.2 Correcting the Multibeam Snippets Data 7
1.1.3 Statistical Analysis of Multibeam Snippets Data 8
in Shallow Water
1.2 Problem Statements 8
1.3 Objectives 9
1.4 Scope of Study 10
1.5 Significance of Study 10
viii
1.6 Thesis Layout 11
1.7 Contributions of Research 12
1.8 Summary 12
2 MULTIBEAM ACOUSTIC BACKSCATTERING
2.1 Introduction 13
2.2 Sea Water Acoustic Waves and Physical Characteristics 14
2.2.1 Acoustic Field 15
2.2.2 Sound Speed Measurements in Water 16
2.3 Acoustic Remote Sensing 16
2.3.1 Vertical-Incidence Sonar Approach 18
2.3.2 Side Scan Sonar Approach 18
2.3.3 Multibeam Sonar Approach 20
2.4 Multibeam Echosounder Systems (MBES) 21
2.4.1 Principle of Multibeam Echosounder 22
Systems Operation
2.4.2 Transducer Arrays 22
2.4.2.1 Flat Array Transducer 23
2.5 Principles of Acoustic Backscatter 24
2.5.1 Introduction 24
2.5.2 Scattering of Sound from the Seafloor 28
2.5.3 Seafloor Backscatter Strength 30
2.5.4 Physical Properties of the Seafloor 31
2.5.5 Angular Dependence of Acoustic Backscatter 33
2.6 Classification of Multibeam Backscatter Data 37
2.6.1 Seafloor Terrain Characteristics 38
2.6.2 Analysis of Backscatter Mosaics 38
2.7 Modelling Seafloor Backscatter Strength 39
2.7.1 Statistical Analysis of Multibeam Backscatter Data 42
2.7.2 Theoretical Models 43
2.7.2.1 Rayleigh Distribution Model 44
2.7.2.3 Log-normal Distribution Model 44
2.8 Computer Aided Resource Information System (CARIS) 45
2.8.1 Total Propagation Uncertainty 45
(TPU) Filter
2.8.2 Swath F ilter 46
2.8.3 Combined Uncertainty and Bathymetric 47
Estimator (CUBE)
2.8.3.1 Hypothesis Resolution 48
2.9 Bathymetry Associated with Statistical Errors 49
(BASE) Surface
2.10 Summary 51
3 MEASUREMENTS OF MULTIBEAM SNIPPETS DATA
3.1 Introduction 52
3.2 Usage of Multibeam Echo Sounder System 52
3.2.1 Reson SeaBat 8124 Multibeam System 54
Descriptions
3.2.1.1 Phase Detection 56
3.2.1.2 Interferometric Detection 57
3.2.1.3 Pulse Width 58
3.2.2 System Settings 59
3.2.2.1 Transmitting Signal 59
3.2.2.2 Receiving Signal 60
3.2.3 Dynamic Focusing 61
3.3 Multibeam Snippets Data Logging 63
3.3.1 Reson SeaBat 8124 Processor Firmware Upgrade 63
3.3.2 81 -P Setup for Snippets and Bathymetric Data 63
3.4 Preliminary view of determining the Snippets 68
Intensities Target Strength
3.5 Summary 71
ix
4 RADIOMETRIC AND GEOMETRIC CORRECTION OF
MEASURED MULTIBEAM SNIPPETS DATA
4.1 Introduction 72
4.2 Radiometric Correction of Measured Multibeam Snippets 74
Data
4.2.1 CARIS Software Processing 74
4.2.1.1 Procedures for CARIS Software 75
Processing
4.2.1.1.1 Vessel Configuration 75
4.2.1.1.2 HIPS Vessel File (HVF) 77
Processing
4.2.1.1.3 Create a New Project 79
4.2.1.1.4 Converting Raw Data 82
4.2.1.1.5 Open a Project 84
4.3 Multibeam Snippets Intensities Data 85
4.4 Removal of System Time Varied Gain (TVG) 88
4.4.1 Time Varied Gain (TVG) Correction 89
4.4.2 Multibeam Snippets Data Corrected for System 95
Settings
4.5 Geometric Correction of Measured Multibeam Snippets 96
Data
4.5.1 Correcting for Local Bottom Slope and Seafloor 96
Insonified Area
4.5.2 Correcting for Angular Dependency 98
4.6 Summary 101
5 SEAFLOOR CLASSIFICATION USING STATISTICAL
ANALYSIS METHOD
5.1 Introduction 102
5.2 Gridding of Multibeam Snippets Data 102
5.3 Statistical Distribution Model in Seafloor 103
Classification
5.3.1 Gamma Distribution Model 103
5.4 Seafloor Classification using Statistical Analysis 106
Method
5.5 Summary 113
X
xi
6 ANALYSIS ON THE INTENSITIES AND STATISTICAL
VARIATIONS OF MULTIBEAM SNIPPETS DATA
6.1 Introduction 114
6.2 Dependence of Multibeam Snippets Data on the 115
Incidence Angle
6.3 Dependence of the Multibeam Snippets Peak 118
Intensities on Incidence Angle
6.4 Statistical Distribution of the Multibeam Snippets 121
Intensities
6.4.1 Analysis of Data in Fitting with Gamma 121
Distribution Model
6.4.2 Kolmogorov-Smimoff (KS) Statistical Test 125
6.5 Summary 128
7 CONCLUSIONS AND RECOMMENDATIONS
7.1 Conclusions 130
7.2 Recommendations for Future Work 132
REFERENCES 133
Appendices A -E 147-165
xii
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Typical values of density, sound speed and resulting 29
acoustic impedance for various grades of sediment
2.2 Input parameters for APL model 41
2.3 Different methodologies of BASE surface 50
2.4 The relationship between degree of resolution and 51
depth
5.1 Groups of measured multibeam snippets data 109
6.1 Scale (A) and shape (P) parameters for the fitting 123
with gamma distribution model shown in Figure 6.7
6.2 Statistical distribution functions 125
6.3 Groups of angle domain 126
xiii
FIGURE NO.
1.1
1.2
1.3
1.4
1.5
1.6
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
LIST OF FIGURES
PAGE
2
2
5
6
6
10
22
23
24
26
Temperature dependence for oceanic waters (34-36 ppt) 27
Schematic representation of the reflection, 28
transmission and scattering of acoustic energy at
smooth and rough bottom
Geometric arrangement for measuring backscatter 31
strength
Wave phase shift due to seafloor roughness 32
Surface scattered patterns at oblique incidence 33
(a) A smooth surface reflecting coherently the Incidence
Wave; (b) A rough surface with a large fraction of
acoustic energy scatter incoherently
Illustration of the -3dB limit from a hypothetical 34
acoustic beam
TITLE
Principle of single beam echo sounder
Principle of multibeam echo sounder
Multibeam Side Scan Sonar Imagery
Multibeam snippets imagery
Derivation of multibeam backscatter imagery
Study area
MBES beam footprint and swath coverage
Beam footprints resulting from the intersection of
transmission and reception in RESON SeaBat 8124
MBES
Example for flat transducer arrays
Spherical spreading of an acoustic wave
2.11 Geometry of incident pulse length on the seafloor for 35
beams at normal and oblique incidence
2.12 Across-track slope is corrected to the angle of 35
incidence with the instantaneous swath profile
2.13 Correction for the angle of incidence using a 36
swath series to determine along-track slope
2.14 CUBE Hypothesis 48
3.1 System performance of Reson SeaBat 8124 system 55
3.2 Phase and amplitude detection 56
3.3 Principle of interferometric detection 57
3.4 Pulse width influence on measurement; 58
a small pulse results in a better resolution than a
large pulse width.
3.5 Sonar head of Reson SeaBat 8124 multibeam 60
system
3.6 TxPower Settings 60
3.7 RxGain Settings 61
3.8 Beam patterns of the Reson SeaBat 8124 62
multibeam system receive array modeled with (green)
and without (blue) dynamic focusing
3.9 BITE display screen 64
3.10 Geo Referencing in backscatter computation setup 64
3.11 Flat bottom assumption 65
3.12 Altitude parameters in backscatter computation setup 66
3.13 Normalization of backscatter data 66
3.14 Channel selection 67
4.1 Work flow of radiometric correction of measured 73
multibeam snippets data
4.2 CARIS HIPS and SIPS 7.0 75
4.3 Caris HIPS and SIPS Vessel Wizard 77
4.4 Vessel File Creation 78
4.5 Vessel in 3D Graphic Display 78
4.6 Project information requirements 79
4.7 Adding created vessel file 80
xiv
4.8
4.9
4.10
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
4.19
4.20
4.21
4.22
4.23
4.24
4.25
4.26
4.27
xv
Adding day 80
Description of created proj ect 81
Selecting UTM zone 81
Defining the geographic view of project extent 82
Select format of file to be converted. 83
Select XTF from the list
Select Navigation Coordinate Type. Choose 83
Ground and UTM-WGS84 Zone 1 IN. Accept the
default navigation limits
Click Convert Side Scan check box and select 84
Sonar Channel 1,2. Set both Navigation data field
and Gyro data field to Ship respectively and select
Apply Image Enhancement
Window of Open Project 84
Ship track line of selected project 85
Measured multibeam snippets intensities data in 86
ASCII format
Plan view of measured multibeam snippets intensities 87
data
Plan view of measured multibeam snippets intensities 87
data with different depths interval
Outliers 88
Outliers removed 88
Various gain options 90
Various losses when the signal travels through the 91
water column and seabed; received levels versus
source levels for an arbitrary model
Transmission loss as function of the frequency 93
Signal display window 94
Signal display window of backscatter intensities 94
(before Time Varied Gain correction)
Signal display window of backscatter intensities 94
(after Time Varied Gain correction)
4.28 Plotting of multibeam snippets data (dB) without 95
correction for system settings
4.29 Plotting of multibeam snippets data (dB) after 95
correction for system settings
4.30 Influence of local bottom slopes correction 98
4.31 Before correction for angular dependency 99
4.32 After corrected for angular dependency 99
4.33 Work flow of geometric correction and 100
seafloor classification
5.1 Multibeam snippets data gridded to 1 x 1 meter grid 103
cell size
5.2 Gamma distribution model fitting with snippets data 104
5.3 Grab Sampler 106
5.4 Muddy sea bottom 106
5.5 Snippets intensities plotted map 107
5.6 Selecting desired area (1) 107
5.7 Snippets intensities distribution histogram 108
5.8 Selecting desired area (2) 108
5.9 Snippets intensities distribution histogram 109
5.10 Multibeam snippets data intensities (Group 1) 110
5.11 Multibeam snippets intensities distribution histogram 110
(Group 1)
5.12 Multibeam snippets data intensities (Group 2) 111
5.13 Multibeam snippets intensities distribution histogram 111
(Group 2)
5.14 Multibeam snippets data intensities (Group 3) 112
5.15 Multibeam snippets intensities distribution histogram 112
(Group 3)
6.1 Intensities for inner beams (~ 0°) 115
6.2 Intensities for moderate beams (-30°) 116
6.3 Intensities for oblique beams (~ 60°) 117
6.4 Numerical modelled mean value of the peak 119
intensities versus number of samples
xvi
6.5
6.6
6.7
6.8
6.9
6.10
6.11
Mean intensities strength (peak and average) versus 120
incidence angle
Skewness squared versus kurtosis of multibeam 122
snippets intensities with different angular domains
Probability density for multibeam snippets intensities 123
for domains of incidence angles; black line shows the
fitting with gamma distribution model
The value of gamma shape parameter, p versus 124
incidence angle
Probability of false alarm fitting with distributions 126
model for normalized multibeam snippets intensities
(near - nadir angle)
Probability of false alarm fitting with distributions 127
model for normalized multibeam snippets intensities
(moderate angle)
Probability of false alarm fitting with distributions 127
model for normalized multibeam snippets intensities
(oblique angle)
xvii
xviii
LIST OF ABRREVIATIONS
ASCII - American Standard Code for Information
Interchange
AUV Autonomous Operated Vehicle
B.A - Backscatter Area
BASE - Bathymetry with Associated Statistical Error
BITE - Built-In Test Environment
BS - Backscatter Coefficient
BTS - Bottom Target Strength
CARIS - Computer Aided Resource Information System
CDF - Cumulative Distribution Functions
CLT - Central Limit Theorem
CUBE - Combined Uncertainty and Bathymetric
Estimator
DB - Decibel
DEM - Digital Elevation Model
DNC - Digital Nautical Chart
DpTPU - Depth Error Estimate
EEZ - Exclusive Economic Zone
EL - Echo Level
ENC - Electronic Navigation Chart
GIS - Geographic Information System
GLCM - Grey Level Co-Occurrence Matrix
HDCS - Hydrographic Data Cleaning System
HIPS - Hydrographic Information System
HVF - HIPS Vessel File
Hz - Hertz
HzTPU - Horizontal Error Estimate
xix
IHO - International Hydrographic Organization
IL - Acoustic Intensity
KS - Kolmogorov-Smimoff
MBES - Multibeam Echo Sounder
MBSS - Multibeam Side Scan
MRU - Motion Reference Unit
MSP - Method of Small Perturbation
NPD - Norwegian Petroleum Directorate
PDF - Probability Density Distributions
PPT - Parts Per Thousand
RMS - Root Mean Square
ROV - Remotely Operated Vehicles
RP - Reference Point
SBES - Single Beam Echo Sounder
SCS - Seafloor Classification Systems
SIPS - Sonar Image Processing System
SNIPPETS - Seafloor Backscatter Envelopes
Std Dev - Standard Deviation
SSA - Small Slope Approximation
TL - Transmission Loss
TPU - Total Propagation Uncertainty
TVG - Time Varied Gain
XTF - Extended Triton Format
XX
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Overall Methodology 147
B RESON SeaBat 8124 Multibeam Sonar 149
Processor Firmware Upgrade
C Multibeam Snippets Intensities Data (dB) 151
D Matlab Code 161
E Multibeam Snippets Intensities Distribution Map 165
CHAPTER 1
INTRODUCTION
1.1 Background of Study
Hydrographic surveying can be defined as a science of measuring and
depicting the parameters and characteristics of the seabed to define the surface of the
seabed, the geographical relationship of the seabed with the land mass and dynamical
features of the sea (International Hydrographic Organization, 2005). For the purpose
and needs for surveying and navigation, these parameters consist of depth or
bathymetric data, the pattern and morphology of the seabed, velocity and direction of
currents, tides, wave and the position of underwater objects (International
Hydrographic Organization, 2005).
Until the Second World War, almost all of bathymetric surveys were made by
lead line. The introduction of single beam echo sounder (SBES) after the world war
has replaced the conventional lead line techniques. The SBES collects bathymetric
data by the measurement of the travel time interval of the returned acoustic signal
(Mayer et al., 2007) and provides better accuracy than the lead line techniques. In
1980s, Multibeam Echo Sounder (MBES) or Multibeam Sonar System has been
introduced in hydrographic surveying as a tool for bathymetric data collection.
Figure 1.1 shows the principle of single beam echo sounder whilst Figure 1.2 shows
the principle of multibeam echo sounder.
2
Figure 1.1: Principle of single beam echo sounder
(Source: http://www.ozcoasts.gov.au/glossary/def_s-t.jsp)
Figure 1.2: Principle of multibeam echo sounder
(Source: http://divediscover.whoi.edu/tools/sonar-multibeam.html)
Development of maritime activities in Malaysia has increased rapidly.
Therefore, the data from hydrographic surveying nowadays is widely used not only
for nautical charting, but also for other applications such as marine engineering, pipe
or cable laying and inspection, sedimentation studies, ocean habitats studies as well
as maritime boundary determination.
3
1.1.1 Acoustic Remote Sensing Technique in Seafloor Study
Towards the development in the exploration and exploitation, the ocean has
become a powerful and commercial interested subject among the industries. Marine
activities whether in the field of oil & gas exploration, marine geology and etc., need
more and more seabed recognition tools and methods. Acoustic remote sensing
techniques are one of the most cost-effective methods of resource mapping,
particularly in the coastal zone. Seabed mapping and monitoring using acoustic
remote sensing techniques has proven to be a useful tool in contemporary marine
resource management particularly in turbid and deep water areas, where aerial and
satellite remote sensing based on measuring the electromagnetic spectra is of limited
use (Caruthers, 1977). The applications of acoustic remote sensing technique in
seafloor study include measurements such as: a.) Bathymetry, which provides
quantitative information on the relief; b.) Acoustic imaging, which is most often used
in a qualitative sense for geomorphology and for feature detection of obstacle
avoidance; c.) Acoustic bottom loss which is related to the physical properties of the
substrate (de Moustier and Matsumoto, 1993).
Backscatter data or better known as the acoustic intensity of the seafloor is
one of the by-products of MBES systems. The power of the received signal is
analysed and compared with the transmitted signal and corrected for parameters such
as incidence angle, attenuation and etc. Some processing algorithms record the full
waveform for each footprint, piece the sample string back together, and analyse the
resulting wave trend in a manner similar to side scan. This approach can yield
backscatter data with higher resolution (smaller pixel size) than the bathymetry data
(Daniel et al., 1999). In the measurement of backscatter data, the data are logged
simultaneously with bathymetric data, which will permit precise co-registration of
the backscatter with the multibeam data set, resulting the positional accuracy of the
backscatter to be better compared to the traditional side scan image (FUGRO, 2005)
Backscatter data carries important information about the seafloor and its
physical properties (Fonseca and Calder, 2005). This information may provide
valuable data to aid in seafloor classification and important auxiliary information for
4
a bathymetric survey. Acoustic imagery of the seafloor, generated from either
multibeam or towed side scan sonar system, represents the received acoustic
backscatter intensities. These acoustic intensities depend on the scattering strength of
the seafloor, distribution of scatterers, degree of bottom penetration and sub-bottom
volume scattering, and ensonification angle (Whitmore, 2003). Acoustic backscatter
imagery is used in a diverse set of applications and disciplines ranging from
geological mapping to the detection of manmade objects on the seafloor. As such,
acoustic backscatter imagery has many naval applications that include obstacle
avoidance, characterization of the type of seafloor for engineering purposes, mine
detection and parameterization of the seafloor for acoustic propagation modelling
(Etter, 2003).
Currently, there are two approaches in the collection of backscatter data using
multibeam system; the Multibeam Side Scan (MBSS) and Seafloor Backscatter
Envelopes (Snippets). Multibeam Side Scan and Snippets can display a
representation of the seafloor using the principle of acoustic imaging.
1.1.1.1 Multibeam Side Scan (MBSS)
Multibeam Side Scan is acquired by forming a large beam on either side of
the sonar head. Time series data is sampled across these beams by summing all of
returns from any given time to a pixel (quoted from webpage: http://www.fugro-
pelagos.com/papers/Backscatter_GIS.doc.). The result of the Multibeam Side Scan
imaging is a mosaic covering the seafloor. Assuming the lines have been run
appropriately, the imaging should provide 100% coverage and it may be that the
intensities information covers more than the bathymetry unless the beams have been
invalidated for accuracy reasons (International Hydrographic Organization, 2005). It
is likely that the extra intensities information will not be used since it does not have
depth information associated with it, but it remains available. The drawbacks of the
Multibeam Side Scan is that potentially useful spatial information is discarded in the
process of reducing the intensities time series surrounding the bottom detect of each
beam to a single value. Figure 1.3 shows the multibeam side scan sonar imagery.
5
Figure 1.3: Multibeam Side Scan Sonar Imagery
(Source: http://halmapr.eom/news/tritech/category/news/page/4/)
1.1.1.2 Seafloor Backscatter Envelopes (Snippets)
The multibeam seafloor backscatter envelopes or better known as multibeam
snippets data, are acquired by sampling an individual time series for each beam in the
multibeam system. Sampling only occurs in the region of the bottom detect rather
than throughout the water column (time-arc) (quoted from webpage:
http ://www. fugropelagos. com/papers/newdevinmulitbeambackscatter/tgpibackscatter
.htm). These samples are assigned as backscatter return for their respective individual
beam footprints. As a result, the signal-to-noise ratio of the backscatter is greatly
improved. Figure 1.4 shows the multibeam snippets imagery whilst Figure 1.5 shows
the derivation of multibeam backscatter imagery.
6
Figure 1.4: Multibeam snippets imagery
(Source :http ://www. fiagropelagos.com/papers/newdevinmulitbeambackscatter/tgpiba
ckscatter.htm)
Figure 1.5: Derivation of multibeam backscatter imagery
(Source:http://www.fugropelagos.com/papers/newdevinmulitbeambackscatter/
tgpibackscatter.htm)
7
As the snippets data act as an individual time series that directly associated
with a portion of the bathymetric profile, therefore it is much easier to correct for
slant range. When properly processed, multibeam snippets data can provide a view of
seafloor geology and geomorphology at resolutions as little as a few decimetres and
applicable in operations such as quantitative estimation of sediment transport in
large-scale sediment waves and style of seafloor mass-wasting. In addition, the
imagery potentially provides a means of quantitative classification of seafloor
lithology, allowing the ability to examine spatial distributions of seabed sediment
type without resorting to subjective estimation or prohibitively expensive bottom-
sampling programs.
Provisions of snippets information are akin to the output of side scan sonar
system and produce a representation of the seafloor in terms of the returned
intensities echoes. The significant difference between the output of snippets
information and side scan sonar system is that the multibeam echo sounder system is
measuring the depth concurrently with the snippets information and this allows for a
more sophisticated level of display. The depth data, when combined with beam angle,
effectively gives the position on the seafloor to which the snippets information relate
and therefore provide a true geometric correction of the backscatter image. Since this
is in fact not the case, there will be distortions on the side scan sonar image. For this
research, snippets data are collected and processed.
1.1.2 Correcting the Multibeam Snippets Data
The snippets signal received by multibeam sonar systems can be influenced
by various parameters, which can be categorized into system settings (e.g. power,
gain, pulse length), acoustic propagation conditions (e.g. absorption and spreading
loss), beam geometry (e.g. range, incident angle, footprint size) and seafloor
properties (seafloor roughness, acoustic properties). It is important that the received
snippets signal is fully corrected so that it is invariant to system settings, propagation
conditions and beam geometry so that changes in the snippets signal can be attributed
to changes in the seafloor properties, and thus, be used to derive information about
8
the substrate and geomorphology of the seabed (Gavrilov, 2005). More details are
discussed in Chapter 3 and Chapter 4.
1.1.3 Statistical Analysis of Multibeam Snippets Data in Shallow Water
The statistical distributions of snippets values have been identified as a
potential characteristic for classifying the seafloor. Exploiting the variation in
multibeam backscatter measurements for seafloor classification has been achieved
through probability density distributions (PDF) (Stewart et al., 1994). In simplified
terms, a weak return signal (low amplitude) indicates a soft bottom substrate and a
strong return signal (high amplitude) indicates a hard bottom substrate (Gustav,
2008).
1.2 Problem Statements
Before a backscatter map can be made use, the backscatter data must be
geographically registered using the collected bathymetric profile which accounts for
full orientation and refraction. It is important to apply the radiometric correction to
the backscatter data on a ping-by-ping basis for variables such as transmission power
and receiver gain in order to turn them back to the “pure condition”, which means the
data are invariant to system settings.
Both system settings and acoustic propagation conditions are easily corrected
for, however, artefacts (angular dependency) in multibeam backscatter images due to
beam geometry are hard to remove. In particular, the angular dependency of
multibeam backscatter strength can be persistent in multibeam backscatter images,
characterized by a stronger return at vertical incident angles, known as ‘nadir
striping’ (Siwabessy and Gavrilov, 2004). Attempts made at correcting through
theoretical models, which are usually based on Lambertian law, seem to be
inadequate especially for the modem multibeam sonar systems currently used for
9
shallow water work (Siwabessy and Gavrilov, 2004). Furthermore, there are no
universal models for angular backscatter correction suitable for every seabed type.
Therefore, geometric corrections play an important role in resolving this problem.
This included using the right method to remove the angular dependency from the
data.
Middleton (1999) theoretically stated that the statistical distribution of the
backscatter intensities should follow a gamma distribution model. However, recent
studies have noted a non-Rayleigh character of backscatter statistics for shallow
water seabed and suggested different models, such as Rayleigh mixture distribution
(Gallaudet and de Moustier , 2003; Hellequin et al., 2003; Lyons and Abraham, 1999;
Abraham, 1997 and Dunlop, 1997) and log-normal distribution models (Trevorrow,
2004; Stanic and Kennedy, 1992 and Gensane, 1989). Therefore, it is important to
determine the distribution model that best fitting with the distribution of the
backscatter intensities.
1.3 Objectives
In this research, the objectives are specified as follows:
1.) To perform geometric and radiometric correction for multibeam
snippets data.
2.) To perform seafloor classification with statistical analysis method
using multibeam snippets data.
3.) Assessment of the statistical distribution models in fitting with
multibeam snippets data.
10
1.4 Scope of Study
The scopes of this study are described as follows:
i. Study and hands-on practical on RESON SeaBat 8124 multibeam
sonar system.
ii. Multibeam snippets data collection.
iii. Processing of multibeam snippets data.
iv. Studies on seafloor classification using statistical analysis method on
measured multibeam snippets data.
v. Study area - for this research, data collection was carried out in
shallow coastal waters with 500m x 500m coverage that located at
Lido Beach, Johor Bahru, Malaysia (Figure 1.6).
Figure 1.6: Study area
1.5 Significance of Study
The use of acoustic remote sensing techniques in seabed mapping and
monitoring has proven to be a useful tool in contemporary marine resource
management (Kenny et al., 2003), where aerial and satellite remote sensing based on
11
the measurement of electromagnetic spectra is of limited use (Caruthers, 1977;
Pickrill and Todd, 2003).
Seafloor acoustic response can be linked to sea-floor roughness produced by
features such as ripples, benthic reworking and to physical properties of the surficial
sediments (texture, dewatering, compaction, acoustic impedance, density, porosity,
velocity). These properties are closely related, for example acoustic impedance is a
function of velocity and density, which in turn depends on other properties such as
compaction and porosity. It is an important development of acoustic remote sensing
technique in marine science (Qiu-Hua Tang et al., 2005).
The integration of bathymetry and backscatter information has led to a
revolution and larger potential in our understanding of seabed features, sedimentary
processes and seafloor studies. For this purpose, more research should be conducted.
(Hughes Clarke et al., 1997b; Mayer et al., 1997b; Baker et al., 1998 and Mayer et
al., 1999).
1.6 Thesis Layout
This thesis is divided into 7 chapters with details as stated below:
1) Chapter 1 mainly introduced the background of the study and to put the work
in context.
2) Chapter 2 gave some key elements of relevant theory and a review of
literature in the field of multibeam acoustic backscattering thus acoustic
seafloor classification.
3) Chapter 3 presented the necessary steps in the measurement of multibeam
snippets data.
4) Chapter 4 is the focus of Objective 1, where the radiometric and geometric
correction of measured multibeam snippets data is discussed.
12
5) Chapter 5 is the focus of Objective 2 which devoted to the method of
classification the seafloor using statistical analysis method.
6) Chapter 6 is the focus of Objective 3 which demonstrated and assessed the
statistical variations of multibeam snippets data.
7) Thesis is ended with Chapter 7 which shows some discussion of the key
findings and makes recommendations for future work in this research area.
1.7 Contributions of Research
In this research, the multibeam snippets data, which is one of the latest by
products of the multibeam system, is being explored for its usage in the study of
seafloor classification. The RESON SeaBat 8124 multibeam sonar system is being
upgraded and explored to generate and collect the multibeam snippets data. Other
studies, such as Bentrem et al. (2006) and Houziaux et al. (2007) have used a side
scan like approach to maximize the resolution of backscatter intensity produced by
multibeam sonar systems. However, implementation of such an approach is not
trivial because it requires adequate corrections for the backscatter data and beam
pattern. Besides that, various tests and experiments have been carried out for the
assessment of the statistical variations of the multibeam snippets data with the
distribution models as stated theoretically by other researchers. As a result, this
research aimed to give a better exploration of the acoustic backscattering technology,
especially in the study of seafloor classification. Overall methodology of this study
are presented in Appendix A.
1.8 Summary
This chapter briefly described the background of the study, providing a
general view towards the acoustic remote sensing technique in seafloor study. These
included discussion about the backscatter data and the necessary corrections for the
data before a seafloor map can be produced with it.
134
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