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The sound of sediments : acoustic sensing in uncertain environments
van Leijen, A.V.
Publication date 2010
Link to publication
Citation for published version (APA):
van Leijen, A. V. (2010). The sound of sediments : acoustic sensing in uncertain environments.
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Contents
Preface ix
1 Introduction 1
1.1 Sea bottom characterization . . . 2
1.2 Geoacoustic inversion . . . 3
1.2.1 Research challenges . . . 3
1.2.2 Research questions . . . 4
1.2.3 Methodology . . . 4
1.3 This thesis . . . 5
1.4 Work not covered in this thesis . . . 6
2 Operational context 9 2.1 Introduction . . . 10
2.2 Environmental information for naval warfare . . . 10
2.3 Acoustic sensing in shallow water . . . 12
2.4 REA as a research project . . . 13
2.5 Discreet REA . . . 13
2.6 Sound sources of opportunity . . . 14
2.7 Applications . . . 15
2.7.1 Basic acoustic sensing . . . 15
2.7.2 Advanced acoustic sensing . . . 16
2.7.3 Assessment of buried waste . . . 17
2.8 Conclusions . . . 19
3 Acoustic inversion 21 3.1 Introduction . . . 21
3.1.2 The environmental model . . . 23
3.2 Inverse acoustic sensing . . . 25
3.3 Sound sources of opportunity . . . 26
3.4 Receiving sensors . . . 27
3.5 The medium . . . 29
3.5.1 Water column . . . 29
3.5.2 Sea bottom . . . 30
3.5.3 Ambient noise . . . 31
3.6 Forward propagation modeling . . . 32
3.6.1 Ray theory . . . 32
3.6.2 Normal mode theory . . . 33
3.6.3 Parabolic equation models . . . 33
3.6.4 Criteria for model selection . . . 33
3.6.5 Available implementations . . . 34
3.7 Objective functions . . . 35
3.7.1 The Bartlett processor . . . 35
3.7.2 Other processors . . . 37
3.8 Optimization . . . 37
3.9 Inversion toolbox . . . 38
3.10 Conclusions . . . 38
4 Geoacoustic inversion using a survey vessel as sound source 39 4.1 Introduction . . . 40
4.2 Material and methods . . . 41
4.3 The Saba bank . . . 42
4.4 Low frequency measurements and inversion . . . 44
4.4.1 Geoacoustic inversion setup . . . 45
4.4.2 Objective function . . . 47
4.5 Results and discussion . . . 47
4.6 Summary and conclusions . . . 50
5 Geoacoustic inversion with an autonomous underwater vehicle 51 5.1 Introduction . . . 51
5.2 Inversion with AUV self noise . . . 52
5.3 Concept of inversion with self noise . . . 53
5.3.1 Error function . . . 53
5.3.2 Movement of the sound source . . . 54
5.4 AUV experiments . . . 55
5.5 Observations . . . 55
5.5.1 Water column and SVP . . . 55
5.5.3 Receiving sensors . . . 58
5.6 Self noise of REMUS AUVs . . . 58
5.6.1 Survey signature . . . 59
5.6.2 Acoustic signature at maximum speed . . . 60
5.7 Results . . . 60
5.8 Discussion . . . 61
5.9 Conclusions . . . 63
6 Inversion with Ant Colony Optimization 65 6.1 Introduction . . . 65
6.2 Introduction to Geoacoustic Inversion . . . 66
6.2.1 Inversion based on Matched Field Processing . . . 66
6.2.2 Inversion for Bottom Geoacoustic Parameters . . . 67
6.2.3 The Yellow Shark Experiments . . . 67
6.3 Ant Colony Optimization for Inversion . . . 68
6.3.1 ACO and Other Metaheuristics for Inversion . . . 68
6.3.2 Application of MAX − MIN Ant System . . . 68
6.3.3 Tuning of MAX − MIN Ant System . . . 70
6.3.4 Results for Yellow Shark . . . 71
6.4 Uncertainty Analysis . . . 72
6.4.1 The Bayesian Framework for Genetic Algorithms . . . 72
6.4.2 Uncertainty Analysis with MAX − MIN Ant System . . . 73
6.5 Conclusions . . . 73
7 Metaheuristic optimization of acoustic inverse problems 75 7.1 Introduction . . . 75
7.2 Optimization of acoustic inverse problems . . . 77
7.2.1 Inverse problems . . . 77
7.2.2 Acoustic inverse problems . . . 77
7.2.3 Selected inverse problems . . . 79
7.3 Metaheuristic search strategies . . . 81
7.3.1 Definitions . . . 81
7.3.2 Simulated Annealing . . . 81
7.3.3 Genetic Algorithm . . . 82
7.3.4 Ant Colony Optimization . . . 83
7.3.5 Differential Evolution . . . 83 7.3.6 Overview . . . 83 7.4 Experimental setup . . . 85 7.4.1 Method of comparison . . . 85 7.4.2 Configurations . . . 86 7.4.3 Tuning . . . 87
7.5 Results . . . 88
7.5.1 Tuning results . . . 89
7.5.2 Run length distributions . . . 92
7.6 Discussion . . . 93 7.6.1 Comparison . . . 93 7.6.2 Uncertainty assessment . . . 99 7.7 Conclusions . . . 100 8 Conclusions 101 8.1 Introduction . . . 101 8.2 Conclusions . . . 101
8.2.1 Inversion with shipping sounds . . . 102
8.2.2 Reduction of data volume . . . 102
8.2.3 Uncertainty assessment . . . 104
8.2.4 Performance of metaheuristic optimizers . . . 104
8.3 Applications and future research . . . 105
A Ambient Noise Curves 107
B Sound speed equations (in water) 109
C Test functions for tuning 111
Bibliography 113
Acronyms 125
Summary 129
