Establishing a sea bottom model by applying a multi-sensor acoustic remote sensing approach
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op vrijdag 5 juli 2013 om 12:30 uur
door
Kerstin SIEMES
Diplom-Ingenieurin
Rheinische Friedrich-Wilhelms-Universit¨at Bonn, Duitsland
geboren te Wermelskirchen, Duitsland
Dit proefschrift is goedgekeurd door de promotoren:
Prof. dr. D.G. Simons
Prof. dr. J.-P.O.F.G. Hermand
Samenstelling promotiecommissie:
Rector Magnificus, voorzitter
Prof. dr. D.G. Simons, Technische Universiteit Delft, promotor Prof. dr. J.-P.O.F.G. Hermand, Universit´e libre de Bruxelles, promotor Prof. dr. ir. O.D. Debeir, Universit´e libre de Bruxelles
Prof. dr. A. Stepnowski, Gdansk University of Technology Prof. dr. C. de Mol, Universit´e libre de Bruxelles Prof. dr. ir. C.P.A. Wapenaar, Technische Universiteit Delft Dr. ir. M. Snellen, Technische Universiteit Delft
Prof. dr. G.J.W. van Bussel, Technische Universiteit Delft, reservelid
Universit´e libre de Bruxelles and Delft University of Technology made important contributions to the work described in this dissertation.
The research was supported by The Netherlands Organization for Applied Research (TNO) and by the U.S. Office of Naval Research.
ISBN 978-90-8891-641-0
Copyright c2013 by Kerstin Siemes
Some rights reserved. Chapters 4 and 5 are adapted from published work
(DOI:10.1109/JOE.2010.2066711 and DOI:10.1121/1.3569718) and are reprinted here, with permission.
Typeset by the author with the LATEXDocumentation System.
Cover design: Proefschriftmaken.nl k Uitgeverij BOXPress Printed by: Proefschriftmaken.nlk Uitgeverij BOXPress Published by: Uitgeverij BOXPress, ’s-Hertogenbosch
Establishing a sea bottom model by applying a multi-sensor acoustic remote sensing approach
Dissertation
submitted for the degree of Doctor in Engineering Sciences
Kerstin SIEMES 2012/2013
thesis directors:
Prof. dr. J.-P.O.F.G. Hermand
Prof. dr. D.G. Simons
Contents
1 Introduction 1
1.1 Historic background of underwater measurements . . . 2
1.2 Recent developments in classifying the sea bottom . . . 2
1.3 Research objectives . . . 4
1.4 Outline of the thesis . . . 4
2 Underwater acoustic sensors 7 2.1 Sensors for hydrographic surveying . . . 7
2.1.1 Seafloor mapping tools . . . 8
2.1.2 Profiling tools . . . 9
2.2 Dedicated acoustic systems . . . 10
3 Trial areas and measurements 13 3.1 The MREA/BP’07 trial . . . 13
3.1.1 Equipment and measurements . . . 15
3.1.2 Bathymetry . . . 17
3.1.3 Properties of the sea bottom . . . 20
3.2 CBBC’04 . . . 23
3.2.1 Equipment and measurements . . . 23
3.2.2 Bathymetry . . . 24
3.2.3 Properties of the sea bottom . . . 25
4 Classification of SBES data 27 4.1 The SBES echo and its parameters . . . 27
4.2 Phenomenological classification by echo shape parameters . . . 30
4.2.1 Principal component analysis (PCA) and clustering . . . 30
4.2.2 Application of the PCA to the MREA/BP’07 data . . . 31
4.3 Model-based classification using the full echo envelope . . . 35
4.3.1 Method . . . 35
4.3.2 Application of the echoshape model to the CBBC’04 data . . . 37
4.3.3 Applicability of the echoshape model to an environment with soft sediments . . . 40
4.4 Model-based classification using the echo energy . . . 41
4.4.1 Method . . . 41
4.4.2 Application of the echo energy model to the CBBC’04 area . . . . 42
ii Contents
4.5 Conclusions . . . 45
5 Bayesian classification of MBES data 47 5.1 The Bayesian approach . . . 48
5.2 Application of the Bayesian approach to the MREA/BP’07 data . . . 52
5.3 Conclusions . . . 54
6 Efficient geoacoustic inversion strategies 55 6.1 Multi-frequency geoacoustic inversion approaches . . . 56
6.2 Establishing an optimal inversion strategy . . . 57
6.2.1 Criteria for comparing inversion strategies . . . 58
6.2.2 Settings of the global optimization method . . . 61
6.2.3 Frequency dependence of the inversion results . . . 70
6.3 Conclusions . . . 73
7 Geoacoustic inversion in practice 75 7.1 Confirmation of the optimal inversion strategy . . . 75
7.1.1 Inversion of synthetic vs. real data . . . 76
7.1.2 Confirmation by hydrographic data . . . 81
7.2 The effect of environmental variability on the inversion results . . . 84
7.2.1 Very soft sediments and the presence of gas . . . 84
7.2.2 Environments with a thick sediment layer . . . 87
7.3 Conclusions . . . 91
8 An integrated environmental picture 93 8.1 A combined survey . . . 93
8.2 Interpretation of the environmental picture . . . 94
8.3 Perspective . . . 95
A Sediment characteristics 97 A.1 Sampling and analysis . . . 97
A.2 Sediment type definitions . . . 97
A.3 Sediment properties affecting acoustic signals . . . 100
B Previous samples taken in the research areas 101 B.1 Sediments in the MREA/BP’07 area . . . 101
B.2 Sediments in the CBBC’04 area . . . 103
C Environmental models 105 C.1 Sound propagation in the water column . . . 105
C.2 Sound interaction with the water–sediment interface and sediment body . . 106
D Modeling the acoustic field 111 D.1 Solving the acoustic wave equation . . . 112
D.2 Normal mode modeling . . . 112
D.2.1 Normal modes in a lossy medium . . . 114
Contents iii
D.2.2 The relevance of modes . . . 114
D.2.3 Relations between the normal modes and the environment . . . 117
E Global optimization 119 E.1 Overview of global optimization strategies . . . 119
E.2 Differential Evolution (DE) . . . 120
E.2.1 Optimization strategy . . . 120
E.2.2 The performance of DE in inverting sediment properties . . . 122
Bibliography 129
Summary 135
Samenvatting 139
Acknowledgements 143
Curriculum vitae 145
