The sound of sediments : acoustic sensing in uncertain environments - Thesis

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The sound of sediments : acoustic sensing in uncertain environments

van Leijen, A.V. Publication date 2010

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van Leijen, A. V. (2010). The sound of sediments : acoustic sensing in uncertain environments.

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The Sound of Sediments

Acoustic Sensing in Uncertain

Environments

The Sound of Sediments

Acoustic Sensing in Uncertain

Environments

Universiteit van Amsterdam Plantage Muidergracht 24

The Sound of Sediments

Acoustic Sensing in Uncertain

Environments

Academisch Proefschrift

ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

op gezag van de Rector Magnificus

prof. dr. D.C. van den Boom

ten overstaan van een door het college voor promoties

ingestelde commissie,

in het openbaar te verdedigen in de Agnietenkapel

op donderdag 18 maart 2010, te 12.00 uur

door

Antonie Vincent van Leijen

Co-promotor: Prof. dr. drs. L.J.M. Rothkrantz Overige leden: Prof. dr. P. Adriaans

Prof. dr. D. G. Simons Dr. M. A. Ainslie Dr. ir. F. Bolderheij Dr. ir. A. Hoekstra

Faculteit der Natuurwetenschappen, Wiskunde en Informatica

ISBN: 978-90-9024895-0

This research was supported by the Netherlands Defence Academy

(NLDA) and the Centre for Automation of Mission-Critical Systems CAMS-Force Vision.

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

3.1.1 Active and passive sonar . . . 22 v

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

5.5.2 Bottom: bathymetry and seismic profiling . . . 55 vi

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 vii

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

Samenvatting 131

Preface

This story dates back to the summer of 1998, when I reported for duty at Naval Air Station Valkenburg. At the time I had just graduated mathematics at Groningen University and began my first job as a naval research officer. Leaving behind the academic practice of lemma, proposition and mathematical proof, I found myself in the operational world of naval aviation.

Valkenburg housed the Centre for Operational Data and Analysis (CODA) where I was introduced to the silent world of passive sonar. The acronym SONAR stands for SOund Navigation And Ranging, but I was soon told to forget these words, as passive sonar is something completely different from what these words suggest. Naval Air Station Valkenburg was the home of thirteen P3C Orion Mar-itime Patrol Aircrafts (MPAs), of which the main task was Anti-Submarine War-fare (ASW). The aircraft were able to drop sonobuoys that radio-transferred un-derwater sounds back to the plane. The centre was responsible for maintaining handbooks with acoustic signatures and the yearly advanced course for sonar op-erators.

At first things were overwhelming and new to me, but I am still thankful to Ir. Bert Houtman for taking me on board and explaining the practical side of signal processing. After five years of mathematics at the university, I finally came to enjoy the beauty of Fourier transformation in practical applications. I also owe much thanks to LCDR Jan Bakker and LT Jaap Viergever (ret.) for learning me the tricks of the trade of classifying ships by their acoustic signatures. We had many good times during our trips abroad, and we analyzed and discussed many weird underwater sounds. I also have good remembrance of the many Friday after-noons in the lab when we operated that big orange machine called FTAS. There in that cinematic setting of the “inner sanctum”, we replayed old tapes from the archives and used new analysis techniques to discover “things” never seen before. To me, the centre was an inspirational place, and in close contact with many petty

Iceland, once the “ASW capital of the world”. We had a laptop computer with our own software tied to a table next to the galley, and managed to analyze un-derwater sounds in parallel with the ageing equipment of the Orion. Shortly after that the sad news came that Valkenburg was to be closed, and the Orions to be sold. Submarine hunting was said to belong to the times of the Cold War and today both air stations Valkenburg and Kevlavik have been abandoned.

In 2004 I moved on to the Royal Netherlands Naval Academy. I thank Prof. Dr. Ir. Frans Absil who offered me the position of assistant professor in underwater systems that I held unto 2008. I also like to thank Dr. Herman Monsuur and Dr. Theo Hupkens for their friendship, advice and support in various matters. After six years of applied R&D at CODA, Prof. Absil gave me the opportunity to do scientific research in the Rapid Environmental Assessment (REA) project. I thank Dr. Jean-Pierre Hermand for his good suggestion to start working on metaheuristic optimization techniques and geoacoustic inverse problems. I thank Prof. Dr. Kevin B. Smith for scientific and personal friendship, both during and after his time at the college as a visiting professor from the U. S. Naval Postgraduate School in Monterey. As this thesis testifies, the scientific research with Hermand and Smith turned out to be quite successful in terms of journal articles, conference papers and reports.

The years I spent as teacher and researcher at the naval college were hectic. The college was reorganized into the Netherlands Defence Academy and all those years there were concerns about the research budget. Nevertheless we managed to pull off two sea trials: a small scale expedition to the Saba bank in 2006 and the much larger NATO sea trial Battlespace Preparation 2007 (BP07) in the Mediterranean sea. Both sea trials featured the deployment of hydrographic survey vessel HNLMS Snellius.

A special word of thank is for the commanding officers of HNLMS Snellius, LCDR Diederik van der Plas (2006) and CDR Ir. Robert-Jan van den Oord (2007), the hydrographers LCDR Onno Grefen and LT Suzanne Duineveld and the crews of HNLMS Snellius in 2006 and 2007. Another word of thank is to LT Jurrien van Kasperen and his Very Shallow Water team for the deployment of the REMUS vehicles. I am convinced that the participation of LCDR Drs. Ren´e Dekeling was an important factor in making BP07 a scientific succes. Ren´e, thank you for your invaluable support in organizing workshops and sea trials, for sharing laughter and disappointments, and not to forget sharing remarkable recordings of Killer Whales and other underwater sounds that impressed our colleagues and other audiences.

A special word of thank is to LT Tjarda Wilbrink. Apart from being an appre-x

ciated colleague, you might be the only one among my friends to really understand what my current and previous jobs are about.

In the summer of 2008 I moved on to the Planning and Decision Support (PADS) department at the Centre for Automation of Mission Critical Systems, CAMS-Force Vision. The centre is a software house that is part of the Defence Materiel Organisation, and as such is responsible for the operational software on her majesty’s ships. I was to replace CDR Dr. Ir. Fok Bolderheij and carry on with his work on sensor management. Fok, I’m very happy with your involvement and the strong team that we are part of. A word of appreciation to those who are or were members of the PADS team: Dr. Ir. Rick van der Meiden, LT Dr. Ir. Wilbert van Norden, LT Krispijn Scholte, Michiel Scholten, LT Ir. Tanja van Valkenburg–van Haarst, and Ir. Mark Zijlstra. It is thanks to the cheerfulness and dedication of you guys that “we do the weird stuff.”

I further like to thank Theun van Dijk and Drs. Marc van Velzen, heads at CAMS Force Vision, for giving me the opportunity to finish my research in acous-tics and optimization techniques, and reporting about it in this thesis.

I thank Prof. Dr. Ir. Frans Groen and Prof. Dr. Drs. Leon Rothkranz for acting as my promotor and co–promotor, and for your vigilant remarks on the draft of this thesis. I further owe thanks the promotion committee, for reading and commenting this thesis. Your contributions are being much appreciated.

Thanks to my friends and family, for your love and attention, friendship, and just for being there. I owe special thanks to my English sister–in–law Catherine, for proofreading this dissertation. And the final word is to my dearly loved wife San: thank you for your patience and all. I don’t know how, but somehow you manage to live with me, and accept all of my noisy or expensive hobbies. Now this one has come to an end... what shall I do next?

Franeker Vincent van Leijen

Spring 2010.

Chapter 1

Introduction

One could say that the ocean depths are the final frontier of our planet. As human beings we appear to enter this inaccessible vicinity by exception only. There is a strange attraction that draws humankind to the depths, adventurers in search of arcane creatures or hidden treasure. And much there is to be found...

The continental shelf conceals many natural resources. The offshore industry undertakes a global search for hydrocarbons, such as oil and natural gas reserves. In some places the seafloor is dredged for sand, to raise artificial islands, like off the coast of Dubai. In the North Sea, the sea bottom has become the foundation for wind farms - and potentially for an airport, in the event that Schiphol gets decided to be re-located at sea. All this human activity requires dedicated instrumentation to assess the character of the seabed.

For military operations, uncertainties about the environmental situation could have a major impact on the command and control of naval forces. Mission planning and decision making on the course of action demand awareness of the coverage of available sensors. The dominant sensor for the underwater domain is sonar, a form of acoustic sensing. To assess the performance of sonar, knowledge is required about the sensor system, the target and the present and future state of the environment.

The environmental information that has an effect on sonar performance is twofold. One part concerns ambient noise, and is connected to wind, rain, marine life, distant shipping, and so forth. The other part concerns the propagation of underwater sound, and involves acoustic properties of the water column, water surface and the sea bottom. Most of these properties can be measured or obtained from historical databases, but things are more complicated with the sea bottom.

1.1

Sea bottom characterization

For the exterior of the sea bottom, various instrumentation exists. Remote sensing with satellites can provide low to medium resolution images as in figure 1.1. Mod-ern hydrographic sensors include multi beam echo sounders (MBES) to construct high resolution depth charts, and side scan sonar (SSS) for acoustic imaging of the sea floor. A recent example of the use of SSS imaging is the search for debris of a crashed aircraft (June 2009).

Figure 1.1: The exterior of the Saba bank as imaged with a Landsat satellite, southwest of Saba island. (Source: Millennium coral reefs landsat archive, NASA, http://oceancolor.gsfc.nasa.gov/cgi/landsat.pl)

There is no direct method to measure the interior of the sea bottom. An extensive campaign of seismic profiling might produce an image of separate layers in the sea bottom, but does not uncover the character of various sediments. Taking grab samples or cores is often not possible or of limited value for the application at hand.

An indirect method to obtain sea bottom properties is called geoacoustic

inver-sion. Many scientific experiments with this technique involve a range of equipment.

1.2. Geoacoustic inversion 3 bottom and a dense array of hydrophones is then used to collect vast amounts of data that contain reflections from sediments and sub bottom. Extensive analysis of the data provides acoustic properties of the sea bottom, such as density and sound speed, that are classifiers of the type of sediment.

The Rapid Environmental Assessment (REA) research project of the Nether-lands Defence Academy [2] aims to bridge the gap between laboratory and oper-ational application of geoacoustic inversion techniques. The project studies the use of sparse and portable sensor arrays to rapidly assess sea bottom properties in shallow water environments.

1.2

Geoacoustic inversion

A geoacoustic inversion technique basically analyses bottom-reflected sound to assess acoustic properties of the sea bottom, such as density and sound speed.

1.2.1

Research challenges

Conventional experiments with geoacoustic inversion usually involve a wide range of equipment to produce and receive underwater sound. The most important tools for inversion are sonar transmitters, numerous hydrophones, and synchronized recorders for data acquisition. As a scientific technique, geoacoustic inversion is a proven method, but for an operational system there are some problematic issues. For a good assessment of geoacoustic properties, the underwater sounds used need to penetrate deep into the sea bottom. This can be achieved by using sonar transmissions of high power and low frequency, like below 2 kHz. Because of the weight of the equipment and the energy consumption of prolonged transmissions, it is customary to transmit from a mothership. For military applications this can be a problem, particularly when the environmental assessment involves denied areas or covert operations. Another issue with sonar transmissions concerns underwater noise pollution. Marine mammals are known to be vulnerable to loud underwa-ter sounds [110, 99, 95]. And even if sea life has been taken into consideration, e.g., with appropriate mitigation measures [11], the attention of environmentalist groups can be an unwanted side effect to the use of sonar transmitters.

The influence of noise is a sonar problem that can be reduced by using many receivers. Therefore a dense receiver array might contain 32 hydrophones, or more. For application in a remote sensing concept, e.g., with many drifters, the acoustic data need to be transmitted wirelessly. The issue then is that a dense array might produce more data than the available bandwidth permits to transmit.

Once the data have been gathered at sea, parameters of a model of the sea floor can be found in the lab with inversion techniques. The result of such an analysis

could be a layered model of the sea bottom, which describes acoustic properties such as density, attenuation and sound speed for the various layers. The inverse process is based on repeated evaluations of simulated propagation of underwater sound [126]. Therefore the method usually takes a long time, sometimes days or more which is not very efficient, when information is needed about the accuracy of the obtained model of the environment. The improvement of both efficiency and accuracy is investigated in chapter 7 with a careful comparison of metaheuristic optimization techniques.

1.2.2

Research questions

Based on the challenges that come with geoacoustic inversion techniques, the fol-lowing research questions have been formulated:

1. What alternatives are there for the use of sonar transmissions in geoacoustic inversion, and more particularly, how can shipping sound be used as a sound source of opportunity?

2. How can the large volume of acoustic data that is required for geoacoustic in-version be controlled, and what are reasonable proportions for an operational system?

3. How can the accuracy of inverted models of the sea bottom be assessed? 4. When an inverse problem is given, how does one select and configure a

meta-heuristic optimizer for the best performance of the inverse process?

The work that is documented in this thesis began as part of the Rapid Environ-mental Assessment (REA) research project of the Netherlands Defence Academy. The project gave rise to two sea trials: in 2006 on the Saba bank [76] and the Battlespace Preparation trials of 2007 in the Mediterranean Sea [78]. Both trials were supported by the Netherlands Hydrographic Service, which participated with hydrographic survey vessel HNLMS Snellius. To answer the first two questions, the REA project studies the use of shipping sound and sparse receiver arrays. Apart from PhD work of Matthias Meyer on adjoint methodology [88, 89], the last two questions have been researched using metaheuristic optimization schemes.

1.2.3

Methodology

The collected data contain many underwater recordings of platforms such as HNLMS Snellius, NRV Leonardo, the REMUS autonomous underwater vehicle, and recreational boats. Next to the recorded shipping sound, other environmental

1.3. This thesis 5 measurements have been made that characterize the water column (sound speed profile) and the sea bottom (seismic profiling). The environmental data either sup-port the inverse process, or provides some degree of ground truth for the inverted model of the environment.

The data are analyzed with inverse techniques that are explained in chapter 3. In short, the analysis concerns phases of selection, pre-processing, geoacoustic inversion and uncertainty analysis of the obtained environmental model. At first a selection is made of good or interesting recordings that are documented well enough to enable inversion. The selection also identifies what frequencies and range intervals are useful for inversion. Then with the pre-processing phase, the large volume of selected acoustic recordings for the separate hydrophones is translated and reduced into a number of cross spectral matrices. For each frequency and range between source and receiver, such a matrix correlates the receptions for combinations of individual phones. The phase of geoacoustic inversion then aims to find an environmental model that best explains the observations. And the last phase aims to assess the accuracy of the obtained model by providing posterior probability distributions.

In addition to the acoustic part of an inverse problem, this thesis studies the optimization part of the process. According to literature, various metaheuristic methods have been applied to deal with the optimization part of geoacoustic in-version. To answer the question about the selection and configuration of the best metaheuristic, four methods are compared using measured data. The measures of performance that are used are efficiency and accuracy of the inverse process.

1.3

This thesis

This thesis is based on published (and reviewed) articles of which some are included as separate chapters. To start with a further motivation, chapter 2 [72] provides the military context for research in geoacoustic inversion. The chapter that follows then provides the basics of (geo)acoustic inverse theory and a review of the relevant scientific literature.

1. Chapter four [76] reports on the sea trials that took place on the Saba bank, in 2006. If underwater acoustic sensing is a battle between signals and ambi-ent noise, why not let the ambiambi-ent noise be the signal? To find out, the self noise of hydrographic survey vessel HNLMS Snellius was received nearby on a sparse vertical array and exploited as a sound source to sense the seabottom. Passive sonar techniques were used to localize the ship, and the ships self noise was effectively used to invert a full geoacoustic model of a layered sea bottom. In addition, an uncertainty assessment was made for the obtained parameters.

2. Chapter five [78] documents a novel concept of geoacoustic inversion with the self noise of an autonomous underwater vehicle (AUV). These programmable platforms are relatively quiet and run below the surface, which makes them ideal sound sources for deployment in a discreet REA campaign. Acoustic data were gathered from a REMUS AUV, during the Battle Space prepara-tion sea trials of 2007. The inversion resulted in a characterizaprepara-tion of marine sediments.

3. To study the efficiency and accuracy of inversion schemes, various meta-heuristic optimization techniques have been studied. Chapter six [73] docu-ments how Ant Colony Optimization (for the first time) has been applied to geoacoustic inverse problems and further how the method has been used to specify the uncertainty in inversion results.

4. With the various metaheuristic optimizers that have been used in geoacoustic inverse problems, the question for the best performing method is addressed in the seventh chapter [77]. Four metaheuristics are compared: Simulated Annealing, Genetic Algorithms, Ant Colony Optimization and Differential Evolution. After proper tuning of each method it has been observed for two real geoacoustic inverse problems that the efficiencies of the population based metaheuristics are nearly the same. It is therefore concluded that proper tuning is just as important as selection of the most suitable metaheuristic.

1.4

Work not covered in this thesis

During the study of geoacoustic inversion, some work has been done on vector sen-sors. These sensors not only measure sound pressure and phase, but also yield the acoustic particle velocity. An article about non-linear beam shapes (‘hippoids’), together with prof. dr. Kevin B. Smith from the Naval Postgraduate School in Monterey (USA), was published in the Journal of the Acoustical Society of

Amer-ica [117]. Smith is the author of the MMPE propagation model and during our

work on the potential of particle velocity for acoustic inversion [75], we found that there was an error in the model with regard to bottom attenuation. The model has been updated [118, 116] and can be downloaded for free from the Ocean Acoustics Library1_.

To carry out inversions and compare various methods, the author has written a LOBSTER toolbox [71] (the Low-frequency Observation Based Sonar Toolbox for Environmental Reconstruction). This object-oriented Matlab code interfaces

1

1.4. Work not covered in this thesis 7 with variants of the KRAKEN [105] and MMPE [115] (third party) propagation models and offers a number of objective functions. For some time now, there has

been a free and open source2 _{inversion software package by Gerstoft [38] that goes}

under the name SAGA. The reason for not using SAGA is that the LOBSTER code supports inversion with acoustic particle velocity [75] and also includes more metaheuristic search strategies. Apart from conventional metaheuristics such as Simulated Annealing and the Genetic Algorithm, implementations of Differential Evolution and Ant Colony Optimization are included.

2

Chapter 2

Operational Context

1

1990. Somewhere above the Atlantic Ocean a lone maritime patrol aircraft is on a mission of anti-submarine warfare. Directed by intelli-gence from at the time highly classified sound surveillance systems, the crew is ordered to monitor a designated area with a field of sonobuoys. The first buoy to hit the water is an expendable bathythermograph (XBT). The device samples the temperature profile in the water col-umn and the operator quickly derives a sound speed profile. After the propagation conditions of underwater sound are reviewed for tactical consequences a pattern of sonobuoys is dropped with favorable spacing and depth settings. It does not take long before a contact emerges on one of the outer buoys...

2000. An expeditionary force of various surface ships is about to enter a coastal area. To predict the performance of various acoustic sen-sors, the water column is sampled with a bathythermograph. Details about the local seabed conditions are unknown and the sonar perfor-mance model is then run with global parameters from an environmen-tal database. As a result a mine hunting operation takes twice the time that was actually needed to clear the area of mines because of non-optimal sonar settings. Meanwhile a bottomed submarine remains effectively hidden in the reverberation, waiting for the main force to close in.

1

Published as: Leijen, A.V. van, “From the lab to the sea, acoustic sensing in uncertain environments”. In: NL ARMS Netherlands Annual Review of Military Studies 2008, Sensors, Weapons, C4I and Operations Research, edit 2008. (Th. M. Hupkens and H. Monsuur, eds.), Den Helder, pp153-163, Nederlandse Defensie Academie, Breda, 2008.

2010. An amphibious force is to land on a beach that has been selected from satellite imagery. A discreet campaign of rapid environmental as-sessment then reveals the presence of a muddy sediment layer. Mud is ideal sediment for self-burying mines and means that the beach is not accessible for heavy armored vehicles. With this secretly gathered in-formation a new area is selected and the amphibious operation unfolds itself as an unopposed landing.

2.1

Introduction

When expeditionary forces enter shallow or confined waters, the environment has a great influence on the performance of platforms, sensors and weapon systems. For this reason, environmental knowledge is regarded as one of the key factors in making decisions on the course of action and asset allocation [1]. The examples above illustrate how the right level of battlespace information enables effective operational planning and mission execution [96]. For naval oceanography the main objective is to provide forces with a competitive advantage over adversaries by exploiting the current and future state of the environment.

The Royal Netherlands Navy (RNLN) possesses various sensor performance models and tactical decision aids for its combat systems. Many environmental input parameters can be provided in advance by the Netherlands Hydrographi-cal Office (nautiHydrographi-cal charting) and the METOC office of CODAM (environmental briefing dockets and databases). Some parameters are measured or sampled at sea, such as weather conditions, water temperature and underwater ambient noise. For expeditionary operations it is likely that a priori knowledge about the environ-ment is limited and outdated. Therefore there is a need for tools that enable hydrographers or naval oceanographers embedded with the forces to collect and validate environmental information at sea [7].

2.2

Environmental information for naval warfare

Each mission type has its own operational need for environmental information in terms of data accuracy and spatial and temporal resolution [96].

In Anti-Submarine Warfare (ASW) it is crucial to know how well sonar per-forms. Environmental information enables the prediction of acoustic detection ranges on submarines and surface ships. For the open oceans, the propagation of sound is foremost determined by properties of the water column, such as temper-ature and salinity. Shallow waters are often characterized as an unpredictable and complex environment. For sonar, the performance is determined by many factors,

2.2. Environmental information for naval warfare 11 such as tides, currents, wind, rain and reflections from the sea surface and complex bottom structures. The essential data for propagation modeling are often incom-plete, and therefore the daily predictions of sonar performance are seldom close to reality. In addition, water conditions and sound speed profiles change during the day due to temperature changes and weather conditions.

Mine Counter Measures (MCM) also depend on various oceanographic factors [98]. The bathymetry (charted water depth) and the acoustic properties of the medium determine how well mine hunting sonar will perform. Acoustic detection of mines is limited by sea bottom reverberation. A rough estimate of the sediment type is sufficient to indicate the underwater visibility and the likelihood of mine burial, but coastal mechanisms of river outflows and sediment transport makes that gathered information gets easily outdated.

In amphibious operations the shallow water bathymetry determines how close to the coast support ships and landing craft can safely get. Important informa-tion about the beach, such as trafficability and the slope, can be found with an autonomous underwater vehicle during high tide. In general, the characterization of the sediment and bathymetry for amphibious purposes permit a rough level of detail.

It is easily overlooked that the shallow character of the littorals can also be ex-ploited. A rough approximation of the underwater battlespace is already valuable for a tactical exploitation of the environment (TEE). A submarine can tactically exploit the reverberant properties of the sea bottom or be positioned to benefit from the directionality of ambient noise. TEE concerns easy rules of thumb and needs only rough estimates about the environment, as in “active sonar performs better in down slope direction than up slope”. Environmental knowledge with a high level of detail enables passive source localization with techniques known as Matched Field Processing (MFP). The advantage of MFP over conventional Doppler arithmetic is that the latter requires movement of the target and infor-mation about the zero-frequency and MFP does not. On the other hand MFP depends on a propagation model that operates on accurate environmental data. The technical character of MFP further calls for a highly skilled and well-instructed operator.

Various levels of battlespace information can be obtained with a campaign of rapid environmental assessment (REA). The aim is then to measure, analyze and evaluate relevant properties of the environment in order to establish a recognized environmental picture (REP). The intention is that forces have a shared awareness of the battlespace and that they have it in time. Since 2004 the RNLN operates two hydrographic survey vessels HNLMS Snellius and HNLMS Luymes. These modern ships are fitted with an extensive sensor suite for digital charting and further tasks of military hydrography [81]. For covert REA the navy may call upon

Special Forces and submarines of the Walrus class, as was demonstrated during the exercise Joint Caribbean Lion (2006). Like many other navies within NATO, the RNLN is still in transformation from a blue water force to an expeditionary brown water force. Currently not all important environmental data for shallow water operations can (rapidly) be gathered.

2.3

Acoustic sensing in shallow water

The environmental factors that impact acoustic sensing capabilities are manifold. Shallow bathymetry and underwater obstacles may hinder the use of long towed arrays. The presence of divers or marine wildlife may call a halt to mid or low frequency sonar transmissions. Coastal ambient noise includes an abundance of directional sound sources with manmade or natural origins. The focus of this chapter is on those parameters that influence sound propagation, or more specific: the transmission loss due to sea bottom interaction.

The water column is usually characterized by measuring conductivity (to esti-mate salinity) and temperature as function of depth (CTD sampling). To study the effects on sound propagation according to Snell’s law of refraction [128], a sound speed profile can be derived from these measurements, e.g. using an empir-ical formula [86]. In deep water the propagation of sound is determined by this profile only; in shallow water many more parameters are involved.

Various definitions can be given for shallow water [130]. From an acoustical point of view shallow water is found “when each ray from the source, when con-tinued long enough is reflected at the bottom” [17]. Another definition is “a water depth in which sound is propagated to a distance by repeated reflections from both surface and source” [128]. To be practical, shallow waters are often said to be on the continental shelf and bordered by the 200 m contour line.

Unlike the water column, the sea bottom cannot rapidly be characterized by insertion of some sampling device. Nevertheless, sound waves easily propagate in and out of marine sediments. Received signals can then be analyzed with geoa-coustic inversion techniques to back trace ageoa-coustic properties of the ocean bottom from the spatial and temporal structure of sound pressure fields. Experiments for seabed assessment utilize a sound source and a receiver array for a one-time observation at sea of bottom reflected sound. A geoacoustic inversion process is then initiated to find a parametric description of an environmental model in terms of sediment layering properties and geoacoustic parameters such as sound speed, density and attenuation.

2.4. REA as a research project 13

2.4

REA as a research project

The Rapid Environmental Assessment (REA) project at the Netherlands Defence Academy aims to understand the nature and impact of environmental conditions on the propagation of sound in shallow waters and sedimentary bottom types [2]. As such, the project aims at the development and validation of acoustic remote sensing systems and inversion methods. The result is a reliable and rapid environmental assessment of shallow water areas in support of various mission types.

A central question in this thesis is: what acoustic information about the seabed can be obtained from bottom-reflected shipping noise? The feasibility of geoacous-tic inversion with non-traditional sound sources has been studied with data from two sea trials.

During SABA06, a Caribbean survey of the NL Hydrographic Office (NLHO) in 2006, small-scale experiments in a remote and isolated area were conducted from hydrographic survey vessel HNLMS Snellius [76]. To acquire data and test the concept of inversion with shipping sound, the Snellius served as the sound source. The trials demonstrated a rapid deployment of sensors and equipment and resulted in a well-documented acoustic dataset. A unique achievement is that geoacoustic inversion was performed while the team was on board and an environmental debrief was provided, all within 24 hours.

The BP/MREA07 sea trials of 2007 were a much bigger effort [34]. Together with the NLHO, the NATO Undersea Research Centre (NURC) and various other institutions a shallow water area in the Mediterranean Sea was surveyed with a multitude of sensors. The overall aim of the trials was to demonstrate the concept of naval battlespace preparation by providing a recognized environmental picture (REP). The dynamic and coastal area includes deeper water (200 m), very shallow water (30 - 10 m), a harbor approach and the beach. The multi sensor approach makes it possible to validate results of geoacoustic inversion experiments with nontraditional sound sources under various circumstances.

2.5

Discreet REA

The preparation of some remote coastal area with an overt REA campaign is in obvious conflict with the concealed nature of submarine and amphibious opera-tions. Therefore environmental assessment in support of military operations will often be a discreet endeavor. Covert assessment of the sea bottom calls for clandes-tine deployment of sound sources and receiving sensors. The REA project studies various ways in which signals with geoacoustic information can be received. Re-ceiving sensors can be inserted in denied areas by acoustic-oceanographic buoys

and drifters that exploit the local currents [51]. A drifting buoy field covers a large area and is not hindered by the presence of mines, yet radio transmissions can be intercepted.

During the scientific experiments SABA06 and MREA07, data were also gath-ered with a sparse vertical array deployed from a rubber boat. The concept can easily be translated to an operational context when acoustic-oceanographic sen-sors are deployed and recovered by Special Forces. The feasibility of this concept has recently been demonstrated with covert hydrographic reconnaissance during exercise Joint Caribbean Lion. More information about oceanography and Naval Special Warfare can be found in [97].

Front-line units such as autonomous underwater vehicles (AUVs) and sub-marines are already fitted with sensors for intelligence, surveillance and reconnais-sance (ISR). Typical but sensitive intelligence missions can easily be extended with an environmental component to make dual-use of ISR sensors [96]. The approach also provides a capability to make dual use of past intelligence missions. In this case archived sonar data from ill-documented areas can be analyzed again, but now for environmental purposes.

2.6

Sound sources of opportunity

For a thorough assessment of bottom properties acoustic signals are required with low frequencies that penetrate deep into the bottom. Shipping sounds are also low, with frequencies from 50 Hz up to 2 kHz. One of the reasons to launch a REA campaign is to aid in the prediction of passive acoustic detection ranges of ships and submarines. The conventional method relies on active sonar transmissions. There are however some practical down sides to the active approach. The high power consumption of low frequency systems limits the endurance of remotely deployed systems such as drifters, buoys and autonomous underwater vehicles [96]. And assessment with loud transmissions and low frequency is also more of an overt approach. An alternative is to utilize sound sources of opportunity. A military motive to do so is that (counter) detection is avoided and environmental assessment can be done in a discreet manner. Another motivation is that the method inflicts a minimal impact on divers and marine wildlife [76].

Coastal waters allow for a high concentration of human activities and as a result shallow waters are a noisy environment. With the right sensors there are many ships that can act as a sound source of opportunity. At some distance from the coast there is merchant traffic in designated shipping lanes, augmented by fishing vessels and offshore suppliers. Closer to the coast there are the ferries and the recreational boats. In times of military conflict various types of naval vessels may patrol coastal waters.

2.7. Applications 15

Figure 2.1: Sound sources of opportunity used so far in the REA project: HNLMS Snellius, NRV Leonardo, REMUS AUV and recreational boats.

The REA project has lead to geoacoustic inversion with cooperative surface ships, unmanned underwater vehicles and even non-cooperative recreational boats; the platforms are pictured in Fig. 2.1. For the Saba bank, geoacoustic inversion with received shipping noise from HNLMS Snellius revealed a very thin layer (15 cm) of sandy sediment over a subbottom of calcareous rock [76].

The BP/MREA07 sea trials featured experiments with various sound sources of opportunity. When opportunities occurred these sources behaved as planned, as in the experiments with self noise from HMLMS Snellius and NRV Leonardo [34]. During a particular run that focused on the self noise from the relative quiet REMUS AUV [69] there was much interference from the weekend traffic. But then these recreational boats turned out to be fantastic sources of opportunity [70] and demonstrated the strength of the inversion method in using non-cooperative sound sources for a rapid and reliable characterization of the local sediment.

2.7

Applications

2.7.1

Basic acoustic sensing

Underwater acoustic sensing is a battle of the decibel that is about hearing without being heard. Quieting of submarines and increased ambient noise in coastal areas have resulted in a general decrease of acoustic detection ranges in anti-submarine warfare.

For passive sonar in shallow water significant gains are possible when sensors with a vertical aperture are combined with (near) real-time signal processing tech-niques [97]. Environmentally adaptive algorithms may combine a track-before-detect approach with time-reversal algorithms in order to focus acoustic waves. Coastal ambient noise is highly directional in bearing and azimuth and this is where adaptive beam forming with arrays of directional vector sensors [117] can contribute even more. For passive sonar, environmental adaptive algorithms pro-vide cleaner displays and easier track identification. The potential for active sonar is strong mitigation of reverberation.

For expeditionary missions, relevant oceanographic data are often under sam-pled in space and time. Therefore, and to further adapt deep-water procedures for the littoral zone, the logical addition to sampling of the water column with expendable bathymetrical thermometers, is to assess seabottom properties with geoacoustic inversion techniques, as the U.S. Naval Oceanographic Office (NAV-OCEANO) already practices [96]. The required resolution and acceptable level of environmental uncertainty depend on the range of mission types that naval forces fulfill.

Significant advances in acoustic sensing are possible, yet they come with a price. Apart from the integration of dedicated shallow water sensors and envi-ronmentally adaptive processing, education and operational training remain a key factor. Acoustic sensing has never been easy, and a lack of education can easily degrade sensor performance. But when the skilled hands of a ‘techno sailor’ are provided, major improvements in sonar performance are still possible.

2.7.2

Advanced acoustic sensing

Littoral waters are often said to be a harsh environment for sonar, but the presence of the seabottom can also be a highly beneficial feature. The advanced concept of matched field source localization exploits bottom reflections of underwater sound and has various advantages over basic sonar systems.

• The technique is passive, which means that naval forces can operate covertly. • In contrast with basic sonar, the technique provides the depth of a contact. Obviously, this is a very important classifier to distinguish a surface vessel from a submerged platform.

• Localization with basic systems of passive sonar is usually the result of target motion analysis (TMA): an extensive effort of analyzing Doppler shift or bearing rates. But considering the shallow water environment and quieting of modern submarines, continuous sensor coverage is something from the past. Matched field processing operates on a single intercept, that can be of a very short duration. The technique even has potential to locate stationary sound sources, such as a bottomed submarine.

Nevertheless, the capabilities of matched field source localization are limited in the absence of proper environmental knowledge. When relevant environmental information is gathered with geoacoustic inversion techniques, matched field pro-cessing can be a fast method to find the range and depth of a sonar contact. As an alternative, this dissertation demonstrated to localization of an autonomous underwater vehicle by inversion of both the position and relevant environmental parameters.

2.7. Applications 17 To demonstrate the influence of environmental knowledge on matched field source localization, the acoustic intercept of a recreational boat has been stud-ied [70, 72]. Chapter 5 describes an experiment from BP07 of environmental assessment based on geoacoustic inversion with the self noise of an autonomous underwater vehicle. During that experiment there were many recreational boats that left Castiglione della Pescaia. Fig. 2.2 shows how one of these boats is local-ized with matched field processing for five tones from the inboard diesel engine, and given two different environmental models. When bottom properties from a military environmental database such as ASRAP are used, with a rough spatial resolution, the method fails to correctly identify the source position. MFP with the bottom model from the AUV inversion resulted in Fig. 2.2.b with one clear spot at the surface and 920 m away from the receiver. This example clearly illustrates how proper environmental information enhances acoustic sensing capabilities.

2.7.3

Assessment of buried waste

Ocean dumping of waste is a forbidden activity by international conventions. Nev-ertheless, there are many dumpsites from the past and marine accidents do still occur, next to the illegal dumping of natural and artificial wastes. Toxic dump-sites are in general not well documented, due to the covert nature of industrial and military dumping operations. An example is found in the Baltic Sea, where according to existing documentation, at least 65,000 tons of toxic chemical muni-tions have been dumped in the post-World War II years [14]. In an attempt to build instrumentation to assess marine dump sites, various authors [59, 67] have considered geoacoustic inversion as a means to investigate buried waste.

During an acoustic sea trial in the M¨oja S¨oderfj¨ard dumpsite [93], a small box

of 1.3 m _{× 0.3 m × 0.3 m was positioned half-buried in the seabed. At first,}

side-scan sonar was used to locate the box with a bottom depth of 75 m. The next step was to use a transmitter that was mounted on an ROV, and to ping the box from nearby with frequencies between 2 kHz and 20 kHz. A separate sparse and vertical receiver array was used to measure the multiple-aspect scattering of the box. The geoacoustic inversion resulted in the acoustic impedance of the contents [60] (density and sound speed), next to range, depth, roll, pitch and yaw of the box.

This application of buried waste assessment deals with a search space of these seven parameters. The performance of the applied global optimization techniques (differential evolution and genetic algorithm) could most likely be improved with the tuning process that is presented in this dissertation.

Range (km)

Depth

(m

)

a. MFP with bottom from database

b. MFP with locally inverted bottom properties

Range (km)

Depth

(m

)

Figure 2.2: The benefit of environmental information for source localization with matched field processing. Pictured is the mismatch surface for depth and range. The engine noise of the recreational boat should be found just below surface, and is identified with minimal mismatch Φ, denoted with the color black. The upper image (a) is based on uncertain bottom properties drawn from databases such as ASRAP and does not give a clear solution. The lower image (b) based on the covert REA mission with the AUV has one clear (black) detection of the recreational boat at the surface and 920 m from the receiver.

2.8. Conclusions 19

2.8

Conclusions

Naval oceanography is regarded to be a key factor in underwater warfare. Vari-ous mission types have their own operational need for environmental information. Since the end of the Cold War, the operational areas have shifted from open ocean towards the littoral zone. The Royal Netherlands Navy has the ambition to sup-port expeditionary operations, that could be executed together with the army or air force (‘joint’) or other nations (‘combined’). Rapid Environmental Assessment is a general concept of gathering timely and relevant environmental information about the area of operation. The proposed use of true sound sources of oppor-tunity, such as ferries, recreational boating or military patrol boats, provides the navy with the capability of discreet rapid environmental assessment of remote and denied areas.

This dissertation aims to find out what acoustic information about the seabed can be obtained from bottom-reflected shipping noise. The feasibility of geoacous-tic inversion with non-traditional sound sources has been studied with data from two sea trials. During the experiments on the Saba bank (2006) the concept was demonstrated with a short REA campaign in a remote and isolated area. With the BP07 sea trials in the Mediterranean Sea of 2007, the covert battlespace prepara-tion concept was further experimented with and complemented by a multi-sensor survey of various bottom types and water depths to further validate geoacoustic inversion methods. Before these activities are further reported, the next chapter gives a basic introduction in acoustic inverse theory.

Chapter 3

Acoustic Inversion

This chapter provides a brief introduction in inverse acoustic sensing. Just like basic acoustic sensing, inverse theory deals with a sound source, a propagation medium and receiving sensors. The scientific literature will be surveyed with regard to sound sources other than sonar transmissions, modeling of the shallow water sound propagation, sparse configurations of receiving sensors, and inversion schemes that are based on optimization with metaheuristics.

3.1

Introduction

Introductions to underwater acoustics are given in various handbooks. The first handbook to be mentioned is undoubtedly Principles of underwater sound by Urick [128]. The book is among the oldest of its kind (first edition 1967), with chapters that can be traced back to declassified U.S. Navy studies from World War II, of which each addresses a single sonar parameter. For many years, the book has been used to educate naval officers at the Royal Netherlands Naval College [68] and the work can still serve as a thorough introduction. Among the contemporary introductions are works of Lurton [82] and Medwin et al. [87]. These authors address a wide range of military and modern civilian applications (Lurton) and present-day scientific studies on bio-acoustics, ocean dynamics and the ocean bot-tom (Medwin et al.). With a minimum of technical jargon, Richardson et al. [110] concisely document underwater acoustic principles and effects of man-made noise on marine mammals. Other works focus more on fundamental mathematical and physical principles, such as Burdic [19], Kinsler [61], and Brekhovskikh [17]. A practical handbook for engineers is Urban [127], or with less stature that of Waite [131], with many practical figures and rules of thumb. And finally, various concise booklets have been compiled, such as a pocket handbook by Bradley [16] or the compendium of definitions by Ainslie [3].

3.1.1

Active and passive sonar

For military applications, basic acoustic sensing comes in two flavors: active and passive sonar.

Active sonar is based on the transmission of a signal that is meant to reflect from a target. The echo is to be discriminated from either ambient noise or from reverberant receptions, most often due to bottom reflections. Active sonar has most in common with radar. The principal difference is that radar is based on radio waves (that are quickly attenuated in water), while sonar depends on sound pressure waves. Nevertheless, the radar performance equations [114] and the active sonar equations [128] are essentially the same [79].

Passive sonar is a receiving-only system and concerns the analysis of sounds that are received from ships and submarines. Every platform has an acoustic sig-nature, which is a composition of mainly machinery noise, propeller noise, and flow noise [111]. When the reception is strong enough, a sonar operator can separate signals from ambient noise aurally or by frequency analysis.

The performance of a sonar system can be expressed by two measures, these are the (maximum) detection range and the probability of detection. When the detection range is given, the sonar equations can be used to work out the prob-ability of detection. And vice versa, when a minimum detection probprob-ability is specified, the equations are used to provide the maximum detection range. For a given frequency, the sonar equations contains the relevant knowledge of signal and noise. The passive sonar equation [128] specifies the performance of detection on shipping sounds, and can be formulated as

SL - TL = NL - DI + DT (3.1)

where all parameters are expressed in decibels.

The left hand side of the sonar equation is the received signal strength. This part consists of the source level (SL) of the shipping sound, which is reduced by a transmission loss (TL). The source level of a point source in free space is defined as the sound pressure level measured at 1 m from the source [111]. Shipping noise is a combination of many sound sources, which are not radiating in free space but merely act as dipoles. For practical use, shipping noise can be treated in the far field as a point source with a complex radiating pattern. The transmission loss expresses the loss (in dB) during the propagation of the signal towards the receiver. A simple example to calculate TL in free space is with the mechanism of spherical

spreading, in which case TL = 20 log₁₀R with R the range in m.

The right hand side of the equation is the effective noise level, and this part is determined by the received (ambient) noise level (NL), the directivity index (DI), and the detection threshold (DT). The ambient noise is due to factors such as wind, rain and distant shipping [129]. Ambient noise can either be measured at sea or

3.1. Introduction 23 estimated from factors as wind speed and sea state, e.g. using the Wenz-Knudsen curves [133, 63] that are printed in virtually any of the above mentioned acoustic handbooks, and also in appendix A. Updates on Wenz and Knudsen are found in an APL report from 1994 [8]. The directivity index is based on the directional sensitivity of the receiving sensor, and this property effectively reduces the noise level. A receiver array with a large aperture results in a large DI and much noise suppression. The DT finally is a function of probability of detection and false alarm, and expresses the signal to noise ratio, which is valid at the threshold of the specified detection probabilities.

To determine the performance of a sonar system, the sonar parameters need to be gathered from intelligence (SL), direct measurement (NL), and calculation (DI and DT). In deep water the calculation of TL depends on spreading loss, attenua-tion and refracattenua-tion in accordance with Snell’s law. As such the only environmental information that is needed is the sound speed profile in the water column. In shal-low water more information is needed, as sound propagates in complex interaction with the seabottom. Therefore, to determine the TL in shallow waters, this thesis aims for means that complete the environmental model with acoustic bottom prop-erties. Such an environmental model is input to a propagation code, which in turn calculates the transmission loss and completes the prediction of sonar performance using the sonar equations.

3.1.2

The environmental model

For both active and passive sonar, acoustic sensing is a battle of the decibel. Detection by sonar depends on the balance between received signals strength and the effective levels of noise or reverberation. The sonar equations express this balance in terms of the precise and individual contribution of the sensor system, the medium and the target. Figure 3.1 illustrates these components for a passive sonar scenario. In order to listen to underwater sound from HNLMS Snellius, a receiver array is deployed from a small boat. Signals of various acoustic frequencies propagate through the water column and the sea bottom. In the water column the propagation is determined by the sound speed profile and Snell’s law, as will be explained in section 3.5. Sound speed in water can be derived from water temperature, salinity and water pressure at the given water depth, some empirical equations are discussed in appendix B. In this thesis we focus on propagation loss due to the properties of the sea bottom. The propagation loss in Figure 3.1 displays a complex pattern, which is due to mechanisms such as spreading loss, absorption and reflection on the layer of sediment and the subbottom.

Various properties of the underwater environment have an influence on the propagation of sound [128]. An obvious parameter is the speed of sound c (in m/s), which is a property of the medium. The sound speed defines the relation

Sound source 30 m 50 m c (m/s) 1500 1600 c = 1520 m/s c = 1580 m/s cs= 625 m/s ρ = 1.5 g/cm3 α = 0.06 dB/λ ρ = 1.8 g/cm3 α = 0.15 dB/λ αs= 0.3 dB/λ WATER SEDIMENT SUBBOTTOM Receiver array 0 km 1 km

HNLMS Snellius Small boat

Figure 3.1: Scenario of passive acoustic sensing with a sparse vertical receiver array in a shallow water environment. The listed environmental parameters are an example for a layered seabottom. The Transmission Loss is plotted as a function of depth and range.

between the acoustic frequency f (in Hz) and the acoustic wavelength λ (in m) by c = f λ. Another important acoustic property of marine sediments is density ρ

(in g/cm3_{), that determines the acoustic impedance ρc and reflective properties of}

marine sediments (the physical principles of reflection and impedance according to Rayleigh will be discussed in section 3.5 using equations 3.3 and 3.4). Attenuation α (in dB/λ) is the damping of amplitude due to mechanisms such as absorption. Sound propagation in water concerns compressional waves. For solid media, sound also propagates in shear waves. In this work sound speed c and attenuation α relate to compressional waves; for shear waves the sound speed and attenuation

are noted as cs and αs. As an example, some realistic values for these parameters

are given in Figure 3.1. The shown environmental model describes a shallow water environment (30 m depth) with a sediment layer of 20 m that covers a subbottom. An environmental model that characterizes the local medium is highly bene-ficial to optimize a sonar system for operation in a given oceanic situation [31]. When the environmental model is used to calculate propagation losses, the sonar equations provide tactical information (such as probabilities of detection and counter detection) to plan strength and frequency of sonar signals and optimal sensor po-sitioning.

con-3.2. Inverse acoustic sensing 25 cepts. Active and passive sonar are direct methods that rely on direct observation of underwater sound. An advanced concept like inverse acoustic sensing is different in being an indirect method that aims to find the plausible physical conditions, like a source position, that explain a certain observation of underwater sound.

3.2

Inverse acoustic sensing

A brief introduction to inverse acoustic sensing will be given next. Principal ref-erence books and tutorial texts are written by Tolstoy [126], Munk et al.[94], and Sen and Stoffa [112].

The problem of inverse acoustic sensing is to infer from precise measurement of a received underwater sound, the state of the source or the acoustic medium. Etter [31] subdivides inverse techniques that concern propagation into three categories.

Matched field processing [126] is a technique that aims to find a source position

or to characterize the marine environment. For ocean acoustic tomography [94] the intention is to describe the state of the ocean with a focus on changes in density fields (eddies, currents) or temperature (as for climate monitoring). In case of geoacoustic inversion [112, 37, 48] the result is a geoacoustic model [44] of sediment parameters and sea-floor scattering characteristics.

The starting point of model-based inversion is that the propagation of under-water sound can be modeled with a forward propagation model. In this way, assumptions about the source position and properties of the environment can be evaluated by correlating modeled propagation loss with an actual observation of underwater sound. Inversion is then a search process for those acoustic parameters that bring about the best correlation. A schematic overview of the inverse process is given in Fig. 3.2.

Inversion begins with observations at sea where underwater sound is recorded, e.g., like in Figure 3.1. Experiments involve planning and extensive logging of the experimental geometry, like GPS positions and transmitting schedules, and the environmental conditions, like sound speed profiles in the water column and the bathymetry of the area. The experiment is followed by a quick-look analysis. Experiments with high potential are then selected for further analysis. Various selection criteria exist, such as a good signal to noise ratio, the quality of the log-ging, or operational needs to assess a certain area. During the processing phase, data logged such as sonar frequencies, sound speed profiles, array configurations, and – most important – the candidate parameters that describe the source po-sition or the environment, are input to a replica model of the experiment. The virtual experiment calculates the propagation loss by means of a forward propa-gation code. The resulting replica data are correlated to the observed data by an objective function. With iterative process, an inversion scheme guides the search

Source Medium Receiver Observation Source Propagation model Receiver Replica Objective function Inversion scheme MFP GI model parameters

Figure 3.2: Diagram with the main components for Matched Field Processing (MFP) and Geoacoustic Inversion (GI).

for candidate solutions that are subject to evaluation. After a number of iterations a solution emerges that describes the source position (matched field processing) or the environment (geoacoustic inversion).

3.3

Sound sources of opportunity

Systems that acoustically assess the seabed depend on transmissions of continuous wave (CW) or frequency modulated (FM) signals of various frequencies [35]. To make sure that signals penetrate into the sea bottom, experiments with geoacoustic inversion mostly rely on transmissions of low frequency.

An known issue with underwater sound, and one that has drawn much interest from the media, concerns underwater noise pollution and possible dangers for hu-man divers [4] and marine mammals. A broad introduction on the topic of marine mammals and underwater sound is given by Richardson [110]. A selection from the many studies and reports are reports by the National Research Council [99] and [95], which functions under the U. S. National Academy of Sciences (NAS), and a report by the National Resources Defense Council [57], an international envi-ronmental advocacy group. But even if sea life has been taken into consideration, e.g., with appropriate mitigation measures [11], the use of sonar transmissions is

3.4. Receiving sensors 27 not always an appropriate option. High power transmissions could give away the position or intentions of military units, or the required energy or equipment may simply not be available. These limitations are a motivation to study inversion with sound sources of opportunity.

In an attempt to exploit sound sources of uncontrolled nature, a variety of low and mid-frequency (0.1 kHz – 6 kHz) sources of opportunity has been investigated for geoacoustic inversion. Examples of opportunity sources are ambient noise [46], different kinds of sea life [28, 124], aircraft propellers [18], and land vehicles [29]. All these sources have successfully been exploited in experiments of environmental assessment, but with these sources it is hard to plan a survey campaign. Finding the positions of sea life, aircraft and land vehicles potentially pose another problem for practical application.

An alternative line of research focuses on the analysis of bottom reflected ship-ping sound. In the absence of sonar transmitters the self noise of the tow-ship can be used, albeit a source of uncontrolled nature. An example of this

through-the-sensor approach is an experiment in the Mediterranean Sea in 2000, when the

Alliance towed a horizontal array, while the self noise of this NATO research vessel was used for geoacoustic inversion [10]. In this case the Alliance was a cooperative source in sailing a requested course and provision of GPS logging. The concept is a variation on the through-the-sensor approach as studied in the RUMBLE project [113], where sonar transmission with a low frequency active sonar (such as LFAS) are the source.

When arbitrary ships are recorded, the shipping noise is a true opportunity source. A typical signature of a ship includes numerous tones with frequencies between 20 Hz and 2 kHz, which ensures good penetration of sound into the sea bottom. Geoacoustic inversion with opportunity ships has been reported [104, 64, 21] for long range receptions, of more than 10 km away, and the use of dense horizontal receiver arrays.

For many opportunity sources the position is not known beforehand. When this is the case, the range and depth of the source are often added to the search space. For short-range receptions of shipping sound, it is possible to estimate the position from Doppler analysis. Particularly when a vessel passes the receiver, the geometry can be worked out. In some cases the Doppler-solution is accurate enough for inversion, and otherwise the inversion can include a search interval for a modest correction to the Doppler range.

3.4

Receiving sensors

Conventional geoacoustic inversion involves the coherent reception of underwater sound on many hydrophones, typically 32 or more. For inversion with transmitted

sonar signals, the receiving sensors are often placed in a vertical line array [125, 47]. Opportunity sources are more often recorded with horizontal arrays, that count as many as eighteen [10] or more [104] hydrophones. A large number of hydrophones reduces the influence of noise, but the disadvantage is in the large volume of acoustic data that need to be recorded, handled and processed in what ultimately needs to be a real time application.

A class of flexible deployable receiving systems are drifters or a field of acoustic-oceanographic buoys (AOBs) [83] that transfer the acoustic data by a wireless communication channel. To limit the bandwidth, it makes sense to limit the number of phones in an array. The physical design and further development of AOBs has been studied in a Joint Research Project (JRP) of the NATO Under-sea ReUnder-search Centre (NURC), the University of Algarve (UAlg), the Netherlands Defence Academy (NLDA) and the Universit´e Libre de Bruxelles (ULB). During the BP’07 sea trails, the University of Algarve participated with two AOBs. The buoys pictured in Figure 3.3 count 8 and 16 hydrophones.

Figure 3.3: Acoustic-oceanographic buoys of the University of Algarve, deployed dur-ing BP’07. The buoys are equipped with sparse receiver arrays and means of wireless communication to transfer acoustic data.

The next chapters examine geoacoustic inversion with data from sparse vertical arrays [47] that were deployed from a small boat. The arrays that were used count four or five phones, which makes the equipment highly portable. During the Saba bank experiments the equipment had been flown in with a commercial airliner. Sparse sampling of the water column also makes that the data require far less bandwidth than would have been the case with a dense arrays. When signals below 2.5 kHz are multiplexed together, just two radio channels suffice to transmit a continuous recording of underwater sound.

The downside of a sparse array is that such a configuration involves less noise cancellation than processing with data from a dense array. For a constant