A19:
UNDERWATER ACOUSTICS

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0037

Phase-coherent underwater acoustic communications in thepresence of impulsive snapping shrimp noise
Hee-Chun Song
Scripps Institution of Oceanography, La Jolla, CA 92093-0238, USA

ABSTRACTSnapping shrimp are the dominant source of high-frequency (>1 kHz) biological noise in warm coastal waters.
During an acoustic experiment conducted in the northeastern East China Sea (SAVEX15), highly impulsive,strong signals produced by shrimp snaps were observed continually in 100-m deep shallow water, affecting
the performance of phase-coherent underwater acoustic communications in a rapidly time-varying
environment. Two adaptive algorithms are applied to process high-frequency (11-32 kHz) communication
sequences with various constellations from BPSK to 32-QAM: (1) traditional multichannel decisionfeedback equalizer (M-DFE) and (2) time-reversal based DFE (TR-DFE). The preliminary analysis of thedata leads to the following conclusions. First, both algorithms (M-DFE and TR-DFE) can provide a similar
performance for low-order constellations such as BPSK and QPSK. For high-order constellations (16-QAM
and 32-QAM), however, only TR-DFE has been successful for decoding with a data rate of up to 60 kb/s
using a 16-element, 56-m aperture vertical array over a 3-km range. Second, high-order QAM constellations
appear more resilient to the impulsive nature of the snaps, apparently treating the impulsive noise as one ofthe outer constellations, albeit with a burst of errors.
Keywords: Multichannel decision-feedback equalizer (M-DFE), Time-reversal-based DFE (TR-DFE),
Snapping shrimp noise, SAVEX15

1. INTRODUCTION
A shallow-water acoustic variability experiment (SAVEX15) was conducted in the northeastern
East China Sea in May 2015. The goal of SAVEX15 was to collect acoustic and environmental dataappropriate for studying the coupling of oceanography, acoustics, and underwater communications in
the dynamic region. The experimental site had a nearly flat sandy bottom with a water depth ofapproximately 100 m. Both fixed and towed transmissions were carried out to two moored arrays overranges of 1-10 km. The acoustic transmissions were in various frequency bands (0.5-32 kHz) [1].
Snapping shrimp dominate the high frequency soundscape (>1 kHz) in warm shallow waters [2].
Surprisingly, the highly impulsive, strong signals were observed continually during SAVEX15 [3-5],
affecting the performance of phase-coherent acoustic communications in this rapidly time-varying
environment. In this paper, we analyze the communications data between a moored source (at 20 m
depth) and a 16-element vertical receiver array separated by 2.8 km, which were collected over an
extended period of 22 hours using the wide frequency band (11-22 kHz) with various constellations(e.g., QPSK, 16-QAM, and 32-QAM).

2. ADAPTIVE ALGORITHM
We consider two adaptive algorithms to decode the communication packets: (i) standard
multichannel decision-feedback equalizer (M-DFE) with the least mean square (LMS) adaptivealgorithm [6] and (ii) multichannel time-reversal combining followed by a single-channel DFE (TRDFE) with frequent channel updates for time-varying environments [7]. In both cases, a second-orderphase-locked loop (PLL) is applied for phase tracking.

3. PERFORMANCEThe highly impulsive nature of snapping shrimp noise had a significant impact on phase-coherent
communications either by disrupting the phase tracking or causing burst errors. Nevertheless, both M-DFE

and TR-DFE provided a similar performance for low-order constellations such as BPSK and QPSK in termsof output signal-to-noise ratio (SNR), suggesting that either algorithm can be used.
However, M-DFE completely failed as the constellation increases to 16- or 32-QAM. On the other hand,
TR-DFE continued to perform very well, as illustrated in a scatter plot of 32-QAM with a data rate of 30 kb/s
[Fig. 1(a)]. What is really interesting is that TR-DFE with high-order constellations (16- or 32-QAM) showeda much better chance to overcome the phase disruption due to the impulsive shrimp noise than TR-DFE with
low-order constellations. It appears that the strong shrimp snap noise was treated as one of the outerconstellations, albeit with a burst of errors. To see the impact of fluctuating ocean environments, Fig. 1(b)shows the TR-DFE performance of various constellations (QPSK, 16-QAM and 32-QAM) over a 22-h period,
indicating the performance can change as much as 8 dB.

Fig. 1. (a) An example of scatter plot for 32-QAM at 2.8-km range using the wide frequency band (11-22
kHz) with a symbol rate of 6 kHz. (b) Performance of TR-DFE communications for various modulations(QPSK, 16-QAM, and 32-QAM) over a period of 22 hours during SAVEX15.

REFERENCES
1. Song H.C. et al. Underwater sound channel in the northeastern East China Sea. Ocean Engineering; 147,
2018. p. 370-374.
2. Chitre M. Acoustic sensing in snapping shrimp dominated environments. Proc. ICA 2010; 23-27 August
2010; Sydney, Australia.
3. Lee D.H. et al. Temporal variability in acoustic behavior of snapping shrimp in the East China Sea andits correlation with ocean environments. Front. Mar. Sci. 8:779283. 2021.
4. Ahn Jongmin et al. Snapping shrimp noise detection and mitigation for uderwater acoustic orthogonalfrequency division multiple communication using multilayer frequency. Int. J. Naval Archi. and Ocean
Eng. 12, 2020; 258-269.
5. Yuan Zhuqing et al. Calibration of vertical array tilt using snapping shrimp sound. J. Acous. Soc. Am.
Sep. 2018; 143; 1203-1210.
6. Stojanovic M. et al. Adaptive multichannel combining and equalization for underwater acoustic
communications. J. Acous. Soc. Am. 1993; 94; 1621-1631.
7. Song H.C. An overview of underwater time reversal communication. IEEE J. Oceanic Eng. 2016; 41:644-655.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0398

Effect of dolphin impulse signals on long-range underwatercommunication
Donghyeon KIM1; Jeasoo KIM2; Jiyoung SONG3
1

Department of Convergence Study on the Ocean Science and Technology, Korea Maritime and Ocean
University, Korea
2

Department of Ocean Engineering, Korea Maritime and Ocean University, Korea

3

Department of Ocean Engineering, Korea Maritime and Ocean University, Korea

ABSTRACTThe biomimetic long-range acoustic communications experiment 2018 (BLAC18) was conducted in the eastof Pohang, South Korea, to verify the feasibility of long-range underwater communication in the East Sea.
During BLAC18, unwanted signals generated by dolphins were recorded. Dolphins generate two types ofsignals: whistle sounds, similar to the frequency modulation signal, and click sounds, similar to the impulsive
signal. Whistle sounds were easily eliminated from the communication signal because they were separated
in the frequency domain, whereas all frequency bands of the communication signal were contaminated byclick sounds. In this paper, we’ll show the effect of click sounds and the cancellation technique.
Keywords: Dolphin, impulse signal, BLAC18, Communication

1. INTRODUCTION
A biomimetic long-range acoustic communication experiment 2018 (BLAC18), was conducted in
the East Sea. The goal of BLAC18 was twofold: 1) to obtain the acoustic and environmental data forstudying the long-range underwater communications and 2) to verify the feasibility of long-range
underwater communication in the East Sea. During BLAC18, however, unwanted dolphin signals wererecorded. About tens of thousands of dolphins are known to inhabit in the East Sea (1).
Dolphins generate two types of signals: whistle sound and click sound (2-4). The whistle sound,which is similar to the frequency modulation signal, is used to communicate with other dolphins. The
click sound used to find prey has the characteristic of the impulse signal. If these marine animal
signals overlap with the communication signal, the phase of the communication signal is distorted andneeds to be eliminated. Whistle sounds recorded in the East Sea are outside the frequency band (< 4kHz) of our designed signal and can be eliminated by bandpass filter. However, because the clicksound is an impulse signal that occupies all frequency bands, cancellation in either the time orfrequency domain is difficult.
In this paper, we will show how the click sound affects the communication signal and how to cancel
the click sounds

2. BLAC18
BLAC18 was conducted in deep water, about 20 km off the east of Pohang, South Korea, in October
2018. The depth of the operation area is between 1,000m ~ 1,500m. The experiment utilized a verticalline array (VLA) of 16 elements spanning to 42 m of a 1,000 m water column. The design frequency
was about 268Hz. The depths of the source and the center of the VLA were about 200 m. The range
between the source and VLA was 30 km.

donghyeon.ual@gmail.com
jskim@kmou.ac.kr
3jysong.ualab@gmail.com
1
2

Figure 1 – (a) Waterfall plot of the signal packet received on the VLA. (b) A spectrogram of thecommunication signal shown in (a)During BLAC18, we transmitted the signal packet consisting of a 3-s LFM signal as a channel
probe, followed by a 2.47-s long communication signal. The carrier frequency and bandwidth of thechannel probe signal and communication signal were set to 2,560 Hz and 640 Hz, respectively. Figure
1 (a) shows the received signals obtained from the VLA. The received signals included a number of
impulse signals, which could also be seen clearly in the spectrogram [Figure 1 (b)]. In Figure 1, thered arrows indicate clearly visible impulse signals with large amplitudes, whereas the blue arrows
show indistinguishable impulse signals in the time domain. Impulse signals act as interferers anddistort the phase of the communication signal. However, because the cancellation in the time andfrequency domains is difficult, this paper shows the cancellation approach in the spatial domain.

3. CANCELLATION OF DOLPHIN CLICKS
To spatially eliminate the click sounds, we use a time reversal processing approach. In general, theconventional time reversal processing (CTRP) is employed to recover the communication signal.
CTRP approach is calculated by correlating the received signal with the channel impulse response(CIR) estimated from the probe signal (5):(1)
𝑆̂(𝜔) = 𝐇 † (𝜔)𝐑(𝜔)̂
where 𝑆(𝜔), 𝐑(𝜔) and 𝐇(𝜔) denote the estimated source signal, the received signal and the CIR,
respectively. The superscript † denotes the complex transpose.
Despite the presence of interferers, the influences of the interferer can be mitigated if the CIR of
the interferers differs from the CIR of the desired signal. Figure 2 (a) depicts a scatter plot obtainedby applying CTRP to the received signal depicted in Figure 1 (a). The majority of the symbols (redcircles) are distributed around the information symbols (yellow circles), implying that their phase was
not distorted. On the other hand, certain symbols (green circles) appear to diverge from theinformation symbols. These outliers are symbols in time that are influenced by strong click sounds.
In other words, while CTRP can mitigate weak click sounds, strong click sounds continue to have aneffect.

Figure 2 – Scatter plots of BPSK modulation based on (a) CTRP and (b) ATRPThe communication signal including impulse signals can be considered as a multiple -inputmultiple-output scenario. Adaptive time reversal processing (ATRP) approach is beneficial when there
are interferers. ATRP spatially eliminates interferers without distorting the phase of thecommunication signal because it employs the weight vector based on minimum variance distortionlessresponse (MVDR) (6). For ATRP, the CIR in Equation 1 is substituted the MVDR-based weight vector(7-8),𝐰(𝜔) =

𝐊 −1 (𝜔)𝐇(𝜔).
𝐇 † (𝜔)𝐊 −1 (𝜔)𝐇(𝜔)(2)Here, 𝐊(𝜔) is the cross-spectral density matrix (CSDM) required to design the weight vector andis expressed as the sum CSDMs yielded by the CIRs of the communication and the click sounds.
Figure 2 (b) is a scatter plot using ATRP. Compared to CTRP, the positions of the green circles aregathered around the information symbol. The performance enhancement due to click cancellation isclearly evident in scatter plots with the migration of the distorted symbols.

4. SUMMARY
During the sea-going experiment, unwanted impulse signals generated by dolphin were recorded.
The impulse signals distorted the phase of the communication signal. Two TRP approaches, which
utilize the spatial domain, are employed to mitigate the impulse signals that overlap in the time andfrequency domains. CTRP mitigated the weak impulse signal but not the strong impulse signal. In
contrast, when ATRP was applied, outliers in the scatter plot migrated to information symbols as aresult of the elimination of strong impulse signals.

ACKNOWLEDGEMENTS
This work was supported by the Agency for Defense Development, South Korea, under Grant No.
UD200010DD.

REFERENCES
1. Kim J. Taxonomic status of common dolphin in the East Sea, Korea. MS Thesis, Pukyong National
University, Busan, Korea; 2015.
2. Au WWL. The Sonar of Dolphins, New York, USA: Simon & Schuster; 1993.
3. Roch MA, Soldevilla MS, Burtenshaw JC, Henderson EE, Hildebrand JA. Gaussian mixture model
classification of odontocetes in the Southern California Bight and the Gulf of California. J Acoust Soc
Am. 2007;121(3):1737-1748.
4. Yoon Y, Sohn H, Park K, Choi Y, Kim S, Choi J. Study of acoustic characteristics of common dolphinsdelphinus delphis in the East Sea. Korean J. Fish. Aquat. Sci. 2017;50(4):406-412.
5. Song H, Hodgkiss WS, Kuperman WA, Higley WJ, Raghukumar K, Akal T, Stevenson M. Spatial
diversity in passive time reversal communications. J Acoust Soc Am. 2006;120(4):2067-2076.

6. Kim J, Song H, Kuperman WA. Adaptive time-reversal mirror. J Acoust Soc Am. 2001;109(5):1817-1825.
7. Capon J. High-resolution frequency-wavenumber spectrum analysis. Proc. IEEE 1969;57(8):1408-1418.
8. Johnson DH, Dudgeon DE. Array Signal Processing: Concepts and Techniques, New York, USA:
Springer; 1992.

ABS-0475

Cross-pattern coherence spatial filtering with hydrophone arrays forpassive-sonar applications
Vasileios BOUNTOURAKIS; Ville PULKKI
Department of Signal Processing and Acoustics, Aalto University, Espoo 02150, Finland

ABSTRACTThis paper presents a spatial post-filtering algorithm for passive-sonar systems deploying linear hydrophonearrays. The algorithm provides an attenuation parameter in the time-frequency domain based on the normalised
cross-spectral density between two signals, which originate from two coincidentally steered conventional beamformers. The computed parameter can be applied as a single-channel spectral post-filter to the output of anotherbeamformer to suppress background noise and interference. The technique is evaluated with simulated dataof two 96-channel flank arrays, as well as with real-world data derived from smaller towed linear arrays. The
evaluation includes two array configurations: a) a single-line configuration using two non-overlapping subdivisions of a linear array for the computation of the parameter, and b) a dual-line configuration using two parallel
linear arrays. In all studied cases, the post-filter is shown to improve the resolution and background-noise suppression capabilities of the baseline beamformers. It is further shown that, in the dual-line configuration, thepost-filter may improve the beamforming performance near the end-fire direction as well as further resolve theport-starboard ambiguity, which is a known drawback of towed linear arrays. Lastly, it is worth noting therelatively low computational complexity of the proposed technique compared to the more demanding adaptive
beamformers.

Keywords: Array processing, Post filtering, Passive sonar

1

INTRODUCTION

Passive-sonar systems utilise hydrophone arrays to provide real-time imaging of the surrounding underwatersoundfield. This task is typically performed by means of beamforming, i.e., combining the multichannel-arraysignals appropriately so that the captured acoustic energy originates from a specific focusing direction (1, 2).
This process is repeated over a dense grid of directions on the horizontal plane and over time, resulting in abearing-time record (BTR), a two-dimensional image of the soundfield energy, which is presented to the sonar
operator.
The most common beamforming technique in the domain of underwater acoustics is the delay-and-sum(DaS) algorithm, also commonly called conventional beamforming (3, 4). Its popularity may be attributed toits simple implementation, low computational load, and robustness to noise. However, the number of available
sensors in the array imposes limitations to the spatial selectivity of the DaS algorithm. Higher resolution may
be achieved, under certain conditions, by employing adaptive beamforming techniques, such as the linearlyconstrained minimum variance (LCMV) (5) and, a special case of LCMV, the minimum variance distortionlessresponse (MVDR) (6, 7). These techniques operate by tracking the noise-field variation and adaptively searchingfor the weights that minimise the beamformer energy while keeping the signal in the desired direction undistorted. LCMV differs from MVDR in that it can also impose additional constraints in specific directions, suchas nulls in the directions of interferers. There are, however, several downsides of these adaptive techniques.
Their resolution is shown to drop in low signal-to-noise ratio (SNR) levels, often reducing to that of DaS(8). They are also known to be susceptible to steering vector errors and coherent reflections (9, 10). Last but
not least, their computational complexity increases non-linearly with increasing number of sensors, a property
that often makes them an undesirable option for contemporary sonar systems, which may employ arrays withhundreds or even thousands of hydrophones (11).
While beamformers are capable of capturing the acoustic energy from one specific direction while rejecting contributions from other directions, a further SNR improvement may be achieved by employing spatial

post-filters (12, 13). These filters typically measure the coherence between sensors and compute attenuation
parameters in the time-frequency domain, which are then used to modulate the output of a beamformer. Although post-filters have been originally developed for speech enhancement applications, they have been recently
deployed successfully for underwater soundfield visualisation demonstrating good noise suppression capabilities(14).
This paper presents a novel algorithm based on the cross-pattern coherence (CroPaC) post-filter (15). The
proposed algorithm is specifically designed to operate with linear arrays, which are the most common type ofhydrophone arrays found in passive-sonar systems. The arrays can either be mounted onto the sides of a submarine hull, in which case they are called flank arrays, or they can be towed on a cable behind a submarine ora surface ship. More recent applications where linear arrays can be encountered include autonomous underwater
vehicles (AUVs) and remotely operated vehicles (ROVs) (16, 17). The proposed post-filter computes a coherence value between two signals captured by two non-overlapping linear arrays using conventional beamforming
over a grid of steering directions. The resulting value is used to attenuate the output of another beamformer
at the time-frequency points where the received energy corresponds to noise or interference; thus, improving its
spatial selectivity with a relatively low increase on the computational load.
The remainder of the paper is organised as follows. Section 2 provides a brief discussion of related works,
while the proposed algorithm is presented in Section 3. The performance evaluation on simulated and realexperimental data is then conducted in Section 4. Section 5 discusses the computational advantages of the
proposed algorithm and, finally, Section 6 provides conclusions and suggestions for future research directions.

2

RELATED WORK

The proposed technique belongs to a well-established class of signal enhancement techniques known as postfilters. Notable examples of such filters include the Zelinski (18), the McCowan (19) and the Leukimmiatis (20)post-filters , which compute coherence values based on the auto- and cross-spectral densities of signals capturedby omnidirectional microphones. The differences between these implementations involve mainly the assumptions
made for the characterisation of the noise field. The noise suppression capabilities of such filters in reverberant
environments are investigated in more detail in (21).
An alternative approach is followed by the CroPaC post-filter (15), which utilises directional microphones
derived through beamforming in order to compute the required auto- and cross-spectral densities. More specifically, the beam patterns used in the derivation of the CroPaC post-filter correspond to circular or sphericalharmonic functions of different orders. The desired properties of these patterns are that a) they have maximum
sensitivity and equal phase only in the look direction, and b) both diffuse and sensor noise are orthogonal
between the patterns. As a result, the cross-spectral density between the beamformed signals provides a good
estimation of the target signal energy, while noise, which is captured incoherently, is suppressed. CroPaC has
demonstrated improved performance in diffuse-noise conditions compared to other post-filters, especially at low
frequencies (22, 23, 24). Moreover, since it makes no assumptions on the properties of the noise field, it ismore suitable for complex acoustic environments. A reformulated version of CroPaC for large circular arrays(14) has shown the potential of the CroPaC method for application in underwater environments with hydrophonearrays. However, none of the known versions of the CroPaC algorithm is suited for linear arrays, since the required patterns can be obtained optimally only by specific geometries, such as circular or spherical arrays. This
motivated the development of the new version of the algorithm proposed here, which utilises different type of
patterns that can be obtained by linear arrays.
The patterns involved in the computation of the proposed post-filter are generated by dividing a linear array
to two non-overlapping sub-arrays and by applying conventional beamforming to each one of them. This idea
draws its inspiration from a class or techniques known in the literature as split-beam processing (25, 26, 27).
These techniques are widely used in underwater acoustics for bearing estimation of broadband signals with
passive linear towed arrays. They operate by steering coincident beams from each sub-array and estimating thecross-correlations between the received signals. These cross-correlations provide the time delays through which
the target bearings can be estimated. This method owes its popularity in the field to its reduced computationalload compared to the full-aperture beamforming. However, its ability to detect weak signals in real-world
passive-sonar scenarios is debated (26). Nevertheless, a recent study (27) proposed a split-beam processing
algorithm that includes an essential prewhitening step, which is argued to improve the detection of broadband
weak targets.
Another topic explored in this work, which may be of particular interest to the underwater community,
concerns the potential benefits of the proposed method in resolving the port-starboard ambiguity of linear arrays.
This problem is caused by the symmetry of the linear array patterns around their main axis, which results in
identical beamformer outputs for targets located at symmetrical bearing angles. For flank arrays, the ambiguity
may be sufficiently resolved through the acoustical shadowing offered by the submarine hull. Therefore, theproblem mainly concerns towed linear arrays where no such shadowing occurs. In this case, the methods
proposed to resolve the ambiguity concern special arrangements of the hydrophones, such as linear arrays of
triplets (28, 29, 30) or twin-line planar arrays (31, 32, 33). The proposed post-filter is tested on a dual-line
towed array configuration and is shown to offer additional resolution of the port-starboard ambiguity whencompared to employing beamforming without the post-filter applied.

3

PROPOSED METHOD

Contrary to the original CroPaC algorithm which is defined in the circular or spherical harmonic domains, theproposed post-filter operates directly in the space (sensor) domain. Thus, it is henceforth referred to as thespace-domain CroPaC (SD-CroPaC) algorithm. The two linear array geometries examined in the work, namely
the single- and dual-line configurations, are shown in Figure 1a. Since the working principle is the same forboth configurations, the post-filter is derived here for the single-line configuration.

…

…

≈
≈

≈
≈

(a)

(b)

Figure 1. SD-CroPaC post-filter: (a) proposed array configurations for the application of the filter and geometrical conventions used in the paper, (b) signal processing flow of the method for the single-line configuration.
The multichannel vector of a Q-sensor linear array in free space at frequency k and time i is modelled asx(k, i) = s(k, i)v(k) + n(k, i),(1)where s denotes the desired signal of the target source, v ∈ CQ×1 is the array transfer vector which describes
the propagation from the target source to the array, and n ∈ CQ×1 is an additive noise component. It is assumed
that the target source signal is captured by the array as a sum of plane waves (far-field approximation).
The proposed method provides a heuristic formula for a single-channel Wiener post-filter applied to the
output of a beamformer. The post-filter’s general expression ishopt =

Φss,
Φss + Φnn(2)

where Φss and Φnn are the signal and noise power spectral densities at the beamformer output. Note that thefrequency and angle indices are omitted for brevity. Since Φss and Φnn are not known a priori, they need to be
estimated in order to derive the post-filter’s expression.
The signal processing flow of the SD-CroPaC post-filter is shown in Figure 1b. The linear array is firstdivided into two adjacent non-overlapping sub-arrays U and V, comprising M and Q − M sensors respectively.

The time-domain signals of the sub-arrays xU (t) ∈ RM×1 and xV (t) ∈ R(Q−M)×1 are first transformed into thetime-frequency domain, typically with a short-time Fourier transform (STFT). The resulting signals xU (k, i) ∈CM×1 and xV (k, i) ∈ C(Q−M)×1 produce respectively the signals SU and SV through DaS beamforming at anangle θ . The post-filtering parameter is then computed as
2ℜ[ΦUV (k, i, θ )],(3)
G(k, i, θ ) = max 0,
ΦU (k, i, θ ) + ΦV (k, i, θ )∗ (k, i, θ )S (k, i, θ )} is the cross-spectral density between the beamformed signals S
where ΦUV (k, i, θ ) = E{SUV
U
and SV , ΦU (k, i, θ ) = E{|SU (k, i, θ )|2 } and ΦV (k, i, θ ) = E{|SV (k, i, θ )|2 } are their auto-power spectral densities,ℜ denotes the real part, and E denotes an expectation operator. Note that the SD-CroPaC beam patterns are
frequency dependent as opposed to the frequency invariant patterns of the original CroPaC algorithm.
The cross-spectral density between the two beamformed signals ΦUV provides an approximation of the target
signal’s energy Φss due to the fact that the two beamformers capture signals coherently in the look direction
while rejecting diffuse noise and interferers. The real operator is applied to ΦUV because the numerator of
the post-filter transfer function (Eq. 2) expresses an energy quantity. The denominator is approximately twotimes the total energy at the output of the baseline beamformer; therefore, the numerator is multiplied by 2 tobound the value of the normalised cross-spectral density between -1 and 1. Finally, the half-wave rectification
with the max operator discards all the negative values - by setting them to 0 - since they correspond to phasesmismatches between the two beamformers. The resulting parameter G can then be used to modulate the outputof the baseline beamformer b steered in the same direction

y(k, i, θ ) = G(k, i, θ ) · b(k, i, θ ).

(4)

The baseline beamformer can be, for example, a DaS or MVDR beamformer that uses all the available sensors
in the array. The filtered spatial spectrum of the soundfield is obtained by plotting the energy of the signal yover a grid of angles θ that scan the horizontal plane. As is shown in the following experiments, the method
is also directly applicable to baffled linear arrays, such as the flank arrays mounted onto the submarine hull,
where the DaS beamformer may be optionally replaced by a spatial matched filter (SMF) to take into accountscattering effects.
Fig. 2 shows two examples of the G patterns obtained with a simulated 12-sensor uniform linear array(ULA), which is divided into sub-arrays of 7 and 5 sensors for the application of SD-CroPaC. The frequency
range extends up to the aliasing limit f = c/(2d). In Fig. 2a, the soundfield consists of a single plane-wave
with incident angle θ = 90◦ . It can be seen that with increasing frequency the post-filter’s pattern becomes
more directive, while more low-level side-lobes appear. The corresponding response of the filter when the array
captures a diffuse noise field is depicted in Fig. 2b. The diffuse field was simulated as the sum of 5012
incoherent plane waves arriving from a uniform distribution of angles around the array. In this case, it isdemonstrated that, above a certain frequency ( f ≃ fal /8), the diffuse-field is captured incoherently between thetwo beamformers, resulting in sufficiently low G values.

(a) G for a single target at 90◦ (broadside)(b) G for a diffuse noise field

Figure 2. Parameter G over frequency and steering angle computed for a 12-sensor uniform linear array.

4

EXPERIMENTAL EVALUATION

4.1 Evaluation with simulated data of flank arrays
In this section, the proposed method is tested on simulated data of two uniform linear flank arrays mounted
onto both sides (port-starboard) of a submarine. For the simulation, a Boundary Element Method (BEM) modelof a submarine with flank arrays was created in the COMSOL Multiphysics® software. A view of the model
geometry along with the adopted orientation of the coordinate system is shown in Fig. 3a. Each array comprises
96 hydrophones with inter-sensor distance d = 0.284 m, resulting in a total length of about 27 m and the distance
between the arrays is 6.87 m. The hydrophones of the flank arrays were modelled as omnidirectional pressure
sensors and their responses were simulated for incident plane waves on the horizontal plane from 360 angles
uniformly distributed around the submarine. These responses correspond to the sum of the incident and scatteredfields received by the sensors. Due to computational time limitations, the simulated frequencies were restrictedin the range 300-800 Hz with a step of 10 Hz. The submarine hull was modelled as a perfectly rigid soundboundary and the acoustic environment as an infinitely large homogeneous water domain with c = 1480 m/s.

(a) Geometry of the BEM submarine model.

(b) Sub-arrays selected for the dual-line configuration.

Figure 3. BEM model of submarine with two uniform linear flank arrays.
For these experiments, the targets were modelled as static white-noise sources in the far-field captured by thehydrophones as plane waves arriving from different angles θ . Accordingly, the ambient noise was simulated asa sum of incoherent plane waves arriving from all directions along the horizontal plane, representing a diffuse
field. The noise level is described by the direct-to-diffuse ratio (DDR) expressed in dB, which is the ratio of
the target’s energy over the total energy of the diffuse field.
In typical passive-sonar applications, each array is assigned a dedicated half-space area: the port arrayscans the horizontal plane over the angles [−180◦ , 0◦ ], while the starboard array steers the beams in the range[0◦ , 180◦ ]. Sound waves arriving from the non-visible area of each array - the half-space area covered by theother array - are highly attenuated, especially at higher frequencies, through acoustic shadowing caused by
the submarine hull. Therefore, the port-starboard ambiguity that linear arrays naturally suffer from is largely
resolved. It is noted, however, that part of the acoustic power is still received from the non-visible area of
each array, especially near the end-fire direction where the acoustical shadowing is less effective. Besides, it
is known that linear arrays exhibit decreased performance near the end-fire due to lower resolution. These
considerations motivated the study of a dual-line configuration in this model, as shown for example in Fig. 3b,
where the information from both arrays is combined in order to improve performance near the end-fire. Hence,in the following experiments, the single-line configuration (using each flank array separately) is employed when
the target is near the broadside, while the dual-line configuration when it is near the end-fire.
To assess the performance of SD-CroPaC in the single-line configuration, the starboard array is divided
into two sub-arrays of 60 and 36 hydrophones, as described in Section 3. Fig. 4a shows the obtained spatial
spectrum using DaS for a target at θ = 80◦ with DDR=0 dB, along with the corresponding spectrum when
SD-CroPaC is applied to the DaS output. It can be seen that the post-filter offers additional background noise
suppression as well as a better resolution of the target.

(a) DDR=0 dB(b) DDR=-10 dB

Figure 4. Spatial spectra using simulated data of the BEM model: (a) for a static target near the broadside(SD-CroPaC with the single-line configuration), (b) for a static target near the endfire (SD-CroPaC with thedual-line configuration).
For the dual-line configuration, a static target was placed at θ = −5◦ and the noise level was set to
DDR= −10 dB. The corresponding sub-arrays U and V were selected to be the ones shown in Fig. 3b. It
was observed in simulations that a number of sensors may be omitted, thus saving resources, without degrading
the performance. Nonetheless, the optimal selection of sub-arrays is beyond the scope of the present study.
The results of this experiment are shown in Fig. 4b. Note that, in this case, the DaS algorithm is replaced
by a spatial matched filter (SMF), which is a more general beamforming approach that also accounts for thelevel differences between the sensor signals apart from the phase shifts. This choice was based on the observation that the beamforming performance improves when the level differences between the two arrays causedby the acoustical shadowing are taken into account. For comparison, the plot also includes the DaS output ofthe port array used in isolation as well as its filtered counterpart using SD-CroPaC. Note that, for the singleline configuration, the SMF approach does not offer any advantage compared to DaS; therefore, only the DaS
spectrum is shown here. It is evident that the near end-fire performance is improved with the dual-line configuration compared to using the port array separately in terms of spatial resolution, bearing estimation accuracy,
and background noise suppression. Moreover, even though the SD-CroPaC filter improves the spatial accuracyof the DaS beamformer in the single-line configuration, there is a more notable advantage demonstrated whenusing the proposed filter in the dual-line configuration.
4.2 Evaluation with real experimental data of a towed dual-line array
The method is also evaluated on real measurement data obtained through the use of a towed dual-line planarhydrophone array. The array comprises two parallel linear arrays of different dimensions separated by a distanceh = 0.135 m. A sketch of the array geometry is shown in Fig. 5. The larger array (array A) consists of 8 nonuniformly distributed sensors and has a total length of lA = 2.08 m. The x coordinates of the sensor positions(in metres) are
xA = [0.00, 0.08, 0.37, 0.75, 0.94, 1.07, 1.50, 2.08].
The smaller array (array B) is a uniform linear array of 6 sensors with inter-sensor distance d = 0.07 m, resulting
in a total length of lB = 0.35 m. The x coordinates of its sensor positions (in metres) arexB = [0.79, 0.86, 0.93, 1.00, 1.07, 1.14].
The studied scenario involves a single target in shallow water moving around the array, whose position
remains fixed throughout the recording. The target’s bearing changes from approximately θ = −105◦ to θ = 75◦
at a relatively constant speed. The duration of the recording is 300 s and the sampling frequency is 32 kHz.

x
q=7
Array A..
.

q=0

Array Bq = 13
..
.
q=8
θ

y

Figure 5. Geometry of the dual-line hydrophone array.
First, the evaluation considers only the data of the non-uniform array A for the application of SD-CroPaC insingle-line configuration. The array is divided into sub-arrays U and V, which consist of the first 4 (q = 0, .., 3)
and the remaining 4 hydrophones (q = 4, .., 7) respectively. Due to axial symmetry (unbaffled linear array),
the target is equally visible from both sides of the array (port-starboard ambiguity). Therefore, it is sufficient
to steer the beams in the half-space [0◦ , 180◦ ]. The obtained spatial spectra are averaged in the frequency
range 2 kHz to 10 kHz. Note that, due to the non-uniform distribution of the hydrophones, it is less likely for
grating lobes to appear; therefore, the aliasing limit can be set higher than the theoretical limit of the array( falA ≃ 1280 Hz). The BTR plots using DaS alongside its filtered version with SD-CroPaC are shown in Fig. 6.
It can be observed that the post-filtering operation results in a BTR image of higher contrast in Fig. 6b, where
the background noise is suppressed and the target is depicted with higher resolution. These observations are inagreement with those arising from the simulated data in Sec. 4.1. Note that, during the first 170 seconds where
the true bearing of the target is negative, the BTR plots display the mirrored image of the target with a positive
bearing.

(a) DaS(b) DaS-filtered

Figure 6. BTRs for the single-line configuration (Array A)
Two slices of the BTR plots taken at different time instants are shown in Fig. 7. For comparison, theplots also include the MVDR output, along with its filtered version with SD-CroPaC. It is observed that SDCroPaC offers an additional background level suppression of approximately 4-6 dB for both baseline beamformers. Moreover, the peak associated with the target is depicted with higher resolution, following the applicationof the proposed post-filter. It is interesting to note that, in this experiment, MVDR demonstrates a belowstandard performance where its theoretical advantages are not evident: it only achieves a better resolution than
DaS when the target is near the broadside, while it appears to be inferior to DaS in terms of background noise
suppression. This may be explained by the noisy nature of the real-world data (sensor positioning errors, phasemismatches, sensor noise), as well as by the acoustic conditions of the underwater environment; it is known
that MVDR is susceptible to low-SNR conditions and to coherent interferers, such as reflections from the water
surface. On the other hand, DaS is known to be more robust under non-ideal conditions. Overall, from thisexperiment, it may be concluded that the proposed method is valid despite the presence of noisy data underreal-world conditions. A further conclusion is that the application scope of the method may be extended to
non-uniform linear arrays consisting of as few as 8 sensors.

(a) t=65 sec(b) t=142 sec

Figure 7. Spatial spectra at two different instants for the single-line configuration (Array A).
For the next experiment, the hydrophone signals from both linear arrays - forming a dual-line planar array- are used. The beams are steered over the whole horizontal space from θ = −180◦ to θ = 180◦ , as there isno symmetry in this case. The beamformer outputs are averaged in the same range (2 kHz to 10 kHz) as in theprevious case of the single array. Note that since the smaller array B is uniform, grating lobes are expected
to appear above its aliasing frequency ( falB ≃ 10.5 kHz); therefore, the upper limit of 10 kHz guarantees thatthese will not appear. Regarding the implementation of SD-CroPaC, the sub-arrays U and V in the dual-line
configuration as defined in Section 3, correspond to the arrays A and B, respectively. The BTR plots for thisexperiment are given in Fig. 8. It can be seen in Fig. 8b that, apart from the advantages observed previously inthe single-line case regarding spatial resolution and noise suppression, this case presents the additional advantageof resolving the port-starboard ambiguity to a certain degree; the mirrored peaks which do not correspond totrue targets are attenuated, compared to the real target peaks. The slices of the BTR plots (Fig. 9) taken at
two time instants show that by applying DaS to the dual-line array, the level difference between the real andthe mirrored peaks is 4-5 dB. By also applying the proposed post-filter, the corresponding level difference isextended to 8 dB for the target near broadside and to more than 10 dB for the target near endfire, in which casethe port-starboard ambiguity appears to be almost fully resolved. Lastly, the performance of MVDR appears
to degrade even more in this case, compared to the single-line configuration. This may be attributed to theaccumulation of errors as more sensors are used.

5

COMPUTATIONAL CONSIDERATIONS

Despite the recent advances in beamforming techniques, conventional beamforming is still a favoured choice
nowadays among sonar system designers, at least partly due to its low computational requirements. Adaptive
beamformers may offer higher resolution of closely spaced targets under certain conditions, but this advantage
is often offset by an increase in computational complexity. This issue becomes particularly important for sonararrays that include hundreds or even thousands of hydrophones, because the CPU and memory requirementsof adaptive techniques scale up considerably with increasing array size (11, 34). The implementation problems
relate mainly to the inversion of the sample covariance matrix, which in practice has an algorithmic growth rateof O(Q3 ) with brute-force computation or at best O(Q2.807 ) using the Strassen algorithm (35), where Q denotes
the number of sensors. By comparison, conventional beamformers have a linear time complexity O(Q), whichmakes them more suitable for real-time systems employing large hydrophone arrays. The computation of the
proposed SD-CroPaC post-filter is based on a set of conventional beamformers utilising sub-arrays; therefore,
its algorithmic growth rate is also linear with respect to the number of input signals (O(Q)). Note also that theSD-CroPaC steering weights may be calculated offline and stored, as opposed to the MVDR signal-dependent
weights, which are calculated for every iteration. These cost-effective implementation characteristics of the pro-(a) DaS(b) DaS-filtered

Figure 8. BTRs for the dual-line configuration (Arrays A and B).

(a) t=65 sec(b) t=142 sec

Figure 9. Spatial spectra at two different instants for the dual-line configuration (Arrays A and B).
posed method suggest its potential for incorporating it into modern sonar systems, where keeping computationalcosts low is highly prioritised.

6

CONCLUSIONSThis paper presents a post-filtering algorithm for applications requiring underwater soundfield visualisation withlinear hydrophone arrays. The proposed method represents a space-domain reformulation of the Cross-Pattern
Coherence (CroPaC) algorithm. In this sense, its applicability is not necessarily limited to linear arrays. Nevertheless, this study focused on two common linear array configurations, namely single-line and dual-line arrays,
owing to their practical importance in passive sonar applications. The evaluation is performed on simulated dataof large flank arrays using a BEM model of a typical submarine as well as on real hydrophone-array data ofsmaller towed arrays. The results suggest an analogous performance to the original algorithm, which consists
in suppressing background noise and improving the spatial resolution of the baseline beamformers. A novel
concept introduced in this paper is to use the two flank linear arrays found in many submarines - ordinarily
deployed each separately for beamforming - in a dual-line array configuration, which, along with the proposed
post-filter, may improve the beamformer performance at angles near end-fire. Moreover, it is shown that when
the proposed method is applied to a towed dual-line array configuration, it may offer additional resolution of theport-starboard ambiguity. Finally, the computational requirements of the proposed method are argued to be low
when compared to those of adaptive algorithms, which typically become quite demanding for a large number of
sensors. Therefore, the post-filtering method, when paired with a signal-independent baseline beamformer, may
represent a promising option for real-time passive-sonar applications.

ACKNOWLEDGEMENTS
The authors would like to acknowledge Saab AB for funding the project, Saab Kockums AB for providing
insight and expertise, and Patria for providing the measurement data of the towed hydrophone array.

REFERENCES
1. Van Trees HL. Optimum array processing: Part IV of detection, estimation, and modulation theory. John
Wiley & Sons; 2004 Apr 7.
2. Van Veen BD, Buckley KM. Beamforming: A versatile approach to spatial filtering. IEEE assp magazine.
1988 Apr;5(2):4-24.
3. Carter GC. Time delay estimation for passive sonar signal processing. IEEE Transactions on Acoustics,
Speech, and Signal Processing. 1981 Jun;29(3):463-70.
4. Quazi A. An overview on the time delay estimate in active and passive systems for target localization. IEEE
Transactions on Acoustics, Speech, and Signal Processing. 1981 Jun;29(3):527-33.
5. Frost OL. An algorithm for linearly constrained adaptive array processing. Proceedings of the IEEE. 1972
Aug;60(8):926-35.
6. Capon J. High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE. 1969
Aug;57(8):1408-18.
7. Ferguson BG, Carevic D. Optimal spatial filtering of real data from submarine sonar arrays. In OCEANS’10
IEEE SYDNEY 2010 May 24 (pp. 1-7). IEEE.
8. Zoltowski MD. On the performance analysis of the MVDR beamformer in the presence of correlated interference. IEEE Transactions on Acoustics, Speech, and Signal Processing. 1988 Jun;36(6):945-7.
9. Reddy VU, Paulraj A, Kailath T. Performance analysis of the optimum beamformer in the presence ofcorrelated sources and its behavior under spatial smoothing. IEEE Transactions on acoustics, speech, andsignal processing. 1987 Jul;35(7):927-36.
10. Cox H. Resolving power and sensitivity to mismatch of optimum array processors. The Journal of the
Acoustical Society of America. 1973 Sep;54(3):771-85.
11. Stergiopoulos S. Advanced beamformers. In Advanced Signal Processing 2017 Sep 29 (pp. 79-146). CRC
Press.
12. Simmer KU, Bitzer J, Marro C. Post-filtering techniques. InMicrophone arrays 2001 (pp. 39-60). Springer,
Berlin, Heidelberg.
13. Pulkki V, Delikaris-Manias S, Politis A, editors. Parametric time-frequency domain spatial audio. John Wiley& Sons; 2017 Dec 26.
14. Bountourakis V, McCormack L, Winberg M, Pulkki V. Parametric spatial post-filtering utilising high-ordercircular harmonics with applications to underwater sound-field visualisation. The Journal of the AcousticalSociety of America. 2021 Jun 24;149(6):4463-76.
15. Delikaris-Manias S, Pulkki V. Cross pattern coherence algorithm for spatial filtering applications utilizing microphone arrays. IEEE Transactions on Audio, Speech, and Language Processing. 2013 Aug
15;21(11):2356-67.
16. Maguer A, Dymond R, Mazzi M, Biagini S, Fioravanti S, Guerrini P. SLITA: A new slim towed array for
AUV applications. Journal of the Acoustical Society of America. 2008 May;123(5):3005.
17. Nicholson JW, Healey AJ. The present state of autonomous underwater vehicle (AUV) applications andtechnologies. Marine Technology Society Journal. 2008 Mar 1;42(1):44-51.
18. Zelinski R. A microphone array with adaptive post-filtering for noise reduction in reverberant rooms. In
ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing 1988 Jan 1 (pp. 25782579). IEEE Computer Society.

19. McCowan IA, Bourlard H. Microphone array post-filter based on noise field coherence. IEEE Transactions
on Speech and Audio Processing. 2003 Nov;11(6):709-16.
20. Leukimmiatis S, Maragos P. Optimum post-filter estimation for noise reduction in multichannel speech processing. In 2006 14th European Signal Processing Conference 2006 Sep 4 (pp. 1-5). IEEE.
21. Marro C, Mahieux Y, Simmer KU. Analysis of noise reduction and dereverberation techniques based on microphone arrays with postfiltering. IEEE Transactions on Speech and Audio Processing. 1998 May;6(3):24059.
22. Delikaris-Manias S, Pulkki V. Parametric spatial filter utilizing dual beamformer and SNR-based smoothing.
In Audio Engineering Society Conference: 55th International Conference: Spatial Audio 2014 Aug 26.
Audio Engineering Society.
23. Delikaris-Manias S, Pulkki V. Cross spectral density based spatial filter employing maximum directivity
beam patterns. In IISA 2014, The 5th International Conference on Information, Intelligence, Systems andApplications 2014 Jul 7 (pp. 1-6). IEEE.
24. McCormack L, Delikaris-Manias S, Pulkki V. Parametric acoustic camera for real-time sound capture, analysis and tracking. In Proceedings of the 20th International Conference on Digital Audio Effects (DAFx-17)
2017 Sep (pp. 412-419).
25. MacDonald VH, Schultheiss PM. Optimum passive bearing estimation in a spatially incoherent noise environment. The Journal of the Acoustical Society of America. 1969 Jul;46(1A):37-43.
26. Stergiopoulos S, Ashley AT. An experimental evaluation of split-beam processing as a broadband bearing estimator for line array sonar systems. The Journal of the Acoustical Society of America. 1997
Dec;102(6):3556-63.
27. Chen T, Wu Y, Zhang W, Chen W, Wang J, Hu Z. Insight into split beam cross-correlator detector with theprewhitening technique. IEEE Access. 2019 Nov 4;7:160819-28.
28. Groen J, Beerens SP, Been R, Doisy Y, Noutary E. Adaptive port-starboard beamforming of triplet sonararrays. IEEE Journal of Oceanic Engineering. 2005 Oct 24;30(2):348-59.
29. Baldacci A, Haralabus G. Signal processing for an active sonar system suitable for advanced sensor technology applications and environmental adaptation schemes. In 2006 14th European Signal Processing Conference 2006 Sep 4 (pp. 1-5). IEEE.
30. Haralabus G, Baldacci A. Unambiguous triplet array beamforming and calibration algorithms to facilitate anenvironmentally adaptive active sonar concept. In OCEANS 2006 2006 Sep 18 (pp. 1-6). IEEE.
31. Allensworth WS, Kennedy CW, Newhall BK, Schurman IW. Twinline array development and performancein a shallow-water littoral environment. Johns Hopkins APL Technical Digest. 1995 Jul 1;16(3):222-32.
32. Feuillet JP, Allensworth WS, Newhall BK. Nonambiguous beamforming for a high resolution twin-line array. The Journal of the Acoustical Society of America. 1995 May;97(5):3292-.
33. Newhall BK, Allensworth WS, Schurman IW. Unambiguous noise and reverberation measurements from adual line horizontal array. The Journal of the Acoustical Society of America. 1995 May;97(5):3289-.
34. Gentilho E, Scalassara PR, Abrão T. Direction-of-arrival estimation methods: A performance-complexity
tradeoff perspective. Journal of Signal Processing Systems. 2020 Feb;92(2):239-56.
35. Strassen V. Gaussian elimination is not optimal. Numerische mathematik. 1969 Aug 4;13(4):354-6.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0743

Implementation of a real-time time-varying channel simulator forverification tests of underwater acoustic communication systems
Sea-Moon KIM1; Seung-Geun KIM1; Youngchol CHOI1; A-ra CHO1
1

Korea Research Institute of Ships and Ocean Engineering, Korea

ABSTRACT
Generally, sea trials for performance tests of underwater acoustic communication modems and network
algorithms are time- and cost-consuming process, because the performance should be verified for various
underwater channels and operating conditions. The Doppler shift channel environment due to the relative
modem movement, the time-varying channel with time-varying environment, and various types of channels
according to depth and locations are some examples. For this reason, we developed a real-time channel
simulator that can simulate time-varying channels including Doppler shift in various marine environments
and test conditions. In the simulator the Doppler shift was implemented using the change of the impulse
response function according to the movement of the modem. Similarly, the time-varying channel is simulatedby random change of each impulse in magnitude and time delay according to a given probability distributionfunction over time. The simultaneous sampling frequency of the eight-channel input and output module is 1
MHz, which covers operating frequency of eight modems up to several hundred kHz. It also has thesimulation capability of delay time more than 20 s which corresponds to 30 km distance. For the systemverification performance test was conducted and verified the key performances.
Keywords: Real-time channel simulator, Underwater acoustic communication, Time-varying channel

1. INTRODUCTION
Sound waves are mainly used for wireless underwater data transmission due to their lower
attenuation rate than electromagnetic waves or light in water. Since the successful demonstration of
the coherent modulation technique at sea numerous studies on underwater acoustic communication
have been performed [1,2]. Currently, commercial underwater acoustic modems are also available.
However, due to the complexity of underwater acoustic communication channels, development ofunderwater acoustic modems still remains challenging work. KRISO, Korea Research Institute ofShips and Ocean Engineering, is developing a wireless underwater network, which comprises twoheterogeneous network systems. One is a long-distance network with a transmission distance of 30
km and a transmission rate of 100 bps, and the other is a short-range high-speed network with atransmission distance of 200 m and a transmission rate of 100 kbps. For these networks the Doppler
effect to be overcome is the frequency shift caused by platform movement speed of 10 m/s and 5 m/s,
respectively. In addition as with other modems stable communication performance should be verified
in a time-varying multi-path environment. Such an underwater communication channel environment
varies greatly depending on location and time, and it takes a lot of time and cost for the verificationof communication systems. For this reason, a system that simulates underwater communication
channels in real time is absolutely necessary. Due to the complicated characteristics of underwater
acoustic channels, it is still difficult to simulate them in real time, despite the recent improvement incomputational ability. Computationally efficient algorithm was proposed and compared with real data
[3] and a fully integrated simulator was introduced for AUVs networking at various underwater
environments [4]. In this paper, a channel simulator that simulates stochastic underwater channels
including Doppler shift in real time and that can verify key performance of the long-range/short-range
1smkim@kriso.re.krnetworks under development is described.

2. SYSTME DESCRIPTION
Figure 1 shows the developed real-time channel simulator. It is composed of two parts: a processing
unit and an AD/DA module. AD/DA sampling frequency is 1 MHz with 16-bit resolution. The numberof channels is eight for each AI and AO module. It has sufficient memory to store over 25-second 8ch data to simulate node-to-node delay over 37.5 km distance. It also supports Doppler shift byplatform movement with moving speed over 20 m/s. The number of multi-paths can be set arbitrarily
up-to 9.

Table 1 – Specifications of the channel simulator

Figure 1 - The front view of the real-time channel
simulator

Parameter
Sampling frequency
Max number of nodes
Max number of multipaths
Max delay of impulse
response
Max velocity of moving
node

Value
1 MHz
8

Remark9> 25 s> 37.5 km> 20 m/s

3. PERFORMANCE TEST
A several tests were performed for the verification of key performances of the channel simulator.
Another data acquisition system for generating arbitrary signals and saving output signals from thesimulator. During the delay check test a rectangular pulse was used for the transmitting signal. The
multi-path delays coincide with the theoretical values and the maximal delay was 25 s, whichcorresponds to 37.5 km distance for c=1500 m/s. For checking Doppler shift performance 6 kHz and
128 kHz tones were used. The platform moving velocity was set up-to 20 m/s. The frequency shifterrors were within the spectral resolution for the both low and high tone signals. For checking timevarying characteristics of simulated channels, BPSK modulated PN sequence data was used. Channel
fluctuation is observed in the spectrogram and the measured Doppler spread was within 10% error
relative to the theoretical value.

ACKNOWLEDGEMENTS
This research was supported by a grant from National R&D Project of “Development of the widearea underwater mobile communication systems” funded by Ministry of Oceans and Fisheries andsupported by Korea Institute of Marine Science and Technology Promotion(1525010926, PMS4720)

REFERENCES
1. M. Stojanovic, J. A. Catipovic, and J. G. Proakis, "Phase-coherent digital communications for underwater
acoustic channels," IEEE J. Oceanic Eng., 1994;19(1):100–111.
2. D. B. Kilfoyle and A. B. Baggeroer, "The state of the art in underwater acoustic telemetry," IEEE J.
Oceanic Eng., 2000;25(1):4-27.
3. P. Qarabaqi, Y. M. Aval, and M. Stojanovic, "Computationally efficient simulation of underwater acoustic
communication systems," Oceans, 23-27 September 2013; San Diego, CA, USA.
4. T. Schneider and H. Schmidt, "NETSIM: A realtime virtual ocean hardware-in-the-loop acoustic modem
network simulator," Fourth Underwater Communications and Networking Conference (UComms), 28-30
August 2018; Lerici, Italy.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0991

Study For Cavitation Inception Speed Of The Model Propeller
Using Accelerometers Installed On The Model Ship
Hyungsuk Han1, Sukku Lee2, Soohong Jeon1, Yonghoon Kim1, Chungwon Lee1, Dongnyuk Lee1,
Gahyang Lee1,
1

1Reliability Test Analysis Team, Defense Agency for Technology and Quality, Korea
2

LIG NEX1, LIG NEX1, Korea

ABSTRACT
Naval vessels detect and identify the enemy under the water using sonar system. Therefore, it is necessary to
reduce the noise of the own ship to detect the enemy and not to be detected from the enemy's sonar system.
In order to reduce the noise of the ship, cavitation of the propeller should be controlled and the ship must sailbelow the cavitation inception speed when engaged in anti-submarine warfare operations. Therefore, thecavitation inception speed should be monitored in real time. In this research, the monitoring algorithm forthe cavitation inception speed of the model propeller using accelerometers installed on the model ship is
described when it is operating in the cavitation tunnel.
Keywords: CIS(Cavitation Inception speed), Kurtosis, DEMON spectrum

1. INTRODUCTION
The importance of the conditions based monitoring for the sound and vibration in a ship is beingemphasized since the noise and vibration for the propulsion system and large capacity onboard
equipment such as the main generator are necessary to be controlled at the anti-submarine warfare
operation. When the cavitation is initiated, the radiated sound of propeller increases rapidly and thesound is modulated in accordance with the number of blade. Since the submarine can identify thesurface ship with its radiated sound occurred at the blade passing frequency of the propeller, surface
ship should be operated under the cavitation inception speed at anti-submarine warfare operation.
Recently, many researchers are studying about sound monitoring of propeller ca vitation using
accelerometers attached on the hull (1-2) .
In this research, the estimating method to define cavitation inception speed is investigated with themodel scale ship using accelerometers attached on the hull perpendicularly above the propeller.

2. Model Scale Test for Detection CIS
2.1 CIS Monitoring Algorithm
In this research, the monitoring technic for the cavitation inception speed of the ship propeller isinvestigated using accelerometers installed on the hull above propeller.
The monitoring algorithm of cavitation inception speed developed in this research is as shown inFig, 1. Considering the characteristics of the sonar sensor and background noise, the minimum soundpressure level that the sonar system is able to detect (Lp_min) can be estimated. If the transfer function
can be defined between sound pressure level of the propeller and the acceleration of the hull above
the propeller, the minimum hull acceleration caused by the sound pressure that the sonar system candetect(La_min) can be defined. If the cavitation is sufficiently developed, the sound pressure level aswell as hull acceleration increases rapidly. In this research, the minimum hull acceleration level when
1
2

gf@aaa.com
gf@aaa.com

the cavitation is sufficiently developed is defined to La_cav. In Fig. 1, when the measured accelerationon the hull above the propeller is over than La_cav, this algorithm defines that the cavitation occurs
already.
In this research, parameters to evaluate the cavitation inception are defined as below. Since the test
is not full scale test but model scale one, the level of these parameters are different to the full scale
test. These values are defined experimentally base on the various experience.
(1) The minimum acceleration level that can found the cavitation inception speed (L a_min) : 100dB(2) The minimum DEMON spectrum level at the harmonics of shaft rate and blade passing
frequency that can found the cavitation inception speed : 0.07m/s 2(3) The minimum DEMON spectrum kurtosis level that can found the cavitation inception speed : 3.0(4) The minimum acceleration level that can found the cavitation develops sufficiently: 107dB

Fig. 1 Estimation algorithm of the cavitation inception speed of the propeller
2.2 Monitoring Results
Using the algorithm to define cavitation inception speed as shown in Fig. 1, model scale test was
performed with the test setup in Fig. 2. Fig. 3 is the monitoring results for 101~107 conditions as
described in Table 1. In Fig. 3, the monitoring of Kurtosis, minimum acceleration of the hull that canbe detect by sonar(La_min), DEMON spectrum level at shaft rate and blade passing frequency,
minimum acceleration when the cavitation is sufficiently developed(La_cav) are denoted using blue
color. In addition, the final decision that the cavitation occur is denoted using red color. In Fig. 3, it
can be found that the cavitation is incepted at 104 condition for starboard side.
Considering Fig. 3, cavitation inception speed can be estimated form 104 conditions(24knots ).

Accelerometer (PCB
W352C03)(a) model ship and propeller

Hydrophone(B&K 8103)(b) hydrophone and accelerometer installation

Fig. 2 Model test setup

Table 1 Full and model scale test condition
F u llsca le

M o d e lsca leT e stN o

S h ip sp e e d
(kts)

σ
_A

J_A

rp s

F lo w
sp e e d (m /s)

101

18

3 .4 9 0 1

1 .1 0 1

2 7 .19

102

20

2 .8 3 8 6

1 .0 8 7

2 7 .59103

22

2 .3 5 1 5

1 .0 8 7

2 7 .49

104

24

1 .9 1 9 5

1 .0 82 7 .69105

26

1 .6 8 3 2

1 .0 5 9

2 8 .29106

28

1 .4 4 5 3

1 .0 3 7

2 8 .89107

30

1 .2 5 1 1

1 .0 2 3

29

9Fig. 3 cavitation monitoring results for starboard side for 101~107 conditions(White : No Cavitation, Blue, Red : Cavitation)

The cavitation bubble could be detected at the propeller root in 105 condition when the cavitation
was started to inspect visually at the cavitation monitoring window of the cavitation tunnel as shownin Fig. 4. Therefore, based on the visual inspection, the cavitation might occur between 104 and 105
condition, where the ship speed is 24~26 knots. Comparing the visual inspection to the inspection
from accelerometers, cavitation inception speed from visual inspection is slightly higher than thatfrom acceleration on the hull. Considering that the sound signal of the cavity can be detected before
the cavity is detected visually, it can be conclude that the cavitation inception speed difference
between acceleration and visual inspection in this test is allowable.

3. CONCLUSIONSIn this research, the cavitation inception speed of the model scale propeller is estimated withacceleration on the hull above the propeller. As a test result, it can be found that the cavitation is
incepted about 24 knots.

Root Cavitation(a) 104(b) 105(c) 106(d) 107

Fig. 4. Cavitation occurring according to speed up of the propeller shaft
When comparing the cavitation inception speed estimated by acceleration and visual inspection,
they are similar considering the general delay time that cavitation sound o ccurs before the cavitation
is visually inspected.
It can be expected that the results of this research can be used effectively with respect to thedevelopment of cavitation inception monitoring system for the ship propeller.

ACKNOWLEDGEMENTS
This researched is performed with support from LIGNEX1 in Korea.

REFERENCES
1 Bruel & Kjær, 2019, Acoustic signature management, BAE’s Naval Noise Management project sets sail,https://www.bksv.com/en/News/BAE-systems,
2 S. Turkmen, B. Aktas, M. Atlar, N. Sasaki, R. Sampson, W. Shi, 2017, On-board measurement techniques
to quantify underwater radiated noise level, Ocean Engineering, vol. 130(2017), pp.166-175

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0995

Some issues on long-range propagation of acoustic waves related
to environmental factors in the East Sea of Korea
Young-Nam NA1; Changbong CHO1; Won-Ki KIM1; Su-Uk SON1; Jooyoung HAHN1;
Young-Gyu KIM1
1

Agency for Defense Development, Korea

ABSTRACTFor acoustic waves propagating long-range, they may be dependent on environmental factors. The East Sea
of Korea has several environmental factors to be considered for propagation including several water masses
and big bathymetry variations. This paper deals with these environmental issues taking effects on the
propagation of acoustic waves. Considering the water mass effects, we employ the concept of ‘Robustnessof Detection (ROD)’ and a model based on the Gaussian beam tracing scheme to measure their effects on
sensor detection range (DR). In the coastal area, we find that the two parameters, DR and ROD, show reverse
patterns in general. We can see that the performances are highly dependent on the sensor receiving depths.
For simulating the bathymetry effects, we use a 3-dimensional model based on the parabolic equation. The
bathymetry effects include horizontal refractions of acoustic waves and data sources used in the simulation.
The model results, propagation losses, give clear horizontal refractions responding to bathymetry
distributions. Also, the propagation losses are sensitive to data sources employed in the simulations.
Keywords: Long-range propagation, Robustness of detection, Detection performance, Water mass,
Bathymetry variation, Horizontal refraction

1. INTRODUCTION
For long-range propagation of acoustic waves, many environmental factors may take effects on
their loss and direction which are main issues to surveillance s ystems with acoustic sensors. The East
Sea of Korea has many water masses which are identified by their temperature, salinity and otherphysio-chemical properties. The East Sea also has complicated bathymetry distributions like sea
mounts, banks, and canyons. This paper manages the issues of water mass and bathymetry effects on
the propagation of acoustic waves.
Concerning the water mass issue, we focus on the relations between its variations (temporal,
spatial) and detection performance assuming mid-frequency passive sensor. We use a model based on
the Gaussian beam tracing scheme and mean temperature data in a coastal area of the East Sea.
Concerning the bathymetry issue, we consider horizontal refractions of acoustic waves of low
frequency in the selected regions of shallow and deep. For simulating horizontal refractions, we use
a 3-dimensional model and bathymetry data of high resolution.

2. Water Masses and Detection Performances
2.1 Some Concepts
We can compute the maximum detection range (DR) at each grid point for each sensor as following(1).
DR   Di N
N

i 1

(1)

Here, Di and N represent the maximum DR at i-th bearing and number of bearings equally divided
in 360o , respectively. In general, we can use some other concepts to measure sensor performance like1

ynna@add.re.kr‘Effective Range (ER)’, ‘Near-continuous Range (NR)’ and ‘Maximum Detection Range (DR)’ (1).
Among these, we borrow the concept ‘Maximum DR’ to measure long range detection including
shadow and convergence zones. However, it is not complete definition to measure the performance ofa sensor, specially in deep water, because it may not guarantee consistent detection when there exist
shadow zones in horizontal and vertical directions. To complete the performance of a sensor, we adopt
another definition ‘ROD’ as following (2).
ROD DB  DA
100 (%)
DB(2)

Here, D A and D B denote shadow range and maximum DR, respectively.
In waters of less than, for example 500 m depth, sound speed is usually decided by temperature.
The temperature variation may be analyzed using the Empirical Orthogonal Function (EOF) method.
Spatio-temporal variations of temperature (T) can be decomposed into modal structure (An ) andcorresponding time coefficients (B n) for each mode n as following (3).

T ( x, y, z, t )   An ( x, y, z) Bn (t )n(3)Solving the eigen-value problems, we get





1
TT T ei  i ei
N(4)

where T , ei , i , N denote temperature matrix, eigen-vector, eigenvalue and number of spatial
points, respectively. The modal structure A n is normalized to magnitude of standard deviation of B n tohave a physical unit °C, while Bn has not any physical unit. Using historical data in the study area of
the East Sea of Korea, we estimate that the 1-st EOF mode accounts for 78 % of total variance and 2nd mode does 14 %.
2.2 Detection Performances
We select an area of Korean Peninsula which has about 80 grid points. Figure 1 presents bathymetry
and examples of horizontal slices of 60m depth. The slices are temperature, DR (ratios relative to the
maximum) and ROD (%) based on the re-analyzed environment at 28 October, 2007. The maximum
DR is selected from the DRs at all grid points. We employ a model of Gaussian beam tracing scheme(4).

Figure 1 – The bathymetry and horizontal slices of 60 m depth in the study area. (a) bathymetry, (b)temperature, (c) relative DR and (d) ROD
The DR (Fig. 1c) shows high values in the northwest direction, where North Korean Cold Water
develops along the bathymetry (5). Extremely low DR in lower part is due to relatively shallow flatbathymetry (Fig. 1a) where sound energy quickly decays by frequent interactions with the sea surface

and bottom. When we look into the ROD (Fig. 1d), we can see that it shows reverse patterns to the
DR variation. The ROD gives low values in the northwest region, where DR does high values.
Meanwhile, in the region along the coastline and upper right (Fig. 1d), the ROD shows high values
when DR does low values (Fig. 1c). In this example, DR and ROD variations show exactly negative
phases, and this fact suggests we should consider the DR and ROD at the same time for complete
description of sensor performance in deep water of complicated environment.

Figure 2 - Yearly variations of the EOF modes (top) and relative DR (bottom)
Figure 2 gives yearly variations of the principal components of the first two EOF modes (top) andDR (bottom) in the study area. We assume one source at 100 m depth and two kinds of receivers,
surface (5 m) and underwater (100 m) with different sensor systems. And then, we calculate the DRat each point by averaging over all horizontal bearings. Once again, the maximum DR is selected fromthe DRs at all grid points of horizontal slice 5 m or 100 m, and relative DR is the ratio to the maximum.
We get the relative DR every 10 days by taking average over 80 grid points. We can see that the 1-st
mode follows seasonal variation but the 2-nd mode does not. The 2-nd mode is regarded to be relatedwith some meso-scale phenomena like eddies and currents. When we get yearly variations of the DR
for some passive sensor, we can see that the 1-st and 2-nd EOF modes have very high correlations(almost 0.9) with the relative DR variations against the surface (5 m) and the underwater (100 m)
receivers, respectively.
Figure 3 shows typical sensor performances in winter and summer. We assume the DR (relative
ratio) for a conventional passive sensor against a source of 100 m for larger area of the East Sea. When
we keep the receiver depth to be 20 m (left two figures), we can see that the DR shows big differences
between winter and summer. However, when we keep the receiver depth to be 100 m (right two
figures), we can see very little differences between the two distributions. This fact implies that when
we keep the receiver to be deep enough, we can expect very consistent detection performances in the
study area regardless of the season.

Figure 3 – The relative DR distributions in winter (Feb.) and summer (Aug.) for receiving depth 20 m (left)
and 100 m (right)

4. Bathymetry and Horizontal Refractions
4.1 Acoustic ModelWe employ a model based on the parabolic equation with higher-order terms (5). Applying Padé
and Taylor approximations to non cross-terms and cross-terms respectively, we have the final
expression to simulate 3-dimensional effects of environment with cross-terms as following (6)
𝑚

𝑚

𝑛

𝑛

(1 + 𝑎̂𝑖 𝑌)(1 + 𝑎̂𝑖 𝑍)
𝑢(𝑥 + ∆𝑥) = ∏∏{1 + ∑ ∑ 𝑐𝑖𝑗 𝑌 𝑖 𝑍𝑗 }𝑢(𝑥)
̂
̂
+
𝑏
𝑌)
+
𝑏
𝑍)
(1
(1
𝑖
𝑖
𝑖=1

𝑗=1

(5)

𝑖=1 𝑗=1

Here, 𝑎̂𝑖 and 𝑏̂𝑖 are Taylor approximation coefficients. The Taylor approximation terms in { }represent cross-terms with the coefficient c ij . By giving some modifications to the 1-st two terms, we
can induce the form of summations which makes parallel processing possible and give the final
expression as following (7)
𝑛

𝑛

𝑖=1

𝑖=1

𝑛
𝑛
𝑎̂𝑖 𝑌
𝑎̂𝑖 𝑍
𝑢(𝑥 + ∆𝑥) = {1 + ∑} {1 + ∑} {1 + ∑ ∑ 𝑐𝑖𝑗 𝑌 𝑖 𝑍𝑗 } 𝑢(𝑥)1 + 𝑏̂𝑖 𝑌
1 + 𝑏̂𝑖 𝑍
𝑖=1
𝑗=1

(6)

Here, 𝑎̂𝑖 and 𝑏̂𝑖 are also approximation coefficients. The coefficient c ij represents the cross-terms.
4.2 Bathymetry and Input
In simulating the horizontal refractions of acoustic waves, we employ a typical sound speed profile
in winter and sandy bottom (density 1.99 g/cm 3 , attenuation 0.5 dB/wavelength) in the East Sea of
Korea (6). We consider two kinds of bathymetry distributions in shallow (depth 50~400 m) and deep
(depth 900~2,000 m) regions as shown in Figure 4. The source is located at (X, Y, Z in km) = (0, 5,0.1), where the simulations area is X (propagating, latitude) = 25 km, Y (crossing, longitude) = 10 km.
We assume the frequency to be 75 Hz.

Figure 4 - Bathymetry distributions of shallow(left) and deep(right) regions
4.3 Horizontal Refractions
We consider two kinds of data sources, KTOPO (Korean Topography) and ETOPO1 (Earth andBathymetry) (6). The KTOPO has 30 m × 30 m resolution but the ETOPO1 does 1ʹ × 1ʹ. With the
KTOPO data, we get various grid resolutions 300 m, 600 m, and 1,800 m. With the grid resolution of
1,800 m, we compare the results with those with the ETOPO1 data.
Figure 5 shows propagation losses at shallow region (upper) and deep region (lower) withbathymetry data resolutions, 30 m (left), 300 m (middle), and 600 m (right). All figures are sliced at
the receiver depth 100 m, while the source is located at 100 m. With varying resolutions of bathymetry
at shallow region (upper), we can see the three results give similar major patterns throughout the
maximum propagating range, 25 km. Propagation losses are little sensitive to bathymetry resolution
and this pattern can be expected from the fact that the acoustic model employs bilinear interpolation
over the bathymetry data before simulations (6). Horizontal refractions of acoustic fields are veryclear specially in the region X = 10~25 km, Y = 5~10 km, where the bathymetry increase with thecross range (Y) as shown in Figure 5. Horizontal refractions happen toward shallower region in thebathymetry distribution.
With varying resolutions of bathymetry at deep region (lower), we can see the three results also
give similar major patterns and are little sensitive to bathymetry resolution. Horizontal refractions o f
acoustic fields are very clear specially in the region X = 0~20 km, Y = 0~4 km, where the bathymetry
increase with the cross range (Y).
For the comparisons with data sources, we select the grid resolution of 1,800 m from the KTOPO
which is similar to that of the ETOPO1. Figure 6 shows propagation losses of receiver depth 100 m
at shallow region (upper) and deep region (lower) with bathymetry data sources, KTOPO (left) andETOPO1 (right). In the short propagating range, X = 0~7 km, major patterns are similar between thetwo data sources but very different after the range. The degree of difference is more crucial in shallow
region.

Figure 5 - Propagation losses of receiver depth 100 m at shallow region (upper) and deep region (lower)with bathymetry data resolutions, 30 m (left), 300 m (middle), and 600 m (right)

Figure 6 - Propagation losses of receiver depth 100 m at shallow region (upper) and deep region (lower)with bathymetry data sources, KTOPO (left) and ETOPO1 (right)

5. CONCLUSIONSIn deep water, where there may exist shadow and convergence zones, we have to consider the DR
and ROD simultaneously to measure sensor performance completely. In the study area, we find thatthe two parameters, DR and ROD, show reverse patterns in general. Concerning the environmental
effects, we can see that the performances are highly dependent on the sensor receiving depths. Thatis, shallow and deep depths of a passive sensor show high correlations with the 1-st and 2-nd EOF
modes, which reflect seasonal and meso-scale variations of environment, respectively. If we keep the
passive sensor depth to be greater than those of seasonal environment changes, we can expect
consistent performance throughout a year.
Throughout the simulation of horizontal refractions, acoustic waves clearly respond tobathymetry variations and they refract toward shallower region of bathymetry. The simulations withthe bathymetry of high resolution, show similar major patterns in the propagation loss from 30 m to
600 m grid resolution, since the model gives an interpolation over the bathymetry data before
simulations. However, the propagation losses are very sensitive to data sources used in simulations,
implying that precise bathymetry data set is crucial for estimating horizontal refractions.

REFERENCES
1. Mcdowell. Environmental and statistical performance mapping model for underwater acoustic
detection system, 2010. Mel Wagstaff, Brief of Acoustics at NAVOCEANO, 2009.
2. Emery W. J and Thomson R. E. Data analysis methods in physical oceanography, Pergamon press, 1997.
p. 336-350.
3. Porter M. B and Bucker H. P. Gaussian beam tracing for computing ocean acoustic fields, J. Acoust. Soc.
Am. 1987;82(4), 1349-1359.
4. Yun J. Y, Magard L, Kim K, Shin C. W, Byun S. K. Spatial and temporal variability of the North Korean
Cold Water leading to the near-bottom cold water intrusion in Korea Strait. Progress in Oceanography;
2004;60(1), 99109.
5. Lee K, Seong W, Na Y. Three-dimensional Cartesian parabolic equation model with higher-order crossterms using operator splitting, rational filtering, and split-step Padé algorithm. J. Acoust. Soc. Am.
2019;146, 2041-204.
6. Na Y, Son S.-U, Hahn J, Lee K. Simulation of acoustic waves horizontal refraction using a threedimensional parabolic equation model. J. Aocust Soc Kor. 2022;41(2):1-12.
7. Lee K. A study on the 3-D low-frequency acoustic propagation model for the development of an integrated
ocean surveillance system. The 3-rd year final report, ADDR-415-193072, 2019.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0097

Increasing presence of marine acoustic technology in marine
environmental impact assessment
Katsunori MIZUNO1
1

Department of Environment Systems, Graduate School of Frontier Sciences, The University of Tokyo,
Kashiwanoha, Kashiwa, Chiba 277-8561, Japan

ABSTRACT
In order to realize the SDGs and carbon neutrality, it is essential to take the global environment into
consideration, and marine environmental impact assessment is essential for this purpose. The introduction of
power generation facilities using renewable energy, development associated with the effective use of fisheries,
tourism, mineral resources, etc., and environmental changes associated with climate change, such as global
warming, are recognized as ongoing issues common to the world, and there is concern about the short- andlong-term impacts of these environmental changes on the natural and human worlds. Our research group has
been working on a new approach to apply marine acoustic technologies, e.g. high-resolution acoustic video
camera and high-resolution sub-bottom profiler, to environmental assessment since around 2013. In this talk,we will introduce some case studies of these efforts and future issues.
Keywords: Ultrasound, Marine environment, Assessment

1. INTRODUCTION
Changes in the marine environment due to anthropogenic activities, such as ocean acidification,
eutrophication, deoxygenation, and pollutions, are recognized as a significant, ongoing, global issue[1]. In order to evaluate short- and long-term effects, data on the baseline biodiversity, its quantitative
distribution, and the ecology of various species are essential. Acoustic systems with various
operating frequencies are commonly used for the monitoring of marine environment. Our researchgroup have newly developed some acoustic systems to visualize the marine creatures in and under
the water [2-5].

2. High-resolution acoustic video camera
The high-resolution imaging sonar termed Dual-frequency IDentification SONar (DIDSON) has
produced near-video-quality images by simultaneously transmitting and receiving multiple acoustic
beams. High-resolution imaging sonar can also provide high-resolution, 3D acoustic data in water andhas been applied to the survey of aquatic plants in shallow lakes. This device is suitable for themonitoring of fish behavior (Fig. 1) and surveying of vegetation [6].

3. High-resolution sub-bottom profiling system
Acoustic systems with various operating frequencies are commonly used for detecting objects
buried in marine sediments, for example buried wooden shipwrecks under the seabed was visualizedusing chirp signals with 1.5–13 kHz swept pulses. Recently, a new monitoring tool, named 3D acoustic“coring” system, was developed and used for the precise survey of buried plant roots with outer
diameters of 5–10 cm using the ultrasound of 100 kHz center frequency [2]. Even higher frequency
signals with center frequencies of 130–1000 kHz are starting to be used for laboratory-based tests toreveal bivalves and other small buried objects, including soft gummy worms [7]. Recently, thevisualization of large infauna in the sediment at deep sea area with using an acoustic coring systemfor deep sea (Fig. 2, A-core-2000) [5].
1kmizuno@edu.k.u-tokyo.ac.jp

Fig. 1 Long-term fish monitoring with high-resolution imaging sonar

Fig. 2 Acoustic survey in the sediment with sub-mm order resolution

ACKNOWLEDGEMENTS
We thank the captain and crew of R/V Yokosuka and the operation team of DSV Shinkai 6500 for
their assistance. This work was supported by KAKENHI (20H02362 and 20KK0238) and The Murata
Science Foundation (M21109). We thank Dr. Tadashi Maruyama for his efforts in bringing together
the collaboration among the authors. Also we thank Prof. Maki and Mr. Sakamaki for their helping todevelop the pressure-resistant container, Kumi Yokoi for assisting with grain size distribution analysis,
and Katsumi Ohira who works in Japan Probe Co., Ltd., for their assistances in conducting thelaboratory experiments.

REFERENCES
1. Joos, F., Plattner, G. K., Stocker, T. F., Marchal, O., & Schmittner, A. (1999). Global warming and marine
carbon cycle feedbacks on future atmospheric CO2. Science, 284(5413), 464-467.
2. Mizuno, K., Liu, X., Katase, F., Asada, A., Murakoshi, M., Yagita, Y., ... & Watanabe, Y. (2016).
Automatic non-destructive three-dimensional acoustic coring system for in situ detection of aquatic plantroot under the water bottom. Case Stud. Nondestruct. Test. Evaluation, 5, 1-8.
3. Suganuma, H., Mizuno, K., & Asada, A. (2018). Application of wavelet shrinkage to acoustic imaging ofburied asari clams using high-frequency ultrasound. J. Appl. Phys., 57(7S1), 07LG08.
4. Mizuno, K., Cristini, P., Komatitsch, D., & Capdeville, Y. (2020). Numerical and Experimental Study ofWave Propagation in Water-Saturated Granular Media Using Effective Method Theories and a Full-Wave
Numerical Simulation. IEEE J. Ocean. Eng., 45(3), 772-785.
5. Mizuno, K., Nomaki, H., Chen, C., & Seike, K. (2022). Deep-sea infauna with calcified exoskeletons
imaged in situ using a new 3D acoustic coring system (A-core-2000). Scientific reports, 12(1), 1-7.
6. Mizuno, K., Asada, A., Ban, S., Uehara, Y., Ishida, T., & Okuda, N. (2018). Validation of a high-resolution
acoustic imaging sonar method by estimating the biomass of submerged plants in shallow water.
Ecological Informatics, 46, 179-184..
8. Schulze, I., Wilken, D., Zettler, M. L., Gogina, M., Schönke, M., & Feldens, P. (2021). Laboratory
Measurements to Image Endobenthos and Bioturbation with a High-Frequency 3D Seismic Lander.
Geosciences, 11(12), 508.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0098

Effects of weak shear rigidity of the seabed on acoustic fields inwater
Oleg A. GODIN 1
1

Physics Department, Naval Postgraduate School, Monterey, CA 93943, USA

ABSTRACT
Surficial marine sediments are often characterized by weak shear rigidity, with shear waves been much slower
than compressional ones. Mathematically, weak shear is a singular perturbation of the wave field in the noshear (fluid) problem. Physically, weak shear leads to several wave phenomena, some of which requirebottom-coupled vector sensors to be observed, while others affect the acoustic field throughout the watercolumn. This paper investigates coupling between compressional and shear waves in continuously anddiscretely stratified solids with weak shear rigidity. Asymptotic and perturbation techniques are employed to
study coupling in continuously stratified solids. Results for the seabed modeled as a stack of homogeneous
layers are obtained as limiting forms of exact solutions. Additional sound attenuation results from radiationof shear waves into the seabed. The radiation attenuation of acoustic normal modes is found to be drasticallydifferent over homogeneous and layered seabeds, with density stratification emerging as the most importantfactor. Efficiency of the wave coupling within a continuously stratified layer strongly depends on the relativechange in the shear modulus and mass density over spatial scales of the order of the shear wavelength. At
low acoustic frequencies, radiation attenuation may be comparable in magnitude to compressional wave
absorption.
Keywords: Sound Attenuation, Shear Waves, Shallow-Water Waveguide

1. INTRODUCTION

The shear wave speed is much smaller than the compressional wave speed and the sound speed inwater in many types of marine sediments, while the evanescent compressional wave field in
propagating modes is often negligible deep in the seabed, where the shear speed is large. Under these
conditions, the seabed is typically described as a stratified fluid in the sound propagation modeling,with the shear wave effects presumably included via “effective” values of the sound speed, density,and the attenuation coefficient of compressional waves in the fluid bottom. While such a model may
be acceptable for calculation of mode phase and group velocities, it may be insufficient for predicting
sound attenuation.
In underwater waveguides, shear-wave contributions to sound attenuation are most significant forbottom-interacting normal modes that propagate over a seabed, where shear speed is less than thephase speed of the mode. In propagating normal modes, compressional waves become evanescent in
the seabed and carry acoustic energy horizontally as long as dissipation is negligible. In contrast,
shear waves are not evanescent and transport energy deep into the seabed. In the absence of dissipation,
coupling to shear waves is the only mechanism of exponential attenuation of propagating normal
modes in range-independent waveguides.
Understanding and accurate evaluation of the shear wave contributions to sound attenuation arenecessary for modeling and interpretation of the sound transmission loss in shallow-water waveguides
and for sonar performance predictions from low to mid-frequencies. Quantifying the shear-wave
contributions to attenuation of normal modes is important for modeling and interpretation of the power
spectra, modal content, and vertical directionality of low-frequency ambient sound in shallow water
as well as in applications of noise interferometry to passive remote sensing of the coastal ocean. An
accurate prediction of the shear-wave contribution to sound attenuation at different frequencies isrequired to interpret the empirical models of the linear and non-linear power-law frequency
1

oagodin@nps.edu

dependencies of the attenuation and evaluate applicability of the physics-based models of wave
propagation in the porous seabed, such as Biot theory and Buckingham’s viscous grain shearing theory.
Shear wave contributions to sound attenuation in the discretely stratified seabed with weak shear
rigidity have been investigated in the recent paper (1). The present work extends the analysis presentedin (1) to include continuously stratified marine sediments.

2. MAIN FINDINGS

Weak shear rigidity of stratified marine sediments is found to have a much stronger effect on
dispersion of bottom-interacting normal modes and especially on sound attenuation than waspreviously surmised based on oversimplified seabed models (2–5). With the effects of the weak shear
being larger and more intricate than previously predicted, applicability of the effective fluid bottom
models and the effect of shear waves on the experimentally measured frequency dependence of thesound dissipation need to be re-assessed.
Acoustic effects of shear rigidity originate from coupling between sound and shear waves at theseafloor and within inhomogeneous seabed. Coupling between compressional and shear waves atinterfaces within the bottom is shown to be far more efficient than the coupling at the water-sedimentinterface. In terms of the small ratio of the shear to compressional wave speeds in soft sediments,
first-order contributions to mode travel time and attenuation result from the P and SV wave couplingin the stratified seabed with density discontinuities. These first-order effects are generally muchstronger and, because of shear wave interference, have a more complicated frequency dependence
than previously studied second-order effects on mode travel times and third-order attenuation effects
in the case of homogeneous solid seabed or bottom with constant density. In discretely stratified
sediments, the acoustic effects of weak shear tend to be most pronounced at lower frequencies, withthe frequency scale being controlled by the shear wave travel time within individual sediment layers,
the rate of change of the sediment properties with depth, and the shear wave dissipation.
When sediment parameters vary continuously within the layers, coupling of compressional andshear waves is enabled by gradients of density and the compressional wave speed, with the density
gradients playing the main role in conversion from compressional to shear waves. In addition to the
local conversion efficiency, shear wave field is also affected by interference of the shear waves
generated at different points. The interference tends to be destructive at high frequencies. Overall
efficiency of the wave coupling within a continuously stratified layer strongly depends on the relativechange in the shear modulus and mass density over spatial scales of the order of the shear wavelength.
The primary implications of this work are for geoacoustic inversions and especially formeasurements of the intrinsic (volume) dissipation of compressional and shear waves. To capture thefirst-order shear effects correctly, geoacoustic models must include a realistic description of density
variation in the seabed in addition to compressional and shear speed profiles. A higher vertical spatial
resolution than in the fluid bottom models is required, particularly for the density and shear speed.
The shear effects are much stronger in the seabed containing dissimilar surficial layers than in aseabed with averaged parameters. Our results suggest that, for narrow-band signals, the shear-wave
contributions to sound attenuation in the bottom can be overly sensitive to uncertain and spatiallyvarying details of sediment stratification, making broadband and/or long-range measurements
preferrable. At low frequencies, shear-wave contributions to sound attenuation in stratified sediments
remain significant even for very low shear speeds. Because of their non-negligible magnitude andnon-monotonic frequency dependence, great care needs to be exercised in characterizing andseparating the contributions to measured attenuation due to compressional-to-shear wave conversion,in order to reliably evaluate the frequency dependence of the intrinsic dissipation of compressional
waves.

ACKNOWLEDGEMENTS

This work was supported by the Office of Naval Research, Award Nos. N00014-21WX012342 andN00014-22WX01287.

REFERENCES

1. Godin OA. Shear waves and sound attenuation in underwater waveguides. J. Acoust. Soc. Am. 2021;
149(5): 3586–3598.
2. Williams AO, Eby RK. Acoustic attenuation in a liquid layer over a ‘slow’ viscoelastic solid. J. Acoust.

Soc. Am. 1062; 34(6): 836–843.
3. Collis JM, Pierce AD, Carey WM. Shear waves and the discrepancy between perceived and ideal
frequency power laws for sediment attenuation. Proc. Meetings Acoust. 2008; 2: 005002.
4. Pierce AD, Carey WM. Sediment shear as a perturbation in geoacoustic inversions and an explanation of
the anomalous frequency dependence of the attenuation. Proc. Meetings Acoust. 2009; 8: 005001.
5. Potty GR, Miller JH. Effect of shear on modal arrival times. IEEE J. Oceanic Eng. 2019; 45(1): 103–115.

ABS-0105

Physics-aided data-driven modal ocean acoustic propagation modeling
Kexin LI(1) , Mandar CHITRE(2)(1) ARL, Tropical Marine Science Institute and Department of Electrical and Computer Engineering, National University ofSingapore, kexin@nus.edu.sg(2) ARL, Tropical Marine Science Institute and Department of Electrical and Computer Engineering, National University ofSingapore, mandar@nus.edu.sg

ABSTRACT
Modeling acoustic propagation accurately is vital to numerous oceanic applications. However, physics-based
acoustic propagation models require accurate prior environmental knowledge, which is often hard and expensive
to acquire. Such a requirement can be relaxed by using data-driven machine learning techniques. Unfortunately,
they are data-hungry and extrapolate poorly. We can potentially train machine learning algorithms with a lot lessdata and get them to extrapolate well by imposing constraints based on our knowledge of acoustic propagation.
Our previous work proposed a physics-based data-aided high-frequency acoustic propagation modeling recipe
based on the ray theory to do precisely this. The promising results obtained motivate us to further tailor therecipe for low-frequency applications. The theory of normal modes tends to be a more appropriate choice tomodel acoustic propagation at low frequencies. In this paper, we incorporate a modal acoustic propagation
model in the structure of a neural network. We demonstrate the superiority of such an algorithm in estimating
acoustic field, as compared with conventional data-driven machine learning techniques. We also show that thistechnique allows us to extract information about the environment, such as estimating the sound speed profile
from acoustic observations.

Keywords: Normal mode, Acoustic propagation model, Data-aided acoustic modeling, SciML.

1

INTRODUCTION

Understanding acoustic propagation in the ocean is crucial for numerous applications, such as communication
channel estimation [1], underwater source localization [2] and geo-acoustic inversion [3]. Oceans offer richmultipath environments that exhibit complicated constructive and destructive interference patterns. Conventional
ocean acoustic propagation models solve the acoustic wave equation [4] using various mathematical techniquesand simplifying approximations. While conventional models have matured over the past few decades, a key
limitation for their effective use is the requirement of having full and accurate prior environmental knowledge.
Accurately measuring required environmental parameters such as sound speed profile (SSP), seabed properties
and sea surface properties, is often hard and expensive in practice.
Advances in data-driven machine learning (ML) resolve many problems that can not be addressed by conventional models. Modeling acoustic propagation through data-driven ML approaches does not need environmental
knowledge. However, classical ML techniques are data-hungry and extrapolate poorly, and therefore poorly
suited for most oceanic applications where data is difficult to obtain and consequently sparse. Recently, a synergetic strategy that embeds underlying domain knowledge into data-driven ML has emerged to handle sucha dichotomy. This emerging technique is in the field of scientific machine learning (SciML) [5, 6]. Physicsinformed neural networks (PINNs) [7], a popular strategy in SciML, encode scientific domain knowledge in theform of partial differential equations (PDEs). The PDEs are added as additional regularization terms in standard loss functions. There are a few recent works that preliminarily demonstrate the feasibilities of PINNs inlearning solutions to the acoustic wave equation [8–11]. However, the use of SciML in the context of ocean
acoustic modeling has not yet been extensively explored.

Augmenting the loss function is one of the possible strategies in SciML to inform a neural network (NN) by
underlying domain knowledge, but is not the only effective one. Embedding the domain knowledge of acoustic
propagation in structures of NNs, and using ML algorithms to train the NNs to solve the simplified wave equation also yields a promising strategy. Our previous work [12] extensively investigates this idea in high-frequency
acoustic propagation modeling problems based on the ray theory [13]. It demonstrates the applicability and superiority of the proposed ray-based data-aided acoustic propagation modeling recipe in various case studies bybenchmarking against classical ML techniques.
Ray theory applies a high-frequency approximation to solve the acoustic wave equation analytically [13],and is therefore not very accurate a low frequencies where the approximation is invalid. Normal mode models [14] are suitable and efficient alternatives for low-frequency oceanic applications. In this paper, we tailor the
propagation modeling recipe based on the normal mode theory. We illustrate the proposed mode-based modelingframework in various scenarios and demonstrate its use in ocean acoustic field estimation problems and a SSP
inversion problem. The proposed framework is flexible to incorporate any known domain knowledge, and canalso embed smaller standard NNs to model unknown parameters. Moreover, it is data-efficient and generalizes
well as compared to classical ML models.

2

METHOD

2.1 Normal mode theory
The acoustic wave equation is a second-order partial differential equation that describes acoustic propagation [13]:∂2p= c2 "2 p,(1)∂t 2
where p denotes acoustic pressure, t represents time and c is sound speed.
We consider an acoustic propagation modeling problem due to a point source in a horizontally stratified(range-independent) two-dimensional (2D) ocean waveguide. A harmonic wave is a feasible solution to (1):p(r, z,t) = p̄(r, z)eiωt ,(2)where p̄(r, z) is complex pressure amplitude at a location with range r and depth z, and ω = 2π f denotes
angular frequency. Substituting (2) back into (1) leads to the Helmholtz equation [4]:k2 (z) p̄(r, z) + "2 p̄(r, z) = 0,(3)ωwhere k(z) = c(z)represents wavenumber at depth z.
Classical normal mode models apply the separation of variables [15] to express acoustic field at location(r, z) as a combination of a depth-dependent term and a range-dependent term:p̄(r, z) = Ψ(z)Φ(r).
We substitute (4) into (3). After rearranging and simplifying, we obtain the modal equation [14]:!
"d1 dΨ(z)ρ(z)+ kz2 (z)Ψ(z) = 0,dz ρ(z) dz
where

kz2 (z) = k2 (z) − kr2 ,

(4)

(5)

(6)

and ρ(z) is density and kr represents horizontal wavenumber.
The modal equation derived in (5) is in the form of a classical Sturm-Liouville eigenvalue problem [16].
Theoretically, there are an infinite number of distinct mode solutions (Ψ(z) and kr ) to the modal equation (5).
Normalized mode solutions form a complete set so that solutions to the wave equation can be represented as
an infinite sum of the normal modes:∞p̄(r, z) = ∑ Ψm (z)Φm (r),
m=1

(7)

where m denotes mth mode. The range-dependent term Φm (r) has a standard form in terms of the Hankel
function [13]:i(1,2)Φm (r) =Ψm (zs )H0 (krm r),(8)
4ρ(zs )
(1,2)

where zs denotes source depth and H0refers to the Hankel function of first or second kind. The choicedepends on radiation conditions. We adopt the Hankel function of the first kind since we assume energy isradiating outwards as r approaches ∞. The asymptotic approximation to the Hankel function is often used inliterature and (8) is approximated by:Φm (r) ≈πieikrm r√e−i 4 Ψm (zs ) √ .
krm
ρ(zs ) 8πr(9)Popular normal mode models, such as Kraken [17], seek all significant eigenfunction solutions Ψm (z) and corresponding eigenvalues krm to the modal equation (5) while satisfying boundary conditions and environment
setup.
2.2 Mode basis neural network
An imaginary krm makes eikrm r an exponentially decaying term with respect to propagation range r. A real krm
leads to a propagating mode that oscillates instead. The infinite sum in (7) can be approximated as a n-mode
finite sum in far-field:nπieikrm r√p̄(r, z) ≈e−i 4 ∑ Ψm (z)Ψm (zs ) √ .
(10)krm
ρ(zs ) 8πr
m=1
Although analytical solutions to (5) are not always available, general field solutions based on the normal mode
theory approximately follow [14]:$n #p̄(r, z) ≈ ∑ Am eikzm z + Bm e−ikzm z Φm (r),

(11)

m=1

where Am and Bm are scaling factors to make sure boundary conditions and environment setup are satisfied.
Even though the approximated field expression is provided in (11), the mode parameters Am , Bm , krm andkzm associated with each mode are calculable only if boundary conditions and all required environmental parameters are accurately known. Any missing environmental parameter prevents the application of the conventional
normal mode model, or requires the modeler to estimate the parameter through other means (e.g. matched fieldprocessing). Such a requirement greatly limits practical uses of normal mode models as operating environments
may not always be well understood.
We propose a mode basis neural network (MBNN) framework to enable the use of normal mode modeling insituations where accurate environmental knowledge is unavailable. The idea of encoding the physics of acoustic
propagation into structures of data-driven ML has been extensively explored in our previous work [12], where
we detail a hybrid modeling framework based on the ray theory for high-frequency underwater acoustic propagation modeling. Following a similar approach, in the MBNN framework, we encode the domain knowledgeof acoustic propagation based on the normal mode theory into the structure of a standard NN to model lowfrequency acoustic propagation in oceans. The MBNN model is differentiable so that well-developed automaticdifferentiation techniques [18] can be utilized to find optimal unknown mode parameters, providing acoustic
measurements and corresponding measurement locations as training data.
The MBNN framework allows numerical propagation models based on the normal mode theory to be datadriven. At the same time, the structure of MBNN encodes essential physics and therefore improves the model’s
data efficiency and generalizability as compared to classical data-driven ML techniques. This leverages complementary strengths of data-driven ML and physics-based models to handle practical scenarios of partially known
physics and limited data availability.
We illustrate our MBNN model formulation through two examples of 2D ocean waveguides with isovelocity
SSP (Section 2.2.1) and non-isovelocity SSP (Section 2.2.2) respectively:

2.2.1 Isovelocity ocean waveguides
We consider an isovelocity ocean waveguide that has a constant sound speed c and density ρ with a water
depth D. A general eigenfunction solution to (5) in this isovelocity ocean waveguide follows [14]:Ψm (z) = Am sin(kzm z) + Bm cos(kzm z).
We assume a pressure-release surface:and a rigid bottom:

Ψ(0) = 0,

(12)
(13)

%
dΨ %%
= 0.
dz %z=D(14)

Such boundary conditions further simplify (12) to:Ψm (z) =&2ρ
sin(kzm z).
D

The corresponding eigenvalue krm is derived as:&# $ #
ω 2
π $2
krm =
− (m + 0.5)
, m = 1, 2, · · · , n.
c
D

(15)

(16)

We assume that we do not know the exact values of c, ρ and D. Due to missing environmental knowledge,
conventional normal mode models cannot estimate acoustic fields. Fortunately, our proposed MBNN model
can automatically learn the best-fitted values of the unknown mode parameters from acoustic data. We train aminimal set of unknown mode parameters and numerically calculate other unknowns using underlying physics
to make sure our method generalizes well.
We denote the minimal unknown mode parameters whose values are yet to learn from acoustic observations
as MBNN model trainable parameters:
T = {c, ρ, D} .
(17)
We minimize the square difference between the estimated pressure amplitude p̄(r, z; T ) and the acoustic fieldmeasurement p̂ at a measurement location (r, z) by tuning T . The loss function is defined as:
L(r, z, p̂; T ) = | p̄(r, z; T ) − p̂|2 .

(18)

Equation (18) is normally summed over a batch of training data in each iteration as per ML standards [19].
With the optimal trainable parameters T ∗ learnt from acoustic observations, we can readily estimate acoustic
fields using (6), (10), (15) and (16).
2.2.2 Non-isovelocity ocean waveguides
Known SSP The MBNN framework is capable of modeling non-isovelocity ocean waveguides as well. As
the modal equation cannot be analytically solved for ocean waveguides with non-isovelocity SSPs, approximate
solutions are necessary. We use the WKB approximation [20] – one of the most widely used approximation
techniques in normal mode literature, to approximate the depth-dependent term:
!z

where

!z

ei 0 kzm (s)ds
e−i 0 kzm (s)ds
Ψm (z) ≈ Am '+ Bm ',
kzm (z)kzm (z)kzm (z) =

(!

ω
c(z)

"2

2 .
− krm

(19)

(20)

SSNN layer
Input layer

MBNN layer"̅! r, z(location)

Output layer(pressure amplitude)!

"̅" r, z∑"̅ r, z…

"$…

"̅# r, z

SSNN($)

trainableparameters &Figure 1. The computational graph for (23) and (24) to estimate acoustic fields with unknown SSP.
We assume boundary conditions are unknown. This missing information introduces more unknown model
parameters as compared to the isovelocity waveguide case in Section 2.2.1. When SSP is provided, we can use
acoustic observations to find the optimal value of the trainable parameters:
T = {A, B, kr } ,(21)where A = (A1 , A2 , . . . , An ), B = (B1 , B2 , . . . , Bn ) and kr = (kr1 , kr2 , . . . , krn ).
The missing environmental knowledge makes it hard to estimate the number of contributing modes n precisely. When A and B are parts of the trainable parameters T , conservatively setting n to an upper bound ofits possible range and adding L1 -norm regularization terms of A and B to encourage sparse solutions can help
in model convergence. The loss function is updated to:L(r, z, p̂; T ) = | p̄(r, z; T ) − p̂|2 + α $A$1 + β $B$1 ,(22)

where α and β control the regularizations. With the trained optimal model parameters T ∗ , acoustic fields in a
non-isovelocity ocean waveguide with known SSP can be estimated using (10), (19) and (20).
Unknown SSP The detailed SSP across the water column is often unknown. The unknown SSP makes thecalculation of kzm (z) infeasible, even though the eigenvalue krm is provided. The MBNN is flexible to incorporate with standard NNs to model unknown physics. For example, we can implement a 1-input 1-output NN tomodel the SSP. We name the NN which models SSP as sound speed neural network (SSNN). Fig. 1 illustrates
the overall structure of the MBNN framework that incorporates SSNN.
In order to train the SSNN, the trainable parameter T defined in (21) is modified to:
T = {A, B, kr , S} ,(23)

where S contains all parameters (weights and bias) in the SSNN model. Calculating kzm (z) is feasible now
using the trained SSNN:(!
"2ωkzm (z) =− k2rm ,(24)
SSNN(z)where SSNN(z) is the estimated sound speed at depth z.
We employ the same loss function defined in (22) to learn optimal T ∗ . We then use (10) and (19) with (24)to estimate acoustic fields in a non-isovelocity ocean waveguide with unknown SSP.

MBNN model training

Acoustic field estimation

Formulate field expression(MBNN model) based on thenormal mode theory.

Define the MBNN trainable
parameters $.

location!, #MBNN modelCollect acoustic fieldmeasurements as training data.

Apply ML techniques to find the
optimal MBNN trainable
parameters $ *.

parameters$*pressure
amplitude!̅ #, %

known
environmental
knowledge
MBNN model input
MBNN model output

Figure 2. Key steps in the proposed MBNN framework in model training stage and field estimation stage.
2.2.3 Generalization to other mode models
We have illustrated a few MBNN formulations in range-independent ocean waveguides and demonstrated how
flexible the proposed modeling framework is in different scenarios. It is worth noting that formulations ofnormal mode models are application and environment specific. The idea of our MBNN modeling framework
generalizes well to tackle different scenarios and can be applied to any variant of classical normal mode models.
For example, the MBNN framework can incorporate adiabatic mode methods or coupled mode methods tomodel range-dependent environments [21–23].
Fig. 2 describes the steps involved in the MBNN model training stage and field estimation stage. For any
ocean environment, the key is to have an analytical field solution or an approximated field solution based on
the normal mode theory, and use a small number of acoustic measurements as training data to find the optimal
MBNN trainable parameters T ∗ . We can calculate other necessary physical quantities based on the trained
MBNN parameters so as to estimate acoustic fields at locations of interest.

3

SIMULATION STUDIESIn [24], the authors study acoustic propagation and hydrological conditions at the Hans glacier front in Svalbard. This paper lays a foundation for several follow-up studies at the glacier [25–27]. We loosely use theenvironment described in this paper as the basis for our simulation studies to illustrate how one might model
the acoustic propagation in a location where full environmental knowledge may be unavailable.
We assume that a work boat anchors at a location 1 km from the glacier. It carries an acoustic modem
that emits 500 Hz continuous wave signals for acoustic communication and environmental monitoring. A peer
survey boat executes exploration tasks in a region that is further away from the glacier. The bathymetry isapproximately flat with a constant water depth of 25 m in the region in which the peer vessel operates. Fig. 3
depicts a schematic of the simulated environment. We use the Kraken normal mode model [28] to generate
synthetic acoustic measurements in the simulated environment (with full environmental knowledge).
We then consider a scenario where the bottom properties and boundary conditions are unknown (and sounavailable to the model). We aim to model acoustic propagation from the work boat to a nearby region
around the peer vessel in Section 3.1 and infer the SSP using acoustic measurements collected at a constantdepth in Section 3.2.
In both cases, we need to train our MBNN using the simulated data. We randomly split the acoustic
measurements1 into a training dataset and a validation dataset based on a 70% : 30% ratio. The training dataset
1 The synthetic acoustic measurements used in simulation studies are peak-to-peak values from hydrophone output in millivolts.

5m1000 mDepth (m)25 mwork ship

glacierwaterpeer vessel

modem

hydrophone array

Distance (m)seabed* The schematic is not to scale.

Figure 3. Schematic of the simulated environment.

AOI
extended regionXmeasurement
regionextended region

training data
validation datameasurement region(2 m x 23 m)

1

1

-45

-60

dB

Depth (m)

-50

-70

24
550

-85

650

752

24

650
profile #1

651
profile #2

652
profile #3

Range (m)Figure 4. Ground truth field pattern in the AOI for field estimation problem with known SSP. The training data
and validation data used in the model training are labelled respectively.
trains the MBNN trainable parameters T , while the validation data is to implement early stopping [29] to avoid
over-fitting during the training. We use 30 modes in the MBNN formulation.
3.1 Acoustic field estimation
We consider two acoustic field estimation problems, one with known SSP and one with unknown SSP. We
assume a 24-element vertical hydrophone array with a 1 m inter-element spacing to the peer vessel to collectacoustic field measurements in the measurement region. We wish to accurately estimate acoustic field in an
area, without having to make measurements at all points in that area. To illustrate how this can be done, we
define a measurement region where we make several measurements, and two 100 m regions on both sides ofthe measurement region as extended region to demonstrate field extrapolation. The measurement region andextended region together form the area of interest (AOI).
In [30], the authors find that the use of PINN does not benefit acoustic field estimation performance ascompared to standard ML models. Our preliminary evaluation of PINN for this application agrees with thisfinding. We thus benchmark the field estimation performance of the proposed MBNN framework against twoclassical data-driven ML techniques – Gaussian process regression (GPR) and deep neural network (DNN). We

Table 1. Acoustic field estimation performance of the three models using a different number of profile measurements in the field estimation problem with known SSP.
Estimated field pattern
Model

MBNN

GPR

DNN

[RMS test error (mVpp )]measurements with profile #1

measurements with profiles #1&#2

measurements with profiles #1–#3[4.67][4.80][1.19][8.12][7.50][2.10][8.89][6.51][4.59]

design a composite kernel of a squared exponential isotropic kernel and a Matérn 5/2 ARD kernel for GPR, andimplement a 2-input 1-output DNN with 3 hidden layers and ReLU activation function. We randomly initialize
the MBNN and the DNN model parameters in each run. The hyper-parameters of the GPR model are fine-tuned
by minimizing validation error. We carry out 10 Monte Carlo simulations for MBNN and DNN models, andpresent the field estimation results with the smallest validation error.
3.1.1 Field estimation with knowledge of SSP
We consider an ocean waveguide with a known non-isovelocity SSP, unknown seabed properties and unknown
boundary conditions. We use the WKB approximation to formulate the MBNN model based on (10), (19)and (20). We use acoustic measurements to find optimal values of the trainable parameters T ∗ defined in (21).
We deploy the hydrophone array to collect acoustic measurements in a 2 m × 23 m measurement region at
three profiles, each spaced by a 1 m range in between. Fig. 4 shows ground truth field pattern in the AOI andthe three profiles where we collect the measurements. In order to investigate the field estimation performance,
we use the Kraken model to generate 464,600 test data with a resolution of 0.1 m in range and depth within

AOI
extended region

measurement
regionextended region

measurement region(50 m x 23 m)

1

1

-45

-60

dB

Depth (m)

-50

-70
24

-85

24
650

550

600

650

700

750

800

Range (m)training data
X validation data

700

Figure 5. Ground truth field pattern in the AOI for field estimation problem with unknown SSP. The collectedtraining data and validation data used in the model training stage are labelled respectively.
the AOI. We investigate data efficiency of the three models by estimating the field patterns in the AOI using
measurements collected at one profile (24 measurements), two profiles (48 measurements) or three profiles (72measurements) in the measurement region.
Table 1 shows the estimated field patterns and the corresponding root-mean-square (RMS) test errors in
the AOI when different amounts of acoustic measurements are given. When acoustic measurements made at
profile #1 & #2 are provided, the GPR and DNN models fail to extrapolate field patterns due to insufficienttraining data. The field estimated by the MBNN shows a rough field pattern with low fidelity. When themeasurements made at profile #1 – #3 are provided, the field estimated by the MBNN model aligns well withthe ground truth field pattern. The GPR can extrapolate more details in the extended region. The DNN still
performs poorly in extrapolation. The corresponding RMS test errors presented also justify our observations
that the MBNN model outperforms the GPR and DNN models in terms of data efficiency and extrapolation
performance.
3.1.2 Field estimation without knowledge of SSP
Sound speed in an ocean waveguide is measured by sending a conductivity, temperature and depth (CTD) sensor
at various depths. When either the CTD sensor or equipment to survey sound speed at various depths is lacking,we do not know the exact SSP. In this case, we assume that no sound speed measurement is available due tothe lack of a CTD sensor. Instead, we only have a rough understanding of a reasonable range that the SSP
may fall in. Our conservative initial guess of the SSP is that it falls in a 100 m/s range between 1,400 m/s and1,500 m/s and SSP variation should not exceed 35 m/s over the 25 m water depth.

(a) Ground truth(b) Estimation by MBNN(c) Estimation by GPR(d) Estimation by DNN

Figure 6. The estimated field patterns in the measurement region when SSP is unknown. Panel (a) shows theground truth field pattern. Panels (b)–(d) show the estimated fields by the MBNN, GPR and DNN models.

(a) Ground truth(c) Extrapolation by GPR(b) Extrapolation by MBNN(d) Extrapolation by DNN

Figure 7. The estimated field patterns in the AOI when SSP is unknown. Panel (a) shows the ground truth field
pattern. Panels (b)–(d) show the extrapolated fields by the MBNN, GPR and DNN models.
Table 2. Acoustic field estimation performance of the three models in the field estimation problem with unknown SSP.
Model

RMS test error (mVpp )In measurement region

In AOI

MBNN

0.039

0.87

GPR

0.011

2.10

DNN

0.10

2.88

Figure 8. The learnt SSP with the ground truth SSP in the field estimation problem. The SSNN is trained using
acoustic measurements sampled across the water column in the measurement region.
One may expect that more acoustic data is required to train the MBNN framework as the size of trainable
parameter T increases as compared to the previous scenario. As shown in Fig. 5, we uniformly collect 1,224
acoustic measurements (24 elements/profile × 51 profiles) within a 50 m × 23 m measurement region. We
assume the detailed SSP and seabed properties are unknown. We use a simple 1-input 1-output NN (SSNN)
with 1 hidden layer and ReLU activation function to learn the SSP in the water column. We formulate the
MBNN model based on the WKB approximation as (10), (19) and (24). We aim to estimate the acoustic
field pattern in the AOI by learning the optimal trainable parameters T ∗ defined in (23) through acoustic
measurements. We generate 575,000 acoustic measurements, with a resolution of 0.1 m in range and depth, as
the test dataset over the AOI to rigorously quantify the field estimation performance.

Figure 9. The trajectory of AUV labelled as an arrow in the inversion of SSP application.

Figure 10. The learnt SSP with the ground truth SSP in the SSP inversion application using acoustic measurements made at a nearly constant depth and a few sound speed measurements.
As shown in Fig. 6, all of the three models can interpolate acoustic fields in the measurement region well.
However, field patterns extrapolated in the extended region shown in Fig. 7 highlight the superiority of our
proposed MBNN framework over the GPR and DNN models. The test errors shown in Table 2 support theobservations we draw from Fig. 6 and Fig. 7. Although estimation of the SSP might not be of interest to thefield estimation problem, the learnt SSP by the SSNN is close to the ground truth SSP in the 100 m/s span asshown in Fig. 8. It demonstrates the flexibility of the MBNN model to incorporate with standard NNs to model
unknown physics.
3.2 Inversion for entire SSP
In Section 3.1.2, we have demonstrated that our proposed MBNN model can learn the SSP reasonably wellusing the acoustic field measurements collected at 50 profiles when a CTD sensor is not provided. However,consider a scenario where measurements are not available through the entire water column, but rather at a
few shallow depths only. Could one estimate the SSP in the entire water column with just a few shallow
measurements? The MBNN framework can incorporate standard NNs to model unknown physics – in this case,the SSP. This makes the MBNN framework a useful tool for solving various inverse problems related to acoustic
propagation modeling.
To illustrate the idea, we assume that an autonomous underwater vehicle (AUV) equipped with an acoustic
sensor and CTD is deployed from the peer vessel and dives to an operating depth of 4 m. It then operates at aconstant depth of 4 m, and therefore does not have access to profiles through the water column to make either
CTD or acoustic measurements. Fig. 9 indicates the AUV’s trajectory in the measurement region. The AUV
uniformly makes 5 sound speed measurements at depths between the water surface and the operating depth of

4 m. We aim to learn the entire SSP using the acoustic field measurements collected at a nearly constant depthwith the aid of just a few sound speed measurements made at shallow depths.
We assume boundary conditions and seabed properties are unknown. We use the same model formulation,
trainable parameters and initial guess of the SSP defined in Section 3.1.2. The lack of strong spatial diversityof the collected field measurements makes this inversion problem particularly challenging.
The learnt SSP, benchmarked against the ground truth SSP, is shown in Fig. 10. We constrain the SSNN
using the sound speed measurements made at the water surface, 1 m, 2 m, 3 m and 4 m in the loss function.
The learnt SSP over 25 m depth is very close to the ground truth SSP, even for depths where no acoustic
field measurement and sound speed measurement are provided. Training the MBNN model using acoustic measurements with stronger field variation can potentially reduce the training data size and improve the inversion
accuracy.

4

CONCLUSION

Conventional normal mode propagation models, although are widely used, require accurate prior environmental
knowledge. Environmental uncertainties thus significantly affect model estimation performance and thus limit its
applicability in practice. We proposed a physics-based data-aided modal acoustic propagation modeling framework based on the normal mode theory. The proposed framework embeds the normal model theory of acoustic
propagation into the structure of a NN, so as to enable a data-efficient propagation modeling framework. The
proposed model is flexible to incorporate any known environmental knowledge and standard NNs to model
unknown physics. At the same time, it extrapolates well and brings interpretability to the trained model parameters. We demonstrate the proposed modeling framework through several simulated application examples, andby benchmarking against classical ML techniques.

References[1] M. A. Çavuşlu, M. A. Altuncu, H. Özcan, F. K. Gülağız, and S. Şahin, “Estimation of underwater acoustic
channel parameters for erdek/turkey region,” Applied Acoustics, vol. 181, p. 108135, 2021.
[2] D. Tollefsen and S. E. Dosso, “Source localization with multiple hydrophone arrays via matched-fieldprocessing,” IEEE Journal of Oceanic Engineering, vol. 42, no. 3, pp. 654–662, 2016.
[3] Z. Li, R. Zhang, J. Yan, F. Li, and J. Liu, “Geoacoustic inversion by matched-field processing combinedwith vertical reflection coefficients and vertical correlation,” IEEE Journal of Oceanic Engineering, vol. 29,
no. 4, pp. 973–979, 2004.
[4] F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Wave Propagation Theory, pp. 65–153. New
York, NY: Springer New York, 2011.
[5] N. Baker, F. Alexander, T. Bremer, A. Hagberg, Y. Kevrekidis, H. Najm, M. Parashar, A. Patra, J. Sethian,
S. Wild, et al., “Workshop report on basic research needs for scientific machine learning: Core technologies
for artificial intelligence,” tech. rep., USDOE Office of Science (SC), Washington, DC (United States),2019.
[6] C. Rackauckas, Y. Ma, J. Martensen, C. Warner, K. Zubov, R. Supekar, D. Skinner, A. Ramadhan, and A. Edelman, “Universal differential equations for scientific machine learning,” arXiv preprint
arXiv:2001.04385, 2020.
[7] M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning
framework for solving forward and inverse problems involving nonlinear partial differential equations,”
Journal of Computational physics, vol. 378, pp. 686–707, 2019.
[8] C. L. Pettit and D. K. Wilson, “A physics-informed neural network for sound propagation in the atmospheric boundary layer,” in Proceedings of Meetings on Acoustics 179ASA, vol. 42, p. 022002, AcousticalSociety of America, 2020.

[9] C. Song, T. Alkhalifah, and U. B. Waheed, “Solving the frequency-domain acoustic vti wave equation
using physics-informed neural networks,” Geophysical Journal International, vol. 225, no. 2, pp. 846–859,
2021.
[10] M. Rasht-Behesht, C. Huber, K. Shukla, and G. E. Karniadakis, “Physics-informed neural networks (pinns)
for wave propagation and full waveform inversions,” arXiv preprint arXiv:2108.12035, 2021.
[11] S. Alkhadhr, X. Liu, and M. Almekkawy, “Modeling of the forward wave propagation using physicsinformed neural networks,” in 2021 IEEE International Ultrasonics Symposium (IUS), pp. 1–4, IEEE, 2021.
[12] K. Li and M. Chitre, “Data-aided underwater acoustic ray propagation modeling,” 2022.
[13] F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Ray Methods, pp. 155–232. New York, NY:
Springer New York, 2011.
[14] F. B. Jensen, W. A. Kuperman, M. B. Porter, and H. Schmidt, Normal Modes, pp. 337–455. New York,
NY: Springer New York, 2011.
[15] W. Miller, “The technique of variable separation for partial differential equations,” in Nonlinear Phenomena(K. B. Wolf, ed.), (Berlin, Heidelberg), pp. 184–208, Springer Berlin Heidelberg, 1983.
[16] R. B. Guenther and J. W. Lee, Sturm-Liouville Problems: Theory and Numerical Implementation. CRC
Press, 2018.
[17] M. B. Porter, “The kraken normal mode program,” tech. rep., Naval Research Lab Washington DC, 1992.
[18] A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, “Automatic differentiation in machinelearning: a survey,” Journal of Marchine Learning Research, vol. 18, pp. 1–43, 2018.
[19] M. Li, T. Zhang, Y. Chen, and A. J. Smola, “Efficient mini-batch training for stochastic optimization,”in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining,pp. 661–670, 2014.
[20] A. K. Ghatak, R. Gallawa, and I. Goyal, “Modified airy function and wkb solutions to the wave equation,”NASA STI/Recon Technical Report N, vol. 92, p. 20427, 1991.
[21] A. Nagl, H. Überall, A. J. Haug, and G. Zarur, “Adiabatic mode theory of underwater sound propagation ina range-dependent environment,” The Journal of the Acoustical Society of America, vol. 63, no. 3, pp. 739–
749, 1978.
[22] B. J. DeCourcy and T. F. Duda, “A coupled mode model for omnidirectional three-dimensional underwatersound propagation,” The Journal of the Acoustical Society of America, vol. 148, no. 1, pp. 51–62, 2020.
[23] G. Athanassoulis, K. Belibassakis, D. Mitsoudis, N. Kampanis, and V. Dougalis, “Coupled mode and finite element approximations of underwater sound propagation problems in general stratified environments,”
Journal of Computational Acoustics, vol. 16, no. 01, pp. 83–116, 2008.
[24] O. Glowacki, M. Moskalik, and A. Prominska, “Simulation of the sound propagation in an arctic fjord:
general patterns and variability,” 2013.
[25] O. Glowacki, G. B. Deane, M. Moskalik, P. Blondel, J. Tegowski, and M. Blaszczyk, “Underwater acoustic
signatures of glacier calving,” Geophysical Research Letters, vol. 42, no. 3, pp. 804–812, 2015.
[26] O. Głowacki, G. B. Deane, M. Moskalik, J. T˛egowski, and P. Blondel, “Two-element acoustic array gives
insight into ice-ocean interactions in hornsund fjord, spitsbergen,” Polish Polar Research, pp. 355–367,2015.

[27] O. Glowacki, M. Moskalik, and G. B. Deane, “The impact of glacier meltwater on the underwater noise
field in a glacial bay,” Journal of Geophysical Research: Oceans, vol. 121, no. 12, pp. 8455–8470, 2016.
[28] “Acousticstoolbox.jl.” https://github.com/org-arl/AcousticsToolbox.jl. Accessed: 2022-06-12.
[29] Y. Yao, L. Rosasco, and A. Caponnetto, “On early stopping in gradient descent learning,” Constructive
Approximation, vol. 26, no. 2, pp. 289–315, 2007.
[30] T. de Wolff, H. Carrillo, L. Martí, and N. Sanchez-Pi, “Assessing physics informed neural networks inocean modelling and climate change applications,” in AI: Modeling Oceans and Climate Change Workshop
at ICLR 2021, 2021.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0122

Research On Shallow Water Acoustics In Lake Kinneret (Israel)
Boris KATSNELSON 1
Department of Marine Geosciences, University of Haifa, Israel

ABSTRACT

Results of study of shallow water sound propagation carried out by the team of University of Haifa, arepresented including results of experiments and the corresponding theoretical modeling:- long range low and mid frequency (0.3 – 10 kHz) sound propagation with reception by vertical line array.
- mode composition and mode coupling were measured and modeled;- reflection and reverberation in the presence of gassy sediment using wideband source and receivers, both
single hydrophone and VLA;
- noise including shipping noise measurements and analysis.
Key words: shallow water acoustics, experiments in lake

1. INTRODUCTION

Freshwater Lake Kinneret (Sea of Galilee, Israel ) is located in the North of Israel, its size is about 12x24 km,
maximum depth was about 40 m. Sound speed profile (temperature profile) in the water column changes
seasonally (Fig. 2b). Near the lake there is Limnological Lab (IOLR) having a few research vessels, a large
set of oceanographic tools, including two anchored platforms (Ecorafts), buoys etc. It means that the lakeis good testing field for research in shallow water acoustics

Figure 1. Bathymetry of Lake Kinneret (left) and sound speed profile (right)

2. EXPERIMENTS
2.1. Oceanographic observations and characteristics, important for acoustical research
Oceanographic survey included measurements of temperature profiles (CTD and thermistor string,bathymetry (echosounder), and currents/internal waves (ADCP). These measurements were carried out frommoving vessel and stationary anchoring (during one year). Also direct measurements of sediment properties
were done using frozen cores .
3.1. Acoustical measuremets
1bkatsnels@univ.haifa.ac.il

a) Long-range propagation of wideband signals (200 Hz – 10 kHz) was studied using moving research
vessel and VLA fixed in the deepest place of the lake for different parameters of signal up to distance 5-7
km. Typical spectrograms for different distances source-receiver is shown in the Figure 2. We can see
transition from ray formed pattern to mode decomposition

Figure 2. Spectrograms for distances 5 km (left) and 500 m (right)b) Reflection and reverberation of wideband signals. In the Fig.3 typical sequence of received pulses where
the first puls (red color) denotes direct signal and the following pulses correspond to different reflections
from bottom and surface. Calculation of this sequence with fitting parameters, characterizing bottom allows
us to find, for example, methane bubbles concentration

Figure 3. Sequence of reflected pulses received by single hydrophonec) Shipping noise. Experiments on the measurements and usage of shipping noise were carried out in 2019 2022 yy. The first option was measurements using vertical line array and moving ship (R/V Hermona) during
5 min. After frequency filtering it is possible to select modes (see example in the Fig. 4)

Figure 4. Two waveguide modes at the frequency 48 Hz
Yere we can find attenuation coefficients of waveguide modes and use the fitting procedure to estimateparameters of bottom.
d) Research of currents and internal waves using ADCPs. Currents in the lake were studied using fixed
and towed Acoustical Doppler Current Profilers. There were two fixed ADCPs which recorded variations ofcurrents during 1 year. The corresponding pattern shows manifestation of internal Kelwin waves. Movong
ADCP was used as well to find cyclonic and anti-cyclonic currents in the lake.

ACKNOWLEDGMENTS
Work was supported by ISF grant 946/20

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0146

Retrieval of the dispersion curves of waveguide modes using
shipping noise recorded by two vertical line arrays
Marina YARINA1; Andrey LUNKOV2, Oleg GODIN3 and Boris KATSNELSON1
1
2
3

University of Haifa, Israel

General Physics Inst, Russia

Naval Postgraduate School, USA

ABSTRACT
A technique is proposed for estimating the dispersion characteristics of waveguide modes based on a comparisonof the shipping noise recorded by two synchronized vertical line arrays. Using the proposed approach, an
experimental study of the modal structure of a low-frequency sound field in a shallow-water waveguide (Lake
Kinneret, Israel) with a gas-saturated bottom was carried out in a wide frequency band from 20 to 250 Hz. Bottomof the lake is rich in methane bubbles (~ 1% by volume) and, consequently, has low sound speed of the order of
100 m/s. The receiving system was two 27-m vertical arrays located at a distance ~40 m from each other in 40.4
m-deep water. The noise source, a research vessel, was moving along a straight line in the vertical plane containing
the arrays up to 1 km range from them. The proposed approach made it possible to determine the frequency
dependences of the phase speed for the first 12 modes, and these dependences turned out to be close to those for a
waveguide with a perfectly soft bottom, except at the frequencies in the vicinity of the mode cutoffs.

Keywords: Shipping noise, Correlation function

1. INTRODUCTION. EXPERIMENTIn the Figure 1 layout of experiment is shown which was carried out in the central part of Lake Kinneret,where the depth is approximately 40.4 m; the sound velocity profile is characterized by a noticeable
jump at a depth of 10–15 m. The bottom of the water area is covered with gas-saturated sediments.
The receiving system consisted of two vertical synchronized chains of hydrophones fixed on thebottom near a moored platform, spaced approximately = 40 m from each other, the western (W), andthe other, the eastern (E). Each chain consisted of 10 hydrophones placed at an interval 3 m, covering
the depth range from 10 to 37 m.

Figure 1. Scheme of experimentFig. 1 shows the movement of the noise signal source, the R/V Hermona, along a straight line through botharrays, with the eastern chain always closest to the vessel. The study considers a time interval of ~5 min,1bkatsnels@univ.haifa.ac.il

during which the vessel moved at a speed of 4 m/s away from the arrays to a maximum distance of 1000 m.
Figure 2 (left) shows an example of the noise spectrum of the ship.

2. DATA PROCESSING AND RESULTS
We consider the field from a noise source recorded by the two arrays (E, W), 𝑃𝐸 (𝑡, 𝑧𝑗 ) and 𝑃𝑊 (𝑡, 𝑧𝑗 ),where 𝑧𝑗 - hydrophone depth, 𝑡 - "fast" time in the time interval of the ship's movement. The corresponding
spectrograms are 𝑃𝐸 (𝜔, 𝑇, 𝑧𝑗 ) and 𝑃𝑊 (𝜔, 𝑇, 𝑧𝑗 ), frequency 𝜔 = 2𝜋𝑓 and "slow" time T in the intervalfrom 0 to 5 min. Expansion coefficients of the spectrograms over waveguide modes 𝜓(𝑧𝑗 , 𝜔, 𝑞), asfunctions of the parameter q, frequency ω, and slow time T, where discrete depths of 10 hydrophones𝐸
𝐴𝐸 (𝜔, 𝑇, 𝑞) = ∑𝑁
𝑗=1 𝑃 (𝜔, 𝑇, 𝑧𝑗 )𝜓(𝑧𝑗 , 𝜔, 𝑞)𝛥𝑧 ,𝑊
𝐴𝑊 (𝜔, 𝑇, 𝑞) = ∑𝑁
𝑗=1 𝑃 (𝜔, 𝑇, 𝑧𝑗 )𝜓(𝑧𝑗 , 𝜔, 𝑞)𝛥𝑧

Figure 2. Spectrum of shipping engine (left) and function 𝑅(𝜔, 𝑞), white lines are dispersion curves (right)
Ratio of the amplitudes 𝐴𝐸 and 𝐴𝑊 , taking into account the compensation of the phase shift at the distanceΔ𝑟 between the antennas, take the real part and average over the slow time (or over the distance from theantennas to the ship), which will give:𝐴𝐸 (𝜔, 𝑇, 𝑞)𝑅(𝜔, 𝑞) = 〈𝑅𝑒 ( 𝑊
exp(𝑖𝑞Δ𝑟))〉 𝑇
𝐴 (𝜔, 𝑇, 𝑞)
Function 𝑅 in coordinates (𝜔, 𝑣𝑝ℎ ) where 𝑣𝑝ℎ = 𝜔/𝑞 is phase speed, is shown in the Fig.2 (right). White
0lines show dispersion curves for waveguide with 𝑐0 = min(𝑐(𝑧)) and soft bottom 𝑣𝑝ℎ,𝑚
(𝜔) =
z

𝜔
𝜔 2
𝜋𝑚 2
√( ) −( )𝑐0𝐻

If we are able to identify waveguide modes then we can choose waveguide parameters to get the best
agreement between dispersion curves, selected ib experiment and theoretical dispersion curves forfitting parameters.
So, the paper proposes a new approach to separating normal modes and estimating their parameters by
analyzing ship noise recorded by two synchronized closely spaced arrays. The advantage of thisapproach is that there is no need to know the exact coordinates and speed of the ship: it suffices toknow only that it is.
Note that the experiment described above in Kinneret Lake is the first attempt to use this approach.
The same method can apparently be applied in shallow water with an arbitrary trajectory of a noisy vessel,
if this trajectory is known. Due to the nature of the signal processing, a high degree of accuracy indescribing the ship’s trajectory is not required. This approach proved to be robust with respect to observed
strong pseudoacoustic noise.

ACKNOWLEDGMENT
This work was supported, in part, by RFBR 20-05-00119.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0224

Seismo-acoustic wave propagation generated by the detonation of
UXOs of large charge weights in a shallow water environment
Nathalie FAVRETTO-CRISTINI1; Anne DESCHAMPS2; David AMBROIS2; Mickaël BONNIN3; Éric
BEUCLER3; E. Diego MERCERAT4; Fang WANG1,*; Thierry GARLAN5; Xavier DEMOULIN6;
Michel ARRIGONI7; Paul CRISTINI1; Olivier MORIO5; Romain SCHWAB6,+; Vadim
MONTEILLER1
1
2

Aix-Marseille Univ., CNRS, Centrale Marseille, LMA UMR 7031, Marseille, France

Université Côte d’Azur, CNRS, Observatoire de la Côte d’Azur, IRD, Géoazur, Valbonne, France

3

Nantes Université, Univ Angers, Le Mans Univ, CNRS, UMR6112, Laboratoire de Planétologie
et Géosciences, Nantes, France
45

Équipe REPSODY, CEREMA Méditerranée, Sophia-Antipolis, France

Shom/DOPS/STM/SEDIM, Marine Geology Department, Brest, France
6

7

MAREE, Plœmeur, France

ENSTA Bretagne, Institut de Recherche Dupuy de Lôme, CNRS UMR 6027, Brest, France*+

Now with CGG, Massy, France

Now with ENSTA Bretagne, Lab-STICC, Brest, France

ABSTRACT
Unexploded historical ordnance (UXO) from World War II, that is discovered almost every week close to the
French coast, must be destroyed quickly after discovery to ensure the safety of divers and ships. The favored
destruction method is countermining, i.e., the use of a high-order detonation conducted by exploding an
additional donor charge placed adjacent to the UXO. In the framework of a UXO countermining campaign
conducted in the Rade d’Hyères (Mediterranean Sea, France) in December 2018, hydro-acoustic and seismic
recording systems have been deployed to record the explosion-induced waves in water and the seismic signals
on the shore, respectively (POSA project). In this expanded abstract, we present the main observations andfocus on the impact of the shallow water environment (whose water depth is less than 50 m), and more
specifically on the impact of the unconsolidated sedimentary layer, on the recorded signals induced by thedetonation of charges with weights ranging from 80 to 680 kg TNT-equivalent. We also discuss the acousticto-seismic wave conversions.
Keywords: Underwater explosion, wave propagation, seismo-acoustic signals

1. INTRODUCTION
Unexploded historical ordnance (UXO) from World War II are discovered almost every week close
to the north-western and south-eastern coasts of metropolitan France. Quickly after their discovery,
the French Navy Mine Warfare Office (FNMWO) must destroy the munitions to ensure the safety ofdivers and ships. The favored destruction method is countermining, i.e., to use a high-order detonation
conducted by exploding an additional donor charge placed adjacent to the UXO. Depending onwhether the UXO is safe to move, such countermining occurs at specific safe locations or at thelocation of the discovery.
The risks for people in charge of the UXO countermining are well known by the FNMWO experts.
1

favretto@lma.cnrs-mrs.fr

In contrast, it is difficult to reliably evaluate the possible consequences of underwater explosions on
the marine environment and on the buildings located on the coast. Indeed, they depend mostly on theenvironment geology and on the characteristics (weight and location) of the explosive charges and,hence, on the detonation-induced wave propagation. Large underwater explosions may trigger small scale landslides that could, in turn, generate large waves on the shore or damage infrastructures(pipelines, optic fibers). Therefore, there is a need for developing a decision support tool for the risk
assessment regarding inland infrastructures before the clearance of UXO of large weights.
One of the main goals of the POSA project, partly funded by the French Navy, was to pave the way
for reliably assessing the risk of building damage on the adjacent shore, induced by the detonation oflarge-charge UXO (of between 80 and 680 kg TNT-equivalent weights) in a variable shallow water
environment with a water depth less than 50-100 m. While the wave propagation generated by thedetonation of small charges (usually, smaller than a few-kilograms TNT-equivalent weight) is quite
well studied in the open literature (e.g., [1-3]), to the best of our knowledge, very few works areconcerned with charges of a few-hundred-kilograms TNT-equivalent weight and located in coastal
waters with a depth close to 50-100 m (e.g., [4-7]). In that respect, the POSA project can be considereda pioneer work.
To understand how the seabed (and possibly, the viscoelastic sedimentary layer with a varying
thickness) and the water column (with a varying depth) influence the propagation of the seismoacoustic waves that are generated by the UXO detonation and that reach the coast, we have relied ona multidisciplinary cross-study including real data obtained within the framework of controlledcountermining campaigns, and numerical simulations of the seismo -acoustic propagation using aspectral-element method. The countermining campaigns were conducted in December 2018 in the
Mediterranean Sea in the Rade d’Hyères (south-eastern part of France). The real data have beencollected by acoustic recording systems (namely, two hydrophones and one shock gauge) and by arelatively dense array of seismic stations (velocimeters, conventional accelerometers, andMicroElectroMechanical Systems (MEMS) accelerometers) located on the shore at a maximum of
15 km from the underwater explosion locations. Most of the results obtained from the analysis of thereal acoustic and seismic data and from the numerical simulations are reported in a two -companion
paper [8,9] and in a paper currently under review [10].
The present extended abstract summarizes some of the main results of the POSA project . Section
2 briefly describes the POSA experiment conducted in the Rade d’Hyères in December 2018. We refer
the reader to [8,9] for further details. Section 3 then presents some of the acoustic and seismic
explosion-induced signals collected by the acoustic and seismic recording systems, and also discusses
the impact of the charge characteristics on the recorded signals, as well as the impact of the locationof the explosion (on the seabed vs. in the water column). Finally, Section 4 investigates the influenceof the environment geology on the seismic explosion-induced signals recorded on the shore.

2. THE POSA EXPERIMENT
2.1 Characteristics of the experiment site
The Rade d’Hyères is approximately 15 km long in the E/W direction and 10 km wide in the N/S
direction. The water depth within the bay is less than 70 m (Fig.1a) and the sound speed in water was
assumed constant and equal to 1507 m/s. The seabed, whose physical and geometrical properties varyspatially, is composed of unconsolidated sediments lying over a bedrock. The sedimentary cover isgenerally less than 5 m thick within the bay, but locally reaches 30 m close to the western part of theshore (Fig.1b). Except close to this deep sedimentary basin, the sediment properties are globally
constant within the bay: measured density sed = 1550-2000 kg/m3 , measured P-wave velocity V Psed =1625-1750 m/s, measured P-wave attenuation Psed = 0.49-0.69 dB/m/kHz, estimated S-wave velocity
V Ssed ≈ 200 m/s, estimated S-wave attenuation Ssed = 40 dB/m/kHz. Depending on the nature of rocks,
the density, P- and S-wave velocities in the bedrock vary within the bay: measured density  rock =2600-2650 kg/m3, measured P-wave velocity V Prock = 4100-4450 m/s, measured S-wave velocity V Srock= 2700-2910 m/s. In the absence of information on their variation with depth, the rock properties wereassumed to be constant with depth.
2.2 Characteristics of the detonated charges
Eight explosive charges of TNT-equivalent weights ranging from 80 to 680 kg were detonated at
two specific locations in the bay (labeled 3TY and 3TZ, respectively, in Fig.1). The first seven (S1-S7) charges were placed on the sea bottom, while the last one (S8) was placed in a container in thewater column at ~11m below the sea surface. The source distance from the shore ranges from 6 to 13
km. At the locations 3TY and 3TZ, the water depth was 46 m and 29 m, respectively. We refer thereader to [8] for further details.

(a)

(b)

Fig.1. The experiment site, namely the Rade d’Hyères in the Mediterranean Sea (south-eastern part ofFrance): (a) bathymetry, (b) 3D map of the sediment thickness. The two locations, labeled 3TY and 3TZ,
are the locations where the UXO were detonated during the campaign of December 2018. The locations of
the temporary seismological stations deployed along the shore are labeled PS01-PS19.

2.3 Hydro-acoustic and seismic experiments
The hydro-acoustic recording system consisted of a shock-gauge transducer T11-Neptune Sonar,
with a nominal charge sensitivity of 0.07 pC/kPa, and two hydrophones Hi-Tech model HTI-96, withnominal sensitivities of −210 dB re 1 V/μPa (hereafter, referred to hydrophone H210) and −240 dB re
1 V/μPa (hereafter, referred to hydrophone H240), respectively. The two hydrophones have flat
responses (within 3 dB) over the frequency band 0.002-30 kHz. The shock-gauge transducer wasplaced at a water depth of ~10 m below the sea surface and at fairly close horizontal distances (~110270 m) from the explosive charge locations, whereas the two hydrophones were suspended at a water
depth of ~10 m and at distances equal to ~ 400 or 2800 m and ~ 3000 or 5900 m from the shot locations,
respectively. We refer the reader to [8] for further details.
The shock-transducer signals were recorded continuously, and the signal digitization occurred at asampling rate of 625 kHz, giving a time resolution of 1.6 μs. As the maximum pressure level that wasrecorded during the experiments was around 1 MPa, i.e. well below the limit of the measurement
system, there was no clipping. The hydrophone signals were recorded continuously, and the signal
digitization occurred at a sampling rate of 78.125 kHz, giving a time resolution of 12.8 μs. The limitof the whole measurement system was 6.68 kPa for the hydrophone H210 and 214.5 kPa for thehydrophone H240. We refer the reader to [8] for further details.
A temporary seismic network consisting of 20 three-component medium to broadband velocimeters
and accelerometers was deployed all along the shore on 17 sites at distances ranging from 6 to 13 km
from the explosion locations (Fig.1a). The seismological stations recorded continuously during theirinstallation period, including the seismic ground motion generated by explosions. The seismic signals
were recorded with sampling rates of 250 samples per second (sps) (velocimeters) and 500 sps(accelerometers), respectively, and then band-pass filtered between 0.1 and 45 Hz (for 250 sps signals)
or between 0.1 and 110 Hz (for 500 sps signals). We refer the reader to [8] for further details.
One of the originalities of the POSA experiment is the use of eight MEMS
(MicroElectroMechanical Systems) accelerometers (Sercel DSU3 -SA) in complement to theconventional velocimeters and accelerometers. Besides their low cost, these instruments have the great
advantage of being easily installed in remote locations without external power sources. Two of theeight MEMS accelerometers were collocated with conventional seismic stations at the rocky site PS05
and at the sandy site PS13 (Fig.1a).

3. ACOUSTIC AND SEISMIC EXPLOSION-INDUCED SIGNALS
3.1 Acoustic signals
Time-series data generated by the explosion of a charge of 80 kg TNT-equivalent and recorded by

the shock gauge transducer T11 are shown at different time scales in Fig.2. The signal associated withthe shock wave highlights the typical feature of a shock waveform, namely an instantaneous pressure
rise (with a peak pressure at 0.15 s) followed by an exponential pressure decay (Fig.2c). The firstbubble pulse arrives ~ 0.30 s after the primary shock arrival (Fig.2a), which is consistent with
empirical predictions [1]. The signal arriving at ~ 0.152 s (Figs.2b and 2c) is associated with the waves
reflected by the free sea surface and received before the completion of the bubble pulse from the direct
wave. The signal associated with the shock wave generated by the explosions on the seabed has thesame waveform, whatever the TNT-equivalent charge weight [8]. However, the shock waveform isvery different for the case of an explosive charge located in the water column, namely, the exponential
pressure decay following the pressure rise is largely missing because of a so-called « cut-off » (i.e. afast pressure drop), which is consistent with the results reported in theliterature (see discussion in [8]).

Fig.2. Signal generated by the explosion of an 80 kg TNT-equivalent charge on the seabed at 3TY, andrecorded by the shock gauge transducer T11 located at 110 m from the source. Because of pyrotechnic
delay, time 0.15 s cannot be considered as a reliable arrival time of the shock wave after detonation.

Time-series data generated by the explosion of a charge of 80 kg TNT-equivalent and recorded by
the two hydrophones H240 and H210 are provided in Fig.3. The signal associated with the shock wave
arrives at ~ 0.15 s. The first bubble pulse arrives ~ 0.30 s after this primary shock arrival on the two
signals recorded by the two hydrophones. The complexity of the longer range signal recorded by thehydrophone H210, compared to the shorter range signal received at the hydrophone H240, is due tothe effects of the shallow water environment on the wave propagation, mainly waveguide dispersion.
For the sake of brevity of this expanded abstract, we choose not to present all the signals recorded by
the hydrophones, since the signals have similar waveforms for similar detonation conditions, i.e. forsimilar charge weights and for similar shallow water environments. Nevertheless, the signal
waveforms depend on the charge location at a same experiment site. The signals associated with the
bubble pulse have globally higher amplitudes when the charge is located in a container in the watercolumn, rather than on the seabed, even if the charge detonated in water is of smaller weight. For
further details, we refer the reader to [8].

Fig.3. Signals generated by the explosion S3 (80 kg TNT-equivalent charge) located on the seabed at thelocation 3TY, and recorded by the two hydrophones H240 and H210 located at 326 m and 2983 m,
respectively, from the source. For a better comparison, both signals are block ed on the same arrival time
for the shock wave. Note also that the beginning of the shock wave signal (highest amplitude) recorded by
the hydrophone H210 is slightly clipped.

Spectral analysis was carried out through the estimation of the Power Spectral D ensity (PSD) for
the signals recorded by the shock gauge transducer T11 and the hydrophone H240. For the sake ofbrevity of this expanded abstract, we choose not to present the different spectra and we refer the reader
to [8] for details and figures. Nevertheless, we summarize here the main key points :i. The signal spectra are consistent for a given charge detonated at the same location (3TZ or
3TY, Fig.1a).
ii. Whatever the charge weight and wherever the explosion location, the spectra of the signals
recorded by the shock transducer (located « close » to the explosion) are quite similar andrelatively constant below 300 Hz. Above 300 Hz, the spectra exhibit a significant drop.
iii. Whatever the charge weight and wherever the explosion location, the hydrophone, althoughhaving a good sensitivity at low frequencies (LF), could hardly record the component s withfrequencies below 30 Hz, because of the waveguide cutoff frequencies.
iv. A detonation on the seabed generates lower frequencies (globally, up to 30 Hz) than a chargedetonation in the water column.
v. The shock wave signal mostly contributes to the HF components of the whole spectrum(frequencies above 100 Hz), whereas the first bubble pulse signal contributes to the LF part
(below 100 Hz with a peak around 30 Hz), which is consistent with information reported in
the literature (see discussion in [8]). Because of its contribution to the lower frequency part,the bubble pulse signal may be the most appropriate candidate to possibly generate seismic
risks, in particular in the presence of sedimentary basins. Indeed, the sedimentary basins may
lower the frequency content of the signals while locally amplifying their amplitude. This is
the well-known site effect observed in seismology, that can damage buildings.
3.2 Seismic signals
For the sake of brevity of this expanded abstract, we choose not to present all the seismic signals
and we refer the reader to [8,10] for details. Nevertheless, in order to show the impact of the weight
and the location of the explosive charges on the seismic signals arriving at the shore, we compare thevertical component of the ground acceleration induced by each explosion (S1 -S8) and recorded at thesame station PS05 (Fig.4a). Firstly, for a given location for explosions (3TY or 3TZ) and for similar
conditions (i.e. located on the seafloor), the signals are consistent. Secondly, the ground acceleration
is linearly related to the charge weight.

(a)

(b)

Fig.4. (a) Vertical component of the ground acceleration, generated by the explosions S1-S8, and recordedby the velocimeters at the station PS05 (see Fig.1). S1 and S2 correspond to a 80 kg TNT-equivalent charge
located at the site 3TZ, S8 to a 80 kg TNT-equivalent charge located at 3TY in the water column (WC), andS3, S7, S6, S5, S4 to a 80, 200, 400, 600, 680 kg TNT-equivalent charge, respectively, located at 3TY on
the seabed (SB). The amplitudes are graphically clipped between ±8 mm.s -2 for clarity of display. (b) Power
spectral densities (PSD) of the vertical ground-acceleration components induced by the explosions S3-S8.

Spectral analysis shows that the detonation of charges of weight larger than 80 kg TNT-equivalent
generates globally similar spectral responses for the vertical ground-acceleration components
recorded on the shoreline (Fig.4b). However, the amplitudes of the spectral responses increase withincreasing charge weight. Whatever the source, the most energetic peak at ~ 10 Hz is likely associatedwith bulk wave propagation. The secondary peak observed in the 0.8-2 Hz frequency range, but only
for the largest charges, is likely associated with interface/surface wave propagation.
Compared to a charge detonation in the water column, a similar detonation on the seabed generates
seismic signals of much lower frequencies (< 30 Hz) and higher amplitudes that propagate in theseabed (see e.g. the signals generated by the explosions S3 and S8, both corresponding to an 80 kg
TNT-equivalent charge, and the associated spectra in Fig.4), Detonating the charges directly on theseafloor, and not close to, makes the coupling between the source and the seafloor super-efficient [11].
Two empirical laws for the explosion-induced local seismic magnitude, as a function of the charge
weight, have been derived in [8] for the cases of a charge detonation in the water column and on theseabed, respectively. These two laws globally follow the same trend, but with a shift of 0.5 inmagnitude, since much less energy is transmitted downwards into the ground when the explosion takes
place at a shallow water depth. Note that, in the POSA experiment, the charge detonations (up to 680
kg TNT-equivalent) have generated seismic events of at most magnitude 2.9 on the Richter scale.
It is worth noting that the contribution of phases, that are not recorded by conventional sensors at
usual sampling rates (i.e. less than 250 sps), could be observed from the high sampling rate data (500sps) recorded by the MEMS accelerometers. Besides the P and S waves, and the interface/surface
waves that follow them, a high-frequency high-amplitude signal with a velocity close to the soundspeed in water could also be observed at some stations located on rocky sites with a thin sedimentary
cover [10]. This signal likely corresponds to an H-phase (following the IASPEI nomenclature), i.e. awave packet that propagates within the water column from the source to a location close to the shore(at the cutoff height of the acoustic modes) and that subsequently converts to seismic energy before
being recorded by the seismic sensor. In addition, at some seismic stations, an energy packet with anapparent velocity close to the sound velocity in the air could also be observed when the largest charges
were detonated on the seabed [10]. This is likely an I-phase (following the IASPEI nomenclature), i.e.
an acoustic phase that propagates in the air from the explosion location.

4. INFLUENCE OF THE ENVIRONMENT GEOLOGY ON THE SEISMIC SIGNALS
Fig.5. illustrates the seismic ground motion induced by the detonation of a 400 kg TNT-eq. charge(S6) located on the seabed at the site 3TY and recorded by the network of 20 three-component (3C)
velocimeters and accelerometers deployed all along the shore of the Rade d’Hyères (Fig.1a).
The global signal waveforms and durations greatly differ according to the explosion -station
distance, and according to the ground properties along the propagation path as well. The sediment
thickness seems to have a significant influence since for the stations PS09-PS17 installed on sites withseveral meters of sediments below, the first P-wave arrival is not as impulsive as the one observed at
the other sites. Most importantly, the signal duration is much longer, with the presence of latedispersive signals with a very LF content and, for some stations, large amplitude. T he largest
amplitudes are generally observed for S waves and surface waves, with amplifications that can besignificant on the horizontal components (e.g., stations PS10 and PS17). These characteristics are
specific to the well-known site effects induced by the sedimentary basin [10] and can be well explained
and illustrated by full-wave numerical simulations of wave propagation [9]. For the sake of illustration,
Fig.6 shows how the different types of waves (namely, P and S waves, interface and surface waves ofStoneley-Scholte (SS), and Rayleigh or Rayleigh-Sezawa (Rayl) type) interact with the physicalproperties and the geometry of the marine environment (including the spatially-varying sedimentarylayer), and how they evolve along the propagation path between the explosion location and the station
location. However, from Fig.5 no strong conclusion can be drawn from the observed differences inthe signal amplitudes. Indeed, several factors, including the source-station distance and the conditionsof the station set-up on the rocky or sedimentary sites, may also impact the wave amplitudes.
In [10] we further investigate the importance of the sedimentary cover on the frequency content of
the seismic signal recorded at the shore. The thick sediment layer is shown to generate importantseismic amplification (H/V ratio up to 20) of the relatively low-frequency energy (1 to 5 Hz). In [10]we evidence that the sedimentary cover has to be taken into account to mitigate potential nuisance onland for large charge weights in shallow water environments.
It is worth noting that, although the largest charge (680 kg TNT-eq.) detonation on the seabedgenerated a seismic event of magnitude 2.9 on the Richter scale, no infrastructure located on the shoreof the Rade d’Hyères was damaged. For the sake of illustration of the seismic response on nearby
civil engineering structures, two velocimeters were located at the top and the bottom (station PS01),
respectively, of the bell tower of the Ste-Anne church located on Porquerolles Island (Fig.1a). During

the POSA experiment, this masonry structure faced motion amplification in response to the explosioninduced seismic signal, but that deformation was well beyond the damage initiation threshold [8,10].

Fig.5. 3C ground accelerations (GA) induced by the explosion of a 400 kg TNT-eq. charge located at 3TY
and recorded at PS01-PS19 (Fig.1a). Each signal amplitude is normalized by the maximum amplitude of
the corresponding GA. The signals are low-pass filtered at 20 Hz. All the traces are plotted with the same
origin time, and the amplitudes are graphically clipped between ± 5 mm.s -2 for display clarity. Sandy color:sites with a thin sedimentary layer (0-1 m), red-pink and blue colors: sites with a thick one (see Fig.1b).

Fig.6. (Left) Bathymetry, geometry of the sedimentary layer, and location of the fictitious horizontal
receiver array (colored dots) along the line 3TY-PS13. (Right) Focus on the first 10 s of the simulated
seismograms related to the horizontal component of the velocity. For the sake of better visualization, thesignals obtained at the different offsets are represented at different amp litude scales. See [9] for the
explanations of the different labels related to the different kinds of waves. The numerical simulations were
performed using a spectral-element method.

5. CONCLUSION
This expanded abstract summarizes some of the main results related to recorded acoustic andseismic signals induced by the detonation of large charges (with weights up to 680 kg TNT-eq.) in theframework of a UXO countermining campaign conducted in a variable shallow water environment,
namely the Rade d’Hyères in the Mediterranean Sea (France) in December 2018 (POSA project). As
expected, the acoustic and seismic signal amplitudes increase with increasing charge weight. A
detonation on the seabed generates lower frequencies (globally, up to 30 Hz) than a charge detonationin the water column. The variable environment characteristics, in terms of bathymetry, thickness ofthe sedimentary layer, and physical properties, have a significant influence on the complex wave
propagation towards the shore and on the acoustic-to-seismic conversions. In particular, the presenceof sedimentary basins can induce site effects, i.e. large amplification of the S-wave andinterface/surface-wave amplitudes, increase in the signal duration and shift of the spectrum towards
the very low frequencies.

ACKNOWLEDGEMENTS
This work was supported in part by The French National Research Agency (ANR) and in part by
the DGA (French Ministry of Defense Procurement Agency) under Grant ANR-15-ASTR-0001 POSA.
This work was granted access to the seismic resources of Résif-EPOS that is a Research
Infrastructure (IR) managed by CNRS-Insu. Inscribed on the roadmap of the Ministry of Higher
Education, Research and Innovation, the Résif-EPOS IR is a consortium of eighteen French researchorganizations and institutions. Résif-EPOS benefits from the support of the Ministry of EcologicalTransition. Data from the POSA experiment are distributed through the Résif-EPOS webservices(https://seismology.resif.fr) under the network code ZH (Deschamps A. and Beucler E., POSA
experiment. RESIF - Réseau Sismologique et géodésique Français, 2013).
This work was also granted access to the French HPC resources of TGCC under allocation gen
7165 and mam0305 and of CINES under allocationA0020407165 andA0030410305, both made by
GENCI, and of the Aix-Marseille Supercomputing Mesocenter under allocations h025.
The authors greatly acknowledge the Préfecture de la Méditerranée, the Groupe des Plongeurs
Démineurs and the Force de Guerre des Mines for their management of the counter mining operations.
X. Mathias, P. Guyomard, E. Brenon, H. Gauduin, M. Gosselin, J. -P. Boivin, and F. Jourdin(Shom/GHOA) are acknowledged for the hydrographic and geological surveys. Semantic -TS and P.
Bernardi are acknowledged for their help with the acoustic surveys. X. Martin and J. Chèze (Géoazur),Ph. Langlaude and M. Pernoud (Cerema), and M. Perrault (Cerema, now at Sercel S.A.) are also
acknowledged for their help with the seismic surveys.

REFERENCES
1. Chapman, NR. Measurement of the waveform parameters of shallow explosive charges. J Acoust Soc
Am. 1985;78(2):672-681.
2. Nolet G., Dorman LM. Waveform analysis of Scholte modes in ocean sediment layers. Geophys J Int.
1996; 125:385-396.
3. Soloway AG., Dahl PH. Peak sound pressure and sound exposure level from underwater explosions inshallow water. J Acoust Soc Am. 2014; 136(3):EL218–EL223.
4. Hunter KS., Geers TL. Pressure and velocity fields produced by an underwater explosion. J Acoust Soc
Am. 2004;115(4):1483-1496.
5. von Benda-Beckmann AM. et al. Assessing the impact of underwater clearance of unexploded ordnanceon harbour porpoises (Phocoena phocoena) in the Southern North Sea. Aquat Mamm. 2015;41(4):503523.
6. Gitterman Y., Sadwin LD. Blast wave observations for large-scale underwater explosions in the Dead
Sea. In: Ben-Dor G., Sadot O., Igra O. editors. 30th International Symposium on Shock Waves 2.
Springer International Publishing; 2017. p. 1315-1319.
7. Salomons EM. et al. Noise of underwater explosions in the North Sea. A comparison of experimental
data and model predictions. J Acoust Soc Am. 2021; 149(3):1878-1888.
8. Favretto-Cristini N. et al. Assessment of risks induced by counter-mining unexploded large-charge
historical ordnance in a shallow water environment: Part 1. Real case study. IEEE J Ocean Eng. 2022;
47(2): 350-373.
9. Favretto-Cristini N. et al. Assessment of risks induced by counter-mining unexploded large-charge
historical ordnance in a shallow water environment: Part 2. Modeling of seismo-acoustic wave
propagation. IEEE J Ocean Eng. 2022; 47(2): 374-398.
10. Bonnin M. et al. Short range recordings of shallow underwater explosions with short period and broadband seismometers in the Bay of Hyères, France. Submitted to Bull Seismol Soc Am. 2022
11. Garlan T. et al. Circular sedimentary figures of anthropic origin in a sediment stability context. J Coast
Res 2018; SI 85:411-415.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0246

Long-range propagation modeling of offshore pile driving noisebased on the configuration of the support structure
Huikwan Kim1,2; James H. Miller1; Gopu R. Potty1 and Jennifer Amaral3
1
2

University of Rhode Island, USA

Naval Engineering & Shipbuilding, Naval Force Analysis, T&E Group, Republic of Korea
3

Marine Acoustics Inc, USA

ABSTRACT
Offshore impact pile driving noise is radiated and propagated through the water column and ocean bottom.
Predicting noise levels around wind turbine support structures at sea is required to estimate the effects of thenoise on marine life. Finite Element (FE) and Parabolic Equation (PE) models were used to predict longrange propagation of noise from the installation of offshore wind turbine foundations. FE analysis produced
acoustic pressure outputs at short ranges which were used as a starting field for the PE propagation model.
Monopile (axisymmetric model) and jacket with raked pile (3D model) wind turbine support structures wereconsidered. The latter presents a particular complication regarding azimuthal dependency due to the nonaxisymmetric geometry of the pile. The MMPE model was modified to accept the FE model’s vertically
distributed acoustic pressure output in the vicinity of the pile as its starting field. Frequencies up to several
hundred Hz were considered in the modeling since this is the range where most of the acoustic energy from
impact pile driving is concentrated. The long-range acoustic pressure field outputs from impact pile driving
in a shallow water environment around Block Island, Rhode Island will be presented.
Keywords: Sound Exposure Level, Monopile, Raked pile

1. INTRODUCTION
Acoustic energy is created when impact pile driving is used to construct offshore wind turbine
support structure and the sound radiates into the water along different paths. The potential paths
include,1. From the top of the pile where the hammer hits, through the air, into the water.
2. From the top of the pile, down the pile, radiating into the air while travelling down the pile,from air into water.
3. From the top of the pile, down the pile, radiating directly into the water from the length of thepile below waterline.
4. Down the pile radiating into the seabed, travelling through the seabed into the sediment andradiating back into the water.
Acoustic energy arriving from different paths with different phase and time lags creates a patternof destructive and constructive interference near the pile and water (and bottom) borne energy radiates
further away from the pile.
To simulate long range propagation of offshore pile driving noise based on the configuration of the
support structure, a combination of Finite Element (FE) and Parabolic Equation (PE) model approachhas been used. The FE model is ideal for short range calculations of acoustic pressure from complex
structure, but it becomes computationally unsustainable when the size of the model is increased dueto the mesh size requirement for long range numerical domain. It is also difficult to handle
environmental inputs such as depth dependent sound speed profile or range dependent bathymetry. In
contrast, the PE model is ideal for long range propagation, with a starting field and appropriate
environmental data. The PE model can handle range dependence of the environmental parameters.
1hkkim524@my.uri.edu

The Monterey-Miami Parabolic Equation (MMPE) model is used in this study which accepts inputs
such as source depth, array length, center frequency, frequency bandwidth and number of frequencies
along with environmental inputs which define the medium properties.
In one of the earlier studies, Reinhall & Dahl (1, 2) detailed a numerical approach to predict
offshore impact pile driving noise. Other studies which presented numerical approaches to predict
long range propagation of noise radiation and investigated potential effects on marine life include
Potty et al., (3) Kim et al., (4,5) Miller et al. (6). Wilkes (7) and Amaral et al. (8) used axisymmetric
FE model to simulate a line array of point sources along the axis of the vertical pile. To represent a
raked pile, they rotated the vertical line array by the inclination angle and calculated the Green’s
function. However, this study uses a direct approach based on 3D FE model for four raked piles
surrounded by water column and ocean bottom to calculate acoustic pressure. To model four raked
piles in FE, we adopted the geometry and the environmental information in Amaral (8). Specifically,
we used the geometry, material properties, and environmental parameters fro m Block Island Wind
Farm (BIWF) which is the first offshore wind farm in the U.S. coastal waters. Details of this study
will be presented by the following sections.
Section 2. describes FE modeling method and results for the case of axisymmetric and 3D models
for the different configuration of offshore wind turbine support structure such as monopile and raked
pile. Section 3 presents the generation of starting field for the MMPE mod el based on the results from
FE models and corresponding results for the long-range prediction of acoustic pressure outputs.
Section 4 provides the conclusions by discussing the advantages and disadvantages of FE combinedwith PE approach as one of the methods of long-range propagation model of offshore impact piledriving.

2. Finite Element modeling
To predict the radiated acoustic pressure from the raked pile support structure using FE analysis,
the following process has been adopted. At first, monopile support structure was modeled using
axisymmetric and the model output were compared with published data from Reinhall and Dahl (2)thereby ensuring that the FE model produces reasonable results. To make computation less intensive,
axisymmetric model for modeling the monopile was used by assuming azimuthal independence. Thenthe axisymmetric model has been extended to same geometry with a 3D model and the acousticpressure outputs from axisymmetric and 3D were compared. Finally, four raked piles are modeled
based on the pile geometry and material properties of the pile, water, and bottom provided by Amaral
et al. (8). The acoustic pressure outputs as a function of depth in the vicinity of raked piles are thenused as the starting field for the acoustic propagation model (MMPE) to predict the acoustic field atlong ranges.
2.1 Benchmark model
We used FE model to simulate Reinhall & Dahl’s work (2) as a benchmark model. FE analysis
using Abaqus/CAE used implicit dynamic analysis by applying pressure due to impact loading 𝑝(𝑡)as function of time on top of the pile.
𝑡(1)
𝑝(𝑡) = 2.1 × 108𝑒 −0.004 (𝑝𝑎)The length of the pile is 30.2m with thickness 25.4mm and radius 0.762m. The water depth is
12.5m and the sediment depth is 10m. A Vertical Line Array (VLA) measured acoustic pressure
outputs the at ranges 8m, 12m, and 15m away from the pile. The water and bottom are modeled as
acoustic medium. A Perfectly Matched Layer (PML) was set at the boundary to remove unwanted
reflection which simulates infinite acoustic medium. We also set series of nodes in FE model to record
acoustic pressure outputs at the same location of VLA.
The FE results provide radiated acoustic pressure in the medium due to impact pile driving atdifferent time steps. Mach wave is generated due to different phase speed in steel, water, and bottom.
The angle of Mach wave is 17.22° in the water and 18.9° in the bottom. A calculation of Sound
Pressure Level (SPL) for the first Mach wave arrival is compared with Reinhall & Dahl’s measured
data (2). The solid lines with different colors represent the result of our FE model and the measured
data from Reinhall and Dahl (2) at the VLA locations (8m, 12m, 15m respectively) are shown asrectangles, triangles, and circles in Figure 1 (right panel). It can be seen that the Mach wave from our
FE analysis compared well with the published data at the VLA locations (Reinhall and Dahl (2)).

Figure 1 – Acoustic pressure outputs at time t=4, 8, 12, 16 milliseconds. (left panel), The first-arrival
pressure amplitude in dB re 1μPa as a function of depth. (right panel)
2.2 FE modeling of monopile
Based on the results of the benchmark model, we modelled monopile support structure usin g both
axisymmetric and 3D elements to confirm that they get similar acoustic pressure outputs . We used thesame pile configuration and material properties as in Amaral et al (8) and the material properties used
are shown in Table1.
Table 1 – Material properties inputs to the FE modelParameters

Water

BottomSteel

Density (𝜌, 𝑘𝑔/𝑚 )

1025

1850

7850

Bulk Modulus (𝐸, 𝑃𝑎)

2,306,250,000

5,994,000,000-

Young’s Modulus (𝐸, 𝑃𝑎)--

210,000,000,000

Poisson’s ratio

-

-

0.3

3

The pile length, diameter, and thickness are 62.5m, 1.524m, 0.0445m respectively. Water
depth is 24m and the depth of penetration of the pile into the bottom is 14m and the sameloading condition in the benchmark model was used. We applied 89 nodes which arevertically distributed and record acoustic pressure time history at range 5m from the pile
support structure. The element spacing in water and bottom as an acoustic medium is 50cm
which covers frequency range up to 500Hz based on the mesh size requirement that 5 or 6
elements per shortest wavelength corresponding to highest frequency of interest. In the caseof water, sound speed is 1500m/s and wavelength at 500Hz is 1.5m. In the FE model, it ispossible to solve for higher frequency with finer mesh size, but it requires higher computation
power and longer running time.

Figure 2 –Acoustic pressure outputs at time t=9, 12 milliseconds. (Left panel), the peak SPL and SEL
comparison for the case of monopile with 3D and axisymmetric model
Figure 2 shows two different model with same problem of interest the peak SPL (Sound Pressure Level)

and SEL (Sound Exposure Level) were plotted. The peak SPL and SEL are defined as follows,
𝑆𝑃𝐿𝑝𝑒𝑎𝑘 = 10𝑙𝑜𝑔10
𝑆𝐸𝐿 = 10𝑙𝑜𝑔10

max (|𝑝(𝑡)|2 )𝑝𝑟𝑒𝑓 2𝑑𝐵 𝑟𝑒 1𝜇𝑃𝑎

∑𝑁 |𝑃 (𝑓)|2 ×𝑑𝑓∫|𝑃(𝑓)|2 𝑑𝑓= 10𝑙𝑜𝑔10 𝑛=1 𝑛 2
𝑝𝑟𝑒𝑓 2𝑝𝑟𝑒𝑓

𝑑𝐵 𝑟𝑒 1𝜇𝑃𝑎

(2)
(3)

Both depth dependent axisymmetric and 3D FE results are relatively similar in water column andocean bottom, however there are 1~2 dB difference between two models especially in water column(depth 0 meter to 24 meters). We applied all the same parameters for both the models, and the reason
for the difference in model outputs is still unknown, and it is going to be explored further in our future
study.
2.3 FE modeling of raked pile
The result of axisymmetric model compared well with published data from Reinhall & Dahl (2)and the new axisymmetric model for monopile (Amaral et al., (8)) also compared with 3D model formonopile (within 1 to 2 dB). Having successfully validated our FE model, we proceeded to model
four 3D raked piles surrounded by acoustic media such as water and ocean bottom. The Block Island
Wind Farm (BIWF) consists of total five 6 Megawatts turbines located three miles southeast of Block
Island, Rhode Island, USA. Each turbine foundation has four legs that are referred to as A1, A2, B1,
B2 (Figure 3 from (8)). Figure 3 shows the specific dimensions of the single raked pile and water andbottom as an acoustic medium and how four raked piles are modeled in FE assembly module (top andisometric view). To make our 3D raked FE model realistic, four piles were inclined by 13.27° toward
to center of the jacket type support structure. Four piles and water column and ocean bottom are
combined by subtracting overlapped part in water and bottom where raked piles occupied in acoustic
medium. It is important to set “tie constraint” between two different surfaces by sharing same physicalquantities. For example, steel pile is tied to inside and outside water and bottom, and we applied a
total of 21 “tie constraints” in FE model.

Figure 3 – Dimensions of single raked pile and surrounding medium for the FE model. (left panel), The
configuration of four raked piles (top view) and its modeling in the FE (right panel)
The purpose of this study is to predict the characteristics of long-range propagation of inclined(raked) offshore wind farm support structure. Differences in the acoustic field depending on whether
the pile is inclined toward the receiver, or it is inclined away from the receiver will also be explored.
During the construction of raked impact pile driving, measurement systems such as geophone andtowed array and vertical line array are located in the of southeast direction. To compare the differences,
we picked the raked pile legs A2 and A1. The leg A2 is inclined toward the receiver and the leg A1
had a different inclination. Also, the distance from leg A2 to the receiver is shorter than that of the leg
A1. We defined 89 nodes to record acoustic pressure time history at the edge of the FE model’s
numerical domain. This time history will be used as the starting field for the long-range propagation
model (modified MMPE model).
The Fourier Transform of acoustic pressure in time domain (𝑝(𝑡)) provided the depth dependent
real and imaginary part of acoustic pressure as a function of frequency. This was then used as thestarting field for the MMPE. The frequency band of interest is identified from the result of the plot
for the spatial average of the magnitude of complex acoustic pressure on vertically distributed nodes.
The dominant frequency as defined in equation (4) and its contribution to long range propagation inwater column and ocean bottom are important.
̅̅̅̅̅̅ = 1 ∑𝑁𝑃(𝑓)|𝑃 (𝑓)|
𝑁 𝑖=1 𝑖

(4)

Figure 4 shows the spectrum of dominant frequencies for the case of raked pile leg A1 and leg A2.
Overall, the pressure magnitude of pile leg A2 is greater than pile leg A1 because the leg A2 is inclined
toward in the direction of receiver and the leg A1 is inclined away from receiver. To cover these
dominant frequencies, the frequency band of interest of the modified MMPE model is set to 80Hz ~1000Hz. The upper high frequency limit corresponds to the mesh size of FE model which is 0.5m.

Figure 4 –Spatial average of the magnitude of Fourier transformed acoustic pressure time history.

3. Parabolic equation modeling
The goal of this study is to investigate the long-range propagation from structure borne noise andvibration. It is required to input complex acoustic pressure at each frequency along the pile as astarting field for the modified MMPE. This study modified the MMPE input files and developed apost processing Matlab script to accomplish broadband calculation. The modified co de enables theiterative broadband analysis with single run. To define vertically distributed complex acousticpressure as function of frequency, we took Fourier transform of the acoustic pressure time history,
from the implicit dynamic analysis in the FE model, using equation (5). Numerical domain of the FEmodel (24-meter water and 20-meter bottom) was extended to the numerical domain of the modified
MMPE model (24-meter water and 114-meter bottom).
∞𝑃(𝑓) = ∫ 𝑝(𝑡) 𝑒 −𝑗𝜔𝑡 𝑑𝑡 (𝑃𝑎)

(5)

−∞

Figure 5 –Modified MMPE starting field and corresponding results for a few dominant frequencies

Figure 5 shows MMPE starting field for the frequencies of 206.7Hz, 284.8Hz, 394.7Hz which area few dominant frequencies within the frequency range of interest. Real and imaginary part ofvertically distributed complex acoustic pressure at each frequency were used as the starting field forlong-range propagation model MMPE.
The modified MMPE iteratively accepted starting field for different frequencies and calculated
acoustic pressure and SPL field output up to a range of 7 kilometers. We applied zero-padding before
taking Fourier Transform of acoustic pressure time history to get better frequency resolution. The
frequency resolution for the model runs in this study was 0.81Hz and a total of 1130 runs covered
frequencies from 80.6Hz to 1000.2Hz. Based on our current FE model with mesh size 50 centimeters,
there are significant contribution from 100Hz to 400Hz frequency band and comparatively lower
contribution from the frequencies higher than 700Hz. The total energy in the frequency band used in
the study, SEL (Sound Exposure Level) in dB re 1𝜇𝑃𝑎2 , can be approximated as the sum of themagnitude squared of complex acoustic pressure multiplied by frequency spacing as indicated inequation (3).
Figure 6 shows cumulative SEL field output in dB re 1𝜇𝑃𝑎2 as function of depth and range for thecase of raked pile leg A1 and A2. Generally, it is observed that the SEL remain high in the watercolumn which indicates that the acoustic energy generated by impact pile driving propagates in waterwith some penetration into the bottom. The difference between two raked piles inclined toward (legA2) and away (leg A1) in the direction of receiver can be compared visually from the left and middle
panels which are plotted with the same dynamic range. The acoustic energy radiated and propagatedby the raked pile leg A2 is more prevailing than the case of the pile leg A1. Quantitatively, we extracted
SEL outputs as function of range at the water-bottom interface. The pile leg A2 has 3 dB higher SEL
in average with a maximum of 8 dB difference. The difference is minimum or reversed for the range
from 10 meter to 70 meters.

Figure 6 –SEL output of the raked pile leg A1 and A2 (left and middle panel), The comparison of SEL fromraked pile leg A1 and A2 outputs at the water-bottom interface (right panel)

4. Conclusions
This study provides a numerical approach for the long-range propagation modeling of acousticpressure from the raked pile driving used in the construction of the Block Island Wind Farm in Rhode
Island, USA. The staring point was validation of our FE modeling for monopile type offshore impact
pile driving with measured data from a previous study (2). Due to monopile’s azimuthal independence,other researchers also used axisymmetric model instead of 3D model for lowering the computational
effort. We developed a numerical approach for the raked pile support structure which requires a 3Dmodeling effort. In order to validate our 3D model, we generated two FE models for monopile(axisymmetric and 3D type element) and compared the FE results from the two models to ensure thatthe results are acceptable within allowable differences between them. Our goal is to predict the
acoustic field, not only in the near field of the pile, but also at long ranges. To do that, two numericalmodels (FE and PE model) are coupled and with the FE output acting as the starting field for PE.
Offshore impact pile driving noise is impulsive in nature and hence we generated vertically distributed
acoustic pressure at different frequencies and modified MMPE model to accept it as a starting field
for long range prediction of noise.
The results indicate azimuthal dependence of the pressure field based on the direction of inclinationof the raked pile in relation to the receiver. The 3D modeling we tried in the paper by providing generalapproach for developing a coupled FE and PE model is a valuable tool for the prediction of radiatednoise from offshore pile driving, specifically for non-axisymmetric scenario. Based on detail
information such as hammer impact, ocean acoustic environment like bathymetry, range dependent
sound speed profile, etc., the result of modeling can be further refined to be more realistic. We also
used mesh size for the FE model which is computationally affordable but better computing power canbe used, when available, to run finer mesh size quickly.

ACKNOWLEDGEMENTS
As the first named author of this paper, it is important for me getting back to research field in 8
years. A motivation was an invitation letter to attend the ICA 2022 from Professor Stephan Lippert.
My doctoral advisors Professor Miller and Potty encouraged me to do research again. I appreciate all
their effort and consideration.

REFERENCES
1. Reinhall et al. Acoustic radiation from a submerged pile during pile driving. OCEANS 2010
2. Reinhall et al. Underwater Mach wave radiation from impact pile driving: Theory and observation. J
Acoust Soc Am. 130, 1209(2011)
3. Potty et al. Acoustic radiation from offshore wind farms. Proceedings of the 2011 International Symposium
on Ocean Electronics, SYMPOL (2011): 178-184
4. Kim et al. Long range propagation modeling of offshore wind turbine construction noise using Finite
Element and Parabolic Equation models. 2012 Oceans - Yeosu, 2012, 1-5
5. Kim et al. Predicting underwater radiated noise levels due to the first offshore wind turbine installation in
the U.S. The Proceedings of Meetings on Acoustics, 2013 19: 040067
6. Miller et al. Pile driving pressure and particle velocity at the seabed: Quantifying effects on crustaceans
and groundfish J Acoust Soc Am. 136, 2206(2014)
7. Wilkes. Sound radiation from impact-driven raked piles. J Acoust Soc Am. 142, 1(2017)
8. Amaral et al. Characterization of impact pile driving signals during installation of offshore wind turbine
foundations. J Acoust Soc Am. 147, 2323(2020)

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0328

Contemporaneous measurements of mid-frequency transmission
and reverberation in shallow water
Taebo SHIM1; Su-Uk SON2; Young Nam NA3; Todd HEFNER4;Dajun TANG5;
1, 4, 52 ,3Applied Physics Laboratory, University of Washington, USA

Agency for Defense Development, Changwon, 51678. R. O. Korea

ABSTRACTAn acoustic experiment was conducted off Geoje Island in southeast Republic of Korea in May 2017.
The purpose it to study uncertainty of mid-frequency sound propagation and reverberation. The environmentis in a transitional area bridging a bay setting to the open sea and the oceanographic condition is dominatedby strong currents and fronts. Transmission loss (TL) and reverberation level (RL) data were simultaneously
collected at 3.5 kHz along two perpendicular tracks each 10-km long. Identical measurements wereconducted twice 5 days apart to examine signal repeatability. Sound speed data were taken by CTD casts anda moored thermistor chain. During the test period, sound speed profiles were close to iso-velocity inside thebay, but they were more variable out in the open ocean due possibly to a migrating coastal front. It is foundthat TL changed mor than 10 dB between the two days, whereas the RL stayed more constant. Modeling andanalysis on TL satisfactorily explain only part of the TL datasets, highlighting the need to capture and model
the changing ocean environment in order to improve mid-frequency SONAR predictions.
Keywords: Transmission loss, Reverberation, oceanography

1. INTRODUCTION
Mid-frequency (1-10 kHz) sound propagation in shallow water often shows strong variability.
Understanding the mechanisms responsible for such variability is a long-term goal. To that end,
environmental measurements at the right scales should go hand-in-hand with acoustics measurements inorder to learn the driving physical mechanisms through model/data comparisons. This was the goal of
KOREX-17 (KOrea Reverberation EXperiment-2017), an experiment conducted off the island of Geoje,
Republic of Korea, 23-31 May 2017. It was a joint effort with participation of three groups: Agency for
Defense Development (ADD), The Applied Physics Laboratory, University of Washington (APL-UW), andHanyang University (HYU). The experiment was performed onboard the ADD research vessel, the R/V
Mirae. One of the objectives of this experiment was to measure both shallow water transmission loss (TL)
and reverberation level (RL) at the same time under different environmental conditions to understand thesources of variability in both measurements.

2. Oceanic and acoustic measurements
Acoustic propagation and reverberation measurements as well as supporting oceanographic
measurements are described below. In addition, bottom data for the experimental site were madeavailable by the Korea Institute of Geoscience and Mineral (KIGAM). Meteorological data collectedby the Korea Meteorological Agency (KMA) were also available for the test period.
1

tbshim@apl.washington.edu
suson@add.re.kr
3
ynna@add.re.kr
4
hefner@apl.washington.edu
5dtang@apl.washington.edu
22.1

Oceanic measurement
The test site is in a small bay off Geoje Island southeast of the Republic of Korea as shown infigure-1 (a). Water depth in the test site varies from 25 m inside the bay and drops to about 65m
outside the bay as shown in figure-1 (b).
(a)

(b)

(c)

(d)

Mud

gmS
(g)mS
Figure-1. (a) Location of test site. The red square in the inset map gives the positions of the site.
(b) Water depth with color bar in meters. (c) Location of CTD castings (blue triangles), thermistor
chain mooring (red square) and KMA data buoy (green circle). (d) Sediment distribution map.
Multiple CTD casts were made inside the bay near the stationary acoustic source, which is
described later. They were also made at two other locations outside the bay, one 10 km due east, theother 10 km due south. They are at the end of the two TL measurement tracks given later. From 23 to
26 May, a thermistor chain, which consists of six Seabird SBE-39s at depths 3m, 10m,20m, 30m, 40m,
and 50m, respectively, was moored at the entrance of the bay as marked by the red square in figure1(c). The thermistor-chain data are unavailable for a period when a strong current pushed the chain to
the seafloor.
Weather data were available through the ocean data buoy operated by KMA, which is locatedfurther NE from the test site marked by the green circle in figure-1 (c). Sediment distribution at thevicinity of the test site is in figure-1 (d): Gravely muddy sand is found inside the bay and near the bay
entrance, and muddy sand is found outside the bay and offshore.
Figure-2 shows sound speed profiles (SSP’s) collected during the experiment period. Daily SSPs
show very similar profiles except for those taken on 25 May where a surface mixed layer is present,
and the layer structure is variable.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure-2. Sound speed profiles by CTD casts during the experiment period. (a) 23, (b) 24, (c) 25, (d)26, (e) 27, (f) 29, (g) 30 and (h) 31 May 2017.
Figure-3 shows the thermistor-chains time series of temperature vs. depth over time. Several
observations are here noted: First, the temperature structure in the water column is close to iso-thermal
except for those for the 25 May and the 26 May when the water column has a warm layer with variable
thickness near the surface. Second, there is a mild warmer water intrusion with a six-hour period,
which suggests this site is strongly influenced by the tidal currents in the region. Third, several data
gaps of varying duration occur, some of them longer than 4 hours. These are found to be due to verystrong currents which overcame mooring buoyancy, resulting in loss of usable data.
During the observation period, warmer surface water intrusions happened several times which
had temperatures greater 15 degrees C. The depth of the intrusions varied from 15 m to 35 m. The
arrows in the figure indicate times when the first set of TL experiments was conducted.

Figure-3. Moored thermistor-chain data from 23 to 27 May 2017. The two arrows indicate time of
TL measurements on the 25 May. Data gaps indicate missing data due to period of strong tidal force
that pushed the chain to the seafloor.

Other relevant oceanic data available for the region are from the ocean data buoy operated by
KMA (see figure-1 (c)). They include wind speed and direction, sea surface temperature (SST) andwave direction and significant wave height, as shown in figure- 4. The sampling interval of the buoy
data is one hour.
Wind direction was mostly westward and wind speed was 0 ~ 5 m/s during the entire
experiment period as shown in figure-4 (a) and (b). SST varies from 16.5 to 18 °C and shows
diurnal variation (figure-4 (c)). Overall, the SST in the first three days was constant at 18 °C andgradually lowered to 16.5 °C hence after. Figure-4 (d) shows wave direction and wave height, andfigure-4 (e) shows significant wave height. Wave height was less than 1 m during the experimentperiod except for the period 26 to 27 May when the wave height is 1-1.5 m. This calm sea surface
condition from the buoy is consistent with observations by sight at the experiment location.

Figure-4. Wind, sea surface temperature (SST), and wave from 00:00, 23 to 24:00, 31 May 2017
observed by the ocean data buoy operated by the KMA.

2.2

Acoustic measurements
Figure-5 (a) shows the TL tracks, marked by the red lines. Monostatic reverberation wasmeasured using the ARMS (Autonomous Reverberation Measurement System) along the tow tracks.
As shown in figure-5(b), it is a bottom lander consisting of a directional source transmitting 3.5 kHz
sound with 100-Hz bandwidth. The beamwidth at 3.5 kHz is ~20 degrees. The source level is 200 dB
and its source elements are at 2-m above the seafloor. While the ARMS can be programed to scan 270degree azimuth angles, it was pointed at fixed directions for the data discussed in this paper. The
ARMS has 4 receive channels with a horizontal aperture of 1m. The ARMS source-receiver combined
beamwidth is 11 degrees for backscatter at 3.5 kHz.
The ARMS is deployed inside the bay, shown as a black dot in figure-5 (a). TL is measured
between 1 and 10 km using ARMS as source and a self-recording hydrophone (SRH) as receiver towed
at 23 m depth, as shown in figure-5 (c). TL experiment was performed along the east-west and northsouth tracks on 25 May and repeated on the 30 May(a) Tow tracks(b) ARMS(c) SRH

Figure 5. (a) Two tow tracks marked by the red lines for TL measurement. (b) ARMS. (c) Self
Recording Hydrophone (SRH) under tow body.
Figure-6 shows bottom profiles of the tow-tracks for both the E-W and N-S directions, whichwere collected by the fathometer on the R/V Mirae. It also shows SSPs measured at the ends of eachof the two tow tracks just before and after each tow. The SSPs near the source location at both tracksof 25 and 30 May are close to iso-velocity and the SSPs at the offshore ends show greater variability.
(a)

25

30

25

30

(b)

25

30

25

30

Figure 6. Bottom topography along the E-W (a) and N-S tow tracks (b) and SSPs at the ends ofthe tow tracks on 25 and 30 May 2017.

3. Transmission loss Analysis
3.1

Measured TL and RL

Figure 7. Sound intensity (minus TL) measured (red) and modeled (blue). Left column: N-S tows,
right column: E-W tows. Top row: 25 May, bottom, 30 May.

When the ARMS transmits toward the south, the TL is measured in the N-S direction; when the
ARMS transmits toward the east, the TL is measured in the E-W direction. Figure 7 is a summary plotof the four TL measurements: two on 25 May, of which one for the N-S tow, and the other for the EW tow. The same measurements were repeated on 30 May. Of the four TL curves, the one taken on
the 25 May along the E-W tow (upper right of figure-7) has a lower loss, whereas the other three aresimilar in level with greater than 10 dB higher loss. Interestingly, the corresponding reverberation
measurements shown in Figure-8 show much less difference among the four data curves.

Figure 8. RL measured. Left: ARMS pointing to the south direction, right: ARMS to the east
direction.

3.2

TL modeling
The Parabolic Equation (PE) method is used to model TL at 3500 Hz. Measured water depth(Figure-6) is used for the strong down-slope environment. The bottom parameters are not measured.
In modeling, a half-space fluid half-space is assumed with the following parameters: the sound speed
is 1630 m/s, sediment density-to-water density ratio is 1.8 and the attenuation coefficient is 0.5 dB
per wavelength. A range of other sediment parameters are tried, including range-dependency, but it
does not alter TL significantly.
The water column sound speed profile is based on the CTD and thermistor data. Rangeindependent modeling was first tried using each and all available SSPs, then range-dependent
modeling was conducted where linear interpolation of SSPs in range is employed. The simulation
results from using these different SSPs differ only slightly. The model curves in figure-7 are ones
which are based on the range-interpolated SSPs.
TL measured on the 25 May along the E-W tow track agrees well with the modeled TL (top rightof Figure-7). However, TL measured along the N-S track is nearly 20 dB greater than the predicted
TL beyond the 5-km range for both days. TL measured on the 30 May along the E-W track also shows
as much as 15 dB difference between model and data.
As noted earlier, a warm surface layer is intermittently measured by the thermistor and CTD
casts. This surface layer is suspected to be caused by a strong current intrusion. The intermittency
implies that the sound speed field in the test area is time-dependent and space-dependent. The lack ofsuch information may have contributed to the model failure for the N-S tow on the 25 May The lackof thermistor data for the 30 May clearly hampers the modeling for TL for that day.

4. Summary and discussion
The simultaneous measurements of TL and RL on two different days at the same site show twointeresting results: (1) The TL has strong intra-day and inter-day variability as demonstrated in Figure7. (2) The RL has much less variability. One might have surmised that RL should have shown stronger
variability than TL because RL involves two-way TL. The measurements show the opposite. The

reason is not known, although it can be speculated that TL variability is more sensitive to sound speed
variations near the surface, whereas RL is less so because its sound energy only comes from those raypaths which interact with the bottom.
The lack of success in modeling effort highlights the need for understanding ocean variability.
Because the TL measurements were taken along the same tracks, the difference in TL between the twodifferent days indicates that the origin of TL variability is due to time-dependent ocean conditions.
The fact that the measured RL has smaller variability than TL variability suggests thatmeasurements of RL may not be reliable way to assess TL variability.
Future efforts should be focused on combining ocean measurements and ocean modeling withacoustic measurements and modeling.

ACKNOWLEDGEMENTS
Authors would like to thank to crews of R/V Mirae and graduate students in the Prof. J.W. Choi’slab, Hanyang University. This project was supported by the U.S. Office of Naval Research.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0364

Acoustic Characterization of Stratified Estuaries
Andone C. Lavery1
1

Woods Hole Oceanographic Institution, Woods Hole, MA, USA

ABSTRACT
Stratified estuaries present unique sampling challenges due to their intense spatial gradients andtemporal variability. Narrowband acoustic scattering techniques have been used for decades as a tool
for remote imaging of physical processes in energetic coastal environments, on spatial and temporalscales difficult to probe with in situ measurements. However, it has been challenging to infer
quantitative information from the measured backscatter. Emerging broadband techniques result inincreased spatial resolution spectral classification and quantification capabilities. Broadband
backscattering collected in the Connecticut River Estuary over more than a decade, together withrecent measurements from the James River, Mobile Bay, and Columbia River, have illustrated thepotential of broadband acoustics for quantitative remote-sensing of microstructure intensity, as wellas other processes, over relevant spatial and temporal scales. The importance of stratification, shear
instability, and fronts in determining the coherence of acoustics signals for forward looking sonar
imaging applications, as well as the impact of these features on acoustic communications andnavigation, is under active investigation. Finally, novel platforms for deployment of broadband sonars,
including the use of AUVs and towed platforms, have resulted in new advances in acoustic methods
that support high-resolution observations of estuarine processes. [Work supported by ONR].
Keywords: Broadband Backscattering, Estuaries

1. INTRODUCTION
Estuarine environments, where fresh water discharge meets the denser ambient ocean water, areoften characterized by strong spatial gradients in water velocity, density and sound speed, and intense
mixing. Understanding estuarine mixing, circulation, and exchange flow remain important questions
in estuarine oceanography [1].
High-frequency acoustic backscattering techniques, spanning 10s to 100s of kHz provide a uniqueand powerful tool to rapidly and remotely investigate the physical properties of the ocean interior overa large range of important spatial and temporal scales. These techniques have been commonly used toimage physical processes that occur in the ocean interior, including, for example, internal waves [24], hydraulic jumps [5], bubbles [6-8], double-diffusion [9,10], thermohaline interfaces [11], oceanic
pycnoclines [12], Langmuir circulation [13], suspended sediments [14,15], and microstructure(4,16,17). From an acoustics perspective, microstructure refers to fluctuations in temperature andsalinity, resulting in fluctuations in sound speed and density, which in turn scatter sound, and occur atscales from sub-millimeter to tens of centimeters. Over the last decade or so, with the emergency ofbroadband acoustic scattering techniques [16, 18], significant advancements have been made in theability to resolve small spatial scales relevant to estuarine mixing, fronts, and shear instability [17,19, 20], and in spectral classification of different estuarine processes [8, 17].

2. METHODS
2.1 Estuarine Acoustics and the Under Sea Remote Sensing Program
High-frequency, broadband acoustic scattering techniques have been used by the author since 2008
to study the structure, dynamics, and mixing in the Connecticut River Estuary, USA [17, 19]. A current
ONR-funded program, the Under Sea Remote Sensing (USRS) program, is aimed at using acoustic
1

alavery@whoi.edu

remote sensing techniques to extend these studies to a variety of different estuarine environments,
such as the James River Estuary, Mobile Bay, and the Columbia River Estuary, which span a wide
range of the conditions that may be encountered in estuarine environments. Specific focus areas
include the three-dimensional structure of surface fronts. Surface fronts are common features in many
coastal environments where buoyant fresh water discharge meets ambient ocean waters. Convergenceat these fronts entrains bubbles where strong downwelling and shear can further entrain and transport
these bubbles. These bubbles represent strong acoustic targets with unique spectral fingerprints. Manyof these processes, particularly the strong vertical and horizontal sound speed gradients, have asignificant impact on the propagation and coherence of sound across a range of frequencies, withramifications for sonar performance and acoustic communications. A secondary goal of the USRS
program is to enhance the performance of AUVs, and other platforms, in challenging estuarine
environments [21], and to continue the development of acoustic techniques for quantification of threedimensional flow features, for example, by developing high-frequency multibeam sonars for estuarinestudies.

Figure 1. Broadband acoustic scattering from an plume front in the Connecticut River in 2017
obtained from upwards looking sonar on a REMUS 100 AUV.
2.2 Acoustic Instruments
A Kongsberg Marine Simrad EK80 WBT-Tube scientific echosounder was used with the ES707CD (50-90 kHz, 7 o full beam width) and ES120-7CD (90-160 kHz, 7 o full beam width) broadband
transducers to perform the ship-board acoustic measurements. The transducers were pole mounted
over the side of the MV Dawn Treader, together with a 1.2 MHz RDI Workhorse ADCP, and a 500
kHz M3 Kongsberg multibeam. Typical operating speeds ranged from 3-6 knots. Also deployed from
the MV Dawn Treader was a towed CTD array comprised of 8 RBR Concerto CTDs withapproximately .5m depth spacing and a v-wing towed underwater depressor (Boston Engineeringmodel 460) to keep the towed array vertical. A REMUS-100 AUV (Hydriod Inc.) was deployedwith an upward looking Kongsberg Marine Simrad EK80 WBT-Mini scientific echosounder withan ES200-7CDK- Split 3(160-2600 kHz, 7o full beam width) and an ES333-7CD-Single (280-420 kHz,
7 o full beam width) broadband transducers.

3. RESULTS
A number of results are published in references [8,17,19,20, 21]. Highlights of the measurementsof acoustic scattering from bubbles entrained in estuarine plume fronts are shown in Figure 1 andhighlights of high-frequency scattering from shear instability observed during ebb tides in the CT
River Estuary are shown in Figure 2.

Figure 2. Spectral characterization and high-resolution imaging of shear instability in theConnecticut River Estuary obtained in 2017. On the bottom right is a 500-kHz multi-beam image ofa similar shear instability.

ACKNOWLEDGEMENTS
The author would like to acknowledge the vast contributions to this work from a large team ofpeople, including Rocky Geyer (WHOI, Department of Applied Ocean Physics and Engineering),
Dave Ralston (WHOI, Department of Applied Ocean Physics and Engineering), Robert Petitt (WHOI,
Department of Applied Ocean Physics and Engineering), the WHOI Oceanographic Systems
Laboratory for support with the REMUS-100 vehicles, and the ONR-funded Under Sear Remote
Sensing Team. This work was funded by the US Office of Naval Research Code 32.

REFERENCES
1. Geyer, W.R. and MacCready, P., 2014. The estuarine circulation. Annu. Rev. Fluid Mech, 46(1), pp.175197.
2. Farmer, D. and Armi, L., 1999. The generation and trapping of solitary waves overtopography. Science, 283(5399), pp.188-190.
3. Moum, J.N., Farmer, D.M., Smyth, W.D., Armi, L. and Vagle, S., 2003. Structure and generation of
turbulence at interfaces strained by internal solitary waves propagating shoreward over the continental
shelf. Journal of Physical Oceanography, 33(10), pp.2093-2112.
4. Lavery, A.C., Chu, D. and Moum, J.N., 2010a. Observations of broadband acoustic backscattering from
nonlinear internal waves: assessing the contribution from microstructure. IEEE Journal of Oceanic
Engineering, 35(4), pp.695-709.
5. Farmer, D.M. and Smith, J.D., 1980. Tidal interaction of stratified flow with a sill in Knight Inlet. Deep
Sea Research Part A. Oceanographic Research Papers, 27(3-4), pp.239-254.
6. Medwin, H., 1977. Counting bubbles acoustically: a review. Ultrasonics, 15(1), pp.7-13.
7. Vagle, S. and Farmer, D.M., 1992. The measurement of bubble-size distributions by acousticalbackscatter. Journal of Atmospheric and Oceanic Technology, 9(5), pp.630-644.
8. Bassett, C. and Lavery, A., 2021, November. Observations of high-frequency acoustic attenuation due tobubble entrainment at estuarine fronts. In Proceedings of Meetings on Acoustics 181ASA (Vol. 45, No.
1, p. 005001). Acoustical Society of America.

9. Lavery, A.C. and Ross, T., 2007. Acoustic scattering from double-diffusive microstructure. The Journalof the Acoustical Society of America, 122(3), pp.1449-1462.
10. Duda, T.F., Lavery, A.C. and Sellers, C.J., 2016. Evaluation of an acoustic remote sensing method forfrontal-zone studies using double-diffusive instability microstructure data and density interface data from
intrusions. Methods in Oceanography, 17, pp.264-281.
11. Stranne, C., Mayer, L., Weber, T.C., Ruddick, B.R., Jakobsson, M., Jerram, K., Weidner, E., Nilsson, J.
and Gårdfeldt, K., 2017. Acoustic mapping of thermohaline staircases in the Arctic Ocean. Scientific
Reports, 7(1), pp.1-9.
12. Ross, T. and Lavery, A.C., 2012. Acoustic scattering from density and sound speed gradients: Modelingof oceanic pycnoclines. The Journal of the Acoustical Society of America, 131(1), pp.EL54-EL60.
13. Thorpe, S.A., 1992. The breakup of Langmuir circulation and the instability of an array ofvortices. Journal of physical oceanography, 22(4), pp.350-360.
14. Crawford, A.M. and Hay, A.E., 1993. Determining suspended sand size and concentration frommultifrequency acoustic backscatter. The Journal of the Acoustical Society of America, 94(6), pp.33123324.
15. Thorne, P.D., Vincent, C.E., Hardcastle, P.J., Rehman, S. and Pearson, N., 1991. Measuring suspended
sediment concentrations using acoustic backscatter devices. Marine Geology, 98(1), pp.7-16.
16. Lavery, A.C., Chu, D. and Moum, J.N., 2010b. Measurements of acoustic scattering from zooplankton
and oceanic microstructure using a broadband echosounder. ICES Journal of Marine Science, 67(2),pp.379-394.
17. Lavery, A.C., Geyer, W.R. and Scully, M.E., 2013. Broadband acoustic quantification of stratified
turbulence. The Journal of the Acoustical Society of America, 134(1), pp.40-54.
18. Stanton, T.K., Chu, D., Jech, J.M. and Irish, J.D., 2010. New broadband methods for resonanceclassification and high-resolution imagery of fish with swimbladders using a modified commercial
broadband echosounder. ICES Journal of Marine Science, 67(2), pp.365-378.
19. Geyer, W.R., Lavery, A.C., Scully, M.E. and Trowbridge, J.H., 2010. Mixing by shear instability at highReynolds number. Geophysical Research Letters, 37(22).
20. Geyer, W.R., Traykovski, P. and Lavery, A., 2013, June. The impact of acoustic oceanographic methods
on estuarine dynamics research. In Proceedings of Meetings on Acoustics ICA2013 (Vol. 19, No. 1, p.
005001). Acoustical Society of America.
21. McNeil, C.L., Shcherbina, A., Isakson, M.J., Kidwell, A.N., Reeder, D.B., Lavery, A.C. and Geyer, W.R.,
2018. Progress on the use of autonomous underwater vehicles to study estuaries. The Journal of the
Acoustical Society of America, 143(3), pp.1728-1728.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0368

Subsurface ducts and their impact on sound propagation off the
Washington coast
DJ TANG; Guangyu XU; Brian T. HEFNER; Eric I. THORSOS
Applied Physics Laboratory, University of Washington, United States

ABSTRACT
Data from an underwater glider taken off the U.S. West Coast near Washington State often show theexistence of a subsurface duct, which may impact sound propagation. The depth of the minimum soundspeed in the duct can vary from 30 m to 130 m. The vertical extent of the duct can vary from 10 m to 60 m.
Available oceanographic models do not routinely predict the presence of these ducts, but efforts areunderway to enable that capability. This paper examines the impact of such ducts on 3.5 kHz soundpropagation. Using measured and modeled sound speed fields as input, the Parabolic Equation method is
employed to evaluate transmission loss for different scenarios. It is found that subsurface ducts could havea strong impact on passive and active sonar. Improved ocean modeling can greatly improve sonar
performance in the presence of subsurface ducts.
Keywords: Duct, Subsurface duct, Transmission Loss

1. INTRODUCTION
Subsurface ducts, or secondary ducts, have been observed by moorings and gliders off the Washington
coast of the United States. They consist of a layer of ocean water that has slower sound speed, hence
enabling long-range propagation of sound waves above a certain frequency in this layer. Such ducts havealso been observed elsewhere, notably in the northeast Pacific off the Canadian coast [1], the Beaufort Sea[2], and off New Jersey coast of the United States [3,4]. The presence of such subsurface ducts and theirtemporal evolution need to be considered in underwater acoustics applications. For this purpose, reliable
oceanographic models which can provide forecast or hindcast of subsurface ducts are an effective tool.
However, the detailed mechanisms which generate and maintain the ducts are not well understood, making
ocean modeling of subsurface ducts an on-going research topic.
Experimental oceanographic and acoustic research focused on the subsurface ducts off the Washington
coast is currently underway. This paper summarizes some of the results from acoustic simulations
conducted in preparation for field efforts. The acoustic simulations are performed using the Parabolic
Equation method at 3500 Hz. The measured sound speed field along an east-west transect by an underwater
glider is used to represent the ocean environment for the simulations. Sound speed fields for the sametransect and same time from two ocean models are used for comparisons. The purpose for this study is
twofold: to investigate effects of subsurface ducts on mid-frequency sound propagation, and to assess thestatus of current ocean modeling capabilities on subsurface ducts.

2. SOUND SPEED FIELD DATA
2.1

Glider Data

The underwater glider, henceforth called the glider, in this paper refers to a coastal glider operated by the
Ocean Observatories Initiative (OOI) [5]. The glider data employed in this analysis was taken 6–16 October
2018 along a 250-km transect off the Washington coast. The left panel of Figure 1 shows the glider transect,which spans from the west on the continental slope in deep water to the east on the continental shelf inshallow water. While the left panel of Figure 2 shows the sound speed field along the entire transect, the rightpanel of Figure 1 gives the top 200-m portion of the sound speed profile taken at the 60 km from the westernend of the transect. A subsurface duct is found throughout most of this transect with the minimum soundspeed of the duct changing between 40–80 m depth. The thickness of the duct is between 30 m and 60 m.
The duct has apparent range-dependence and complex depth-dependence. For example, the profile at the 60
km range on the right panel of Figure 1 has a double duct that has two minima, one near 70 m, the other near
140 m. The duct gradually tapers off toward shore. Above the duct, the surface mixed layer which reaches
approximately 20-m depth has sound speed more than 15 m/s higher than the minimum within the duct.
Immediately below the duct, the sound speed reaches a local maximum which is 2–3 m/s faster than the duct
minimum. The sound speed at deeper depths is typical for October and is dictated by decreasing temperatureand increasing pressure.

Figure 1 – Glider transect from 03:15, 06 Oct 2018 to 17:32, 16 Oct 2018 (left). Sound speed profiles(right) from the glider and two corresponding models at single point (corresponding to 60 km range in
Figure 2).

Figure 2 – Sound speed field measured by glider (left), GLORYS model (middle) and HYCOM model(right). The red curve is the seafloor. Colorbar is in m/s.
It took the glider approximately 10 days to finish collecting data along this transect, therefore, the soundspeed field displayed in left panel of Figure 2 is not a true ‘snapshot’ of the actual sound speed field, butrather an aliased version of it. The acoustic simulations reported in this paper are based on the following
assumptions: (i) The sound speed field from the glider data is treated as a snapshot and any range-dependencyof the field due to the finite speed of the glider is neglected. (ii) The glider data do not cover depths very
close to the sea surface; the missing near-surface data is filled-in with a constant value that equals theshallowest glider data point. In other words, the top layer of water is assumed fully mixed. (iii) The glider
data do not cover depths close to the seafloor; the missing near-bottom data is filled in by extrapolation using
the deepest portions of the glider data.
2.2 GLORYS12V1 Model Data
This model data product will be referred hereafter to as GLORYS. Details are given in [6], and the data
is downloaded from Copernicus Marine Services [7]. The sound speed field determined from daily averaged
temperature and salinity data in the GLORYS product along the same glider transect is employed for acoustic
simulations. This reanalysis product has 1/12° horizontal resolution and 50 vertical layers. To match theglider data in the time domain, the model sound-speed field is constructed from the reanalysis output for thesame day when the glider data was collected at a given location along the transect. Compared to the glider
data, the GLORYS sound speed field (middle panel of Figure 2 and right panel of Figure 1) also shows thepresence of a subsurface duct at similar depth, although the duct strength is weaker: the layer beneath theduct has sound speed 1.5 m/s greater than the duct minimum, less than that of the glider data. The GLORYS
duct is also spatially smoother, which is largely the result of the model’s relatively coarse horizontal
resolution. The basic features of the surface mixed layer and sound speed field beneath the duct are similar
to those seen in the glider data, but not as persistent in range as in the glider data. It should be noted that
GLORYS is a reanalysis product with assimilation of observational data.
2.3 HYCOM Model Data
There are several versions of HYCOM (Hybrid Coordinate Ocean Model). The model data employed inthis paper is from the NOAA website given in [8]. The HYCOM model data is available every three hours
with 40 vertical layers and with a horizontal grid spacing of 1/12°. The HYCOM sound speed field is from asingle time slice at 12:00 UTC on 6 October 2018. While this sound speed field (right panel of Figure 2 andright panel of Figure 1) shows similar near-surface and deep-water profiles as those in the glider data, thesubsurface duct is hardly present.
2.4 Acoustic Simulations
Sound transmission simulations are conducted using the three sound fields as input. All acoustic results
reported are at 3500 Hz and are simulated using the PE (Parabolic Equation) method. The geoacoustic
seafloor properties along the transect are not known. For this simulation effort, the sediment is assumed to
be a homogeneous, fine sand half-space with sound speed 1630 m/s, sediment-to-water density ratio of 1.8,
and attenuation coefficient of 0.5 dB per wavelength. For each of the remaining figures, the color bar gives
the sound intensity in dB, except in the upper left in Figures 6 and 7 where it gives the sound speed in m/s.
The following two scenarios are investigated: (i) sound source depth-dependence where the source isplaced above the duct, in the duct, and below the duct, respectively. (ii) Duct range-dependence where thesound source is at a fixed depth inside the duct but at different horizontal positions along the glider transect.
In (i), results based on the glider data and the two model ocean data are compared. In (ii), results based on
glider data are compared only to those based on the GLORYS model product.
Figures 3–5 compare the simulation results for scenario (i) where the sound source is placed at 60 km
from the western end of the glider transect (see Figure 2), and its 4 depths are at 50 m which is above theduct, 100 m which is close to the duct axis, 140 m which is close to the minimum of the lower double duct,and 150 m which is below the duct. The reason for choosing 60 km as the starting range for the simulations
is that this is the only area where the GLORYS model gives apparent subsurface duct, and HYCOM showsa hint of it. The results based on the glider data (Fig. 3) show the following features: (1) When the source isabove or below the duct, the duct does not have an obvious effect on sound propagation, whereas when thesource is in the duct, guided propagation results. (2) How tightly the duct confines the sound energy in depth
strongly depends on source location relative to the duct axis. (3) The complex structure of the sound speed
profile at the source depth causes the sound to be trapped at different depths of the water column, as evidenced
in the cases for source depth at 100 m and 140 m in Figure 3. This is apparently the result of the double duct
discussed earlier.
Results based on the GLORYS model (Fig. 4) show similar source depth effects as those from the glider
data, but the trapped sound energy is weaker. The reason being that the GLORYS modeled duct is weaker
than that from the glider as seen in Figure 2 as discussed before.
With the subsurface duct being largely missing, results using HYCOM data (Fig. 5) show only someintermittent trapping of sound near the depth of 100 m.

Figure 3 – Sound pressure squared for sound source at different depths using glider data. The highsound intensity near the 100-km range is due to bottom reflection.

Figure 4 – Sound pressure squared for sound source at different depths using GLORYS data.

Figure 5 – Sound pressure squared for sound source at different depths using HYCOM data.
Figures 6 and 7 compare the simulation results for scenario (ii) where the sound source is at 150-m depth
but is placed at different positions along the glider transect, starting at the 10 km range. As seen in the top
left panel of Figure 6, the subsurface duct in this portion has complex range-dependence and the lower
boundary of the duct extends beyond the 160 m depth. The interesting feature from the glider data (Fig. 6) issound refraction, or mode coupling: guided sound energy spreads vertically between 70 m and 150 m at
ranges shorter than 50 km, but behaves differently beyond the 50-km range, due to the range-dependence ofthe duct. The simulations for the same case based on the GLORYS data (Fig. 7) do not show sound trappedby the subsurface duct because the duct depth is shallower at the chosen range.

Figure 6 – Glider sound speed field (upper left) and sound pressure squared for source at fixed depth, but atdifferent positions along the glider transect. High intensity near the 50-km an 90-km ranges are due tobottom reflections.

Figure 7 – GLORYS sound speed field (upper left) and sound pressure squared for source at fixed depth,
but at different positions along the glider transect.

2.5 Discussion
Simulations of acoustic propagation based on the glider sound speed field show that 3.5 kHz sound iswell trapped in the subsurface duct if the sound source is in it. Once trapped, the sound tends to remain in theduct, even when the duct is range dependent. For a moving source, the trapped sound can be at different
depth, depending on local sound speed profile at the source locations.
The GLORYS model shows the presence of the subsurface duct, but the duct is weaker and with lesshorizontal persistence. The modeled depth of the duct can be different from that of the glider data. Acoustic
simulations based on these models partly capture the guided propagation seen in the glider data. Because
they contain little range-dependence, the phenomena of mode coupling are not captured. The HYCOM model
however has just some very weak indications of a duct.
The simulation results show that the GLORYS ocean model partly captures the characteristics of thesubsurface duct, indicating the viability of employing ocean models to assist underwater acoustic applications.
The HYCOM model largely missed the subsurface duct. Future model developments should focus on several
areas: (1) Investigate the generation conditions of subsurface ducts. (2) With limited spatial and temporalmodel resolution, investigate how to characterize the variability of subsurface ducts in space and time. (3)Investigate the consequences of data assimilation to model forecasts of subsurface ducts.

ACKNOWLEDGEMENTS
Work supported by the U.S. Office of Naval Research.

REFERENCES
1. Dosso SE, Chapman NR, ‘Acoustic propagation in a shallow sound channel in the Northeast Pacific
Ocean,’ J. Acoust. Soc Am. 75, 413–418 (1984).

2. Kucukosmanoglu M, Colosi JA, Worcester PF, Dzieciuch MA, Torres DJ, ‘Observations of sound-speed
fluctuations in the Beaufort Sea from summer 2016 to summer 2017,’ Acoust. Soc Am. 149, 1536–1548(2021).
3. Lermusiaux PFJ, Haley Jr. PJ, Mirabito C, Ali WH, Bhabra M, Abbot P, Chiu C-S, Emerson C, ‘Multiresolution Ocean Physics-Acoustics Modeling: Validation in the New Jersey Continental Shelf,’
OCEANS '20 IEEE/MTS, 5–30 October 2020.
4. Fredricks A, Colosi JA, Lynch JF, Gawarkiewicz G, Chiu C-S, Abbot P, ‘Analysis of multipath
scintillations from long range acoustic transmissions on the New England continental slope and shelf,’ J.
Acoust. Soc Am. 117, 1038–1057 (2005).
5. Trowbridge J, Weller R, Kelley D, Dever E, Plueddemann A, Barth JA, Kawka O, 'The Ocean
Observatories Initiative,' Front. Mar. Sci., 04, March (2019).
6. Fernandez E, Lellouche, JM, 2021. Product User Manual: For the Global Ocean Physical Reanalysis
product GLOBALREANALYSIS_PHY_001_030.
7. https://cmems-resources.cls.fr/documents/PUM/CMEMS-GLO-PUM-001-030.pdf.
The CMEMS global ocean eddy-resolving reanalysis (GLORYS12V1) is downloaded from Copernicus
Marine Services (https://resources.marine.copernicus.eu/product-detail/GLOBAL_MULTIYEAR_PHY_001_030/INFORMATION).
8. HYCOM data is downloaded from the following NOAA site:
https://www.ncei.noaa.gov/products/weather-climate-models/frnmoc-navy-global-hybrid-ocean.

ABS-0589

Numerical simulations of wavefields generated by UXO counter-mining:
study of the transition from shallow to deep waterPaul Cristini(1) and Nathalie Favretto-Cristini(1)(1) Aix-Marseille Univ, CNRS, Centrale Marseille, LMA, Marseille, France

ABSTRACT

Explosive devices from World War II are discovered almost every week on the French coasts and they must
be destroyed by the French Navy Mine Warfare Office. The consequences of the counter-mining on the marine environment are complex to evaluate. In a previous work, we focus on the impact of the seismoacoustic
waves generated by such explosions on the infrastructures located on the coast and illustrate the importanceof interface waves in this context. In this presentation, we address a different perspective of this problem
and analyze the wavefields generated by the counter-mining of unexploded ordnances towards deep waters.
For this purpose, we performed several numerical simulations using 2D time-domain spectral-element software which allows for accurate handling of the characteristics of the complex marine environment (complex
bathymetry and elastic sediments). We will focus on the role of the steep transition from shallow to deep
water which takes place in the area where the counter-mining is triggered and on the influence of a thinsedimentary layer on the generated wavefields.

Keywords: Counter-mining, UXO, Wave propagation, Interface wave, Numerical modeling

1

INTRODUCTION

Historical unexploded ordnances (UXO) from World War II are discovered almost weekly near the coasts
of northwest and southeast France. Soon after their discovery, they must be destroyed to ensure the safetyof divers and ships. The preferred method of destruction is counter-mining, i.e. the use of a higher-orderdetonation carried out by the explosion of an additional donor charge placed near the UXO. Depending onwhether the UXO can be safely moved, this counter-mining takes place at specific safe locations or at thediscovery site. In a previous work [1, 2], we evaluated the risk of damage to buildings on the adjacentshoreline induced by the detonation of highly charged UXO in a variable shallow water environment. The
importance of environmental characteristics was emphasized and in particular the need to consider the elasticity of marine sediments to account for interface waves. In this extended abstract, as a complement tothat work, we study the structure of acoustic wavefields generated towards the deep water to investigate how
acoustic energy is distributed in the water column in that direction.
In this extended abstract, we first build a 2D model corresponding to the transition from shallow to deep
water in the same area where we previously performed experiments. Then, this model is used to perform
numerical simulations in the time domain of acoustic and seismic wave propagation. Finally, the results ofthese numerical simulations are analyzed and some conclusions are drawn.

2

CONFIGURATION

We first identified a bathymetric profile corresponding to a transition path to deep water in the rade d’Hyères(Mediterranean Sea), where the experiments reported in previous works [1, 2] took place. This path is shownin Figure 1. Note that there is a steep transition from shallow to deep water. At the beginning of the path,the depth of the water column is almost constant and equal to 46m. Towards the middle of the path, thedepth is 240m to finally ends at 800m. After designing the computational domain, a mesh of it is createdusing quadrilateral elements with the Gmsh software [3].
Numerical simulations are then performed using the open-source software SPECFEM2D1 which implements a spectral-element method. It is based upon a high-order piecewise polynomial approximation of the
1 https://github.com/geodynamics/specfem2d

Figure 1. Bathymetry towards deep waters used in the numerical simulations. The green points represent theposition of the receivers considered in the numerical simulation.
weak formulation of the wave equation and combines the accuracy of the pseudospectral method with theflexibility of the finite-element method. In this method, the wavefield is represented in terms of high-degree
Lagrange interpolants, and integrals are computed based upon Gauss–Lobatto–Legendre quadrature. This
combination leads to a perfectly diagonal mass matrix, which in turn leads to a fully explicit time schemethat lends itself very well to numerical simulations on parallel computers. It is particularly well suited tohandling complex geometries and interface conditions. As a consequence, the accurate simulation of surface
wave propagation is straightforward without any additional cost. This is a very important feature in our configuration since the influence of an interface wave of the Stoneley-Scholte type is known to be important.
The numerical simulations are performed using the cylindrical coordinate system.
Two source positions are considered. The first position is “proud on the bottom”. In this configuration
the charges are placed on the sea bottom. This position corresponds to the majority of the experiments
which were done. The second position is “under barrel”. In this position, the UXOs are gathered in a barrel
immersed at a depth of 11m under the sea surface. This source position is used to avoid the generation ofvibrations at shore but is likely to generate more acoustic energy inside the water column.
We consider Very Low-Frequency acoustic propagation (VLF) by using a source time signal of the Ricker
wavelet type with a central frequency of 25 Hz.
The bottom of the ocean is considered as an elastic medium. The physical characteristics are :• Water: ρ = 1000 kg/m3 , c p = 1477 m/s• Bottom: ρ = 2600 kg/m3 , c p = 4100 m/s, cs = 2700 m/s
where ρ, c p and cs are respectively the density, the velocity of longitunal waves and the velocity of shear
waves.

3

NUMERICAL RESULTS

In this section, we present the numerical results obtained with the configuration presented in section 2. Figure
2 represents snapshots of the acoustic energy distribution in the water column for two source positions,
namely “proud on the bottom” and “under barrel”. It can be seen that, when the water depth increases
strongly, the way the acoustic energy is spreading into the water column is very differentdepending on thesource position suggesting a very different structure of the acoustic wavefield.

(a) Under barrel(b) Proud on the bottom

Figure 2. Snapshots of the acoustic wavefield for two source positions.
3.1 Analysis of simulated time signals
To investigate more in detail the structure of the acoustic wavefield which is generated with the two source
positions, we present in Figure 3 the signals received at several vertical arrays situated along the path towards
deep waters. The vertical arrays have different lengths because they span the entire water column at different
distances from the source for which the water depth is different.

(a) Source proud on the bottom (d=3000m)(b) Source under barrel (d=3000m)(c) Source proud on the bottom (d=12000m)(d) Source under barrel (d=12000m)(e) Source proud on the bottom (d=14000m)(f) Source under barrel (d=14000m)

Figure 3. Signals received on three vertical arrays spanning the entire water column at three different distances
It can be clearly seen a strong first arrival which occurs when the source is “proud on the bottom”. This
first arrival corresponds to the Stoneley-Scholte interface wave propagating at the water/bottom interface. It
is always present even when the water depth increases. When the source is “under barrel”, this arrival isweaker because the source is far from the interface but is still present. In addition, it can be noted that thefirst arrival in the last vertical array (d=14000m), situated in deep waters, shifts in time with depth indicating
an effect of the curvature of the interface due to the steep transition from shallow to deep water which occurs
in this configuration.
3.2 Sound exposure levels
The Sound Exposure Level (SEL) is another physical parameter useful for the evaluation of the impact of
the acoustic wavefield on the marine fauna. This parameter is given by the following relation:
Z T
SEL = 10 log
p2 (t)dt0where T is the length of the time signal.
It can be easily evaluated from time domain numerical simulations. In this subsection, we compare theSEL variations for the two source positions (“proud on the bottom” and “under barrel”).
We first evalute the SEL variations with depth on several vertical arrays situated at different distances tothe source (see Figure 4).

(a) d=3000m(b) d=6000m(c) d=9000m(d) d=12000m(e) d=13000m(f) d=14000m

Figure 4. Variation of the SEL on vertical arrays spanning the entire water column for six different distances
to the source
For all vertical arrays, it can be noted that the SEL values for the “proud on the bottom” source arealways greater than the SEL values for the “under barrel” source. This results is counter intuitive and puts inlight the strong effect produced by the interface wave. A 10 dB gap is present in the vicinity of the interface
between water and sediments. In addition, a variation with depth of the SEL values is observed suggestingthat it cannot be considered as constant in this configuration as usually guessed in the literature.
In a second step, we investigate the evolution of the SEL values with range at a given depth.

(a) Receiver depth=20m(b) Receiver depth=30m(c) Receiver depth=40m

Figure 5. Variation of SEL with source-receiver distance for three different depths
Figure 5 confirms the results of Figure 4 showing that when the receiver gets close to the interface thedifference between the SEL produced by the two sources can be more than 10 dB. This difference is higher
in the shallow water zone but remains non negligible even when reaching deeper waters.

4

CONCLUSION

In this extended abstract, we studied the propagation of acoustic waves generated by the counter-mining of
UXOs in shallow water. Specifically, we investigated the structure of the acoustic wavefield which propagates
towards deep waters. It was found that the interface waves generated during disposal are critical highlighting
the absolute necessity of considering the elasticity of sediments in this type of studies.

ACKNOWLEDGEMENTS
This work was granted access to the HPC/AI resources of CINES under the allocation A0110410305 madeby GENCI and of the Aix-Marseille Supercomputing Mesocenter under allocations h025. The authors would
like to thank Artemis Zegna-Rata for her contribution to this work during her internship.

REFERENCES[1] Favretto-Cristini N. et al. Assessment of risks induced by counter-mining unexploded large-charge historical ordnance in a shallow water environment: Part 1. Real case study. IEEE J Ocean Eng. 2022; 47(2):350-373.
[2] Favretto-Cristini N. et al. Assessment of risks induced by counter-mining unexploded large-charge historical ordnance in a shallow water environment: Part 2. Modeling of seismo-acoustic wave propagation.
IEEE J Ocean Eng. 2022; 47(2): 374-398.
[3] C. Geuzaine and J.-F. Remacle. Gmsh: a three-dimensional finite element mesh generator with built-in
pre- and post-processing facilities. International Journal for Numerical Methods in Engineering 79(11),pp. 1309-1331, 2009.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0658

Temporal variability of mid-frequency sonar in a deep bay
Brian T. HEFNER1; DJ TANG1; David DALL’OSTO1; Grant EASTLAND2
1

Applied Physics Laboratory, University of Washington, Seattle, WA 98105
2

Naval Undersea Warfare Center Division, Keyport

ABSTRACT
Mid-frequency (2-10 kHz) sound propagation and reverberation are sensitive to temporal changes in theocean environment which can produce significant signal variability on timescales ranging from minutes tohours. To study this temporal variability, an experiment was conducted in January 2020 in Dabob Bay using
fixed source and receivers to measure transmission loss, reverberation level, and ambient noise over a 10day period. Over the course of the experiment, several storms passed over the site producing significant
surface waves and an influx of cold, fresh water which formed a surface layer, the structure and thickness ofwhich varied over time. The surface roughness and freshwater layer, as well as a significant tidal exchange,
were the major drivers of mid-frequency acoustic intensity variability observed within the bay. Despite theconfined nature of the bay, the significant variation in the depth (30-185 m) makes the environment similar
in many respects to the ocean shelf. This talk provides an overview of the variability of the acoustic
measurements made over the course of the experiment, relates that variability to environmental factors, anddiscusses how those factors would manifest in the ocean environment.
Keywords: Mid-frequency sonar, transmission loss, reverberation

1. INTRODUCTION
A major challenge in the use of mid-frequency (2-10 kHz) sonar for passive and active target
detection is signal uncertainty. For passive sonar, it is not uncommon to encounter 20 dB or greatertransmission loss (TL) variability in the matter of hours, even minutes (1). For active sonar, signal
excess suffers two-way TL uncertainty as well as varying reverberation. In addition, in comeapplications, active sonar performance relies heavily on TL estimates to make sonar predictions. Thissignal uncertainty comes from lack of knowledge of environment, which includes sea surface
roughness, entrained bubbles, water column variability due to changing oceanographic conditions,and bottom complexity. Unlike low-frequency sonar, where the wavelengths are on the order of meters,
mid-frequency sonar wavelengths are 10s of centimeters and variations in environmental properties
at these length scales will have a larger impact on mid-frequency propagation and reverberation. It
can be difficult to assess the relative importance of different sources of environmental variability atthese length-scales since they are not only difficult to measure over large areas, but multiple sourcesof variability may be impacting propagation at the same time. For sonar performance prediction basedon ocean modeling, even if the impacts of environmental variability are well understood, the relevant
length-scales are smaller than the resolution of most ocean models and it’s unclear whether the model
fidelity could ever reach the point where they could be predicted deterministically.
To systematically investigate such uncertainty issues, it would be desirable to have a testbed where
repeated experiments can be performed during different seasons and under varying wind, tide, ambient
noise, and other oceanographic conditions. There are several open ocean sites that have seen repeatedacoustic experiments such as the Atlantic coast of Florida (2) and the New England shelf (3). The
difficulty with an open ocean site is that they can be challenging to access frequently due to distancefrom shore, comprehensive environmental characterization requires extensive, repeated measurements,
and in some seasons, the weather conditions may not permit access at all. Sheltered bodies of watersuch as lakes, bays, and lagoons allow for easier access and the potential for the long-term installationof measurement equipment, but by their very nature, the environmental conditions in these waters can1hefner@apl.washington.edu

be very different from those encountered in the open ocean. By careful selection of a sheltered site,
however, it may be possible to study the variability of mid-frequency sonar due to variations under asubset of environmental conditions. This not only isolates the mechanisms driving variability anduncertainty but also reduces the extent of the environmental characterization needed to understandthose mechanisms.
As a first step towards the goal of development a mid-frequency sonar testbed, a mid-frequency
propagation and reverberation experiment was conducted in Dabob Bay, WA. Dabob Bay is a 185-mdeep fjord extending 20 km to the north from is entrance at the Hood Canal in Puget Sound. The
orientation of the Hood Canal and Dabob Bay are such that during the January time frame of theexperiment, prevailing winds blow north along the canal and into the bay with sufficient fetch togenerate significant wind-driven waves which propagate up the length of the Bay. The wind waves,along with the injection of fresh, cold water from river and streams into the bay, produces anenvironment where the combined effects of a variable surface acoustic duct and rough sea surface on
mid-frequency propagation and reverberation can be studied. This paper provides an overview of theexperiment, highlights several acoustic observations of TL and reverberation level (RL) variability
during the experiment, and discusses the potential for future experiments within the bay.

2. EXPERIMENT OVERVIEW

Figure 1– Left: Bathymetric map of Dabob Bay showing the locations of ARMS and the VLA. Right: The
Autonomous Reverberation Measurement System (ARMS) on the deck of the R/V Jack Robertson prior to
deployment.
This paper will focus on measurements made using two acoustic systems deployed as shown in
Figure 1 in Dabob Bay during 21-30 January 2022. The Autonomous Reverberation Measurement
System (ARMS), shown in Figure 1, is a bottom lander with a directional source and 4-element receive
array. The source and receive array are mounted on a motorized rotation stage and the system can be
programmed to transmit and collect reverberation data over a 270-degree azimuthal range. The system
can transmit pulses at 3-6 kHz with a maximum source level of 210 dB. For the Dabob Bay experiment,
the ARMS transmitted 1-sec-long LFM pulses with a center frequency of 3.5 kHz, a source level of
200 dB, and bandwidths ranging from 100 Hz to 3000 Hz. The ARMS was deployed in shallow waterat approximately 50 m depth near the northern end of the bay. The ARMS source has a 1.2-m-wide
horizontal aperture and when combined with the horizontal array of 4 elements separated by 0.46 m,
this produces a combined beam pattern that is 11 degrees wide at the 3 dB down and sidelobes at -22
dB. The second system was a moored vertical line array (VLA) with 10 hydrophones distributed over
37 m and which could be moved as a group to different depths in the water column. The array wasmoored at roughly 180 m depth and 5.5 km to the southeast of the ARMS such that the bearing fromthe ARMS to the VLA was roughly parallel to the long axis of the bay. For the measurements discussed
here, the ARMS was typically oriented so that it was transmitting in the direction of the VLA.
Near the location of the ARMS, several oceanographic instruments were deployed to monitor theenvironment over the course of the experiment. These included an upward-looking ADCP deployedin 30 m water on a bottom lander, a thermistor chain consisting of 10 sensors evenly spaced anddeployed in 60 m of water, and a Datawell Waverider buoy which continuously recorded wave spectra
data over most of the experiment. In addition to these stationary instruments, a series of CTD casts
were periodically made at 500 m intervals along the line connecting the ARMS and the VLA. These
measurements provided a rough estimate of horizontal variability of the sound speed as a function of
depth along the propagation path between the source and receive array.

Figure 2 – (a) Omnidirectional wave spectra recorded by the Datawell Waverider buoy over the course of
the experiment. (b) Rose plot showing distribution of RMS wave heights and direction. (c) Sound speed
field measured between the ARMS and the VLA on 27 January 2020.
The decision to conduct the experiment during January was based upon historical meteorological
data which showed that during the winter, there are frequent storms, and the associated wind blows
predominately from the southwest. This causes waves to build up along the Hood Canal and into
Dabob Bay. This was indeed the case during the 2020 experiment as can be seen in the omnidirectional
wave spectrum (Figure 2a), where frequent wave events can be seen, and the distribution of wave
directions (Figure 2b) which appear to be predominantly from the southwest. The wave buoy was
deployed near the ARMS, in shallow water, and we believe that the incoming waves were refracted
shoreward as they approached the buoy. In the deeper waters of the bay, the waves were aligned closer
to the ARMS-VLA line and the acoustic propagation path between the two systems is approximately
parallel to the wind wave direction.
Another consequence of the frequent storms is the influx of cold, fresh water into the bay fromstreams and rivers. This incoming water formed a low sound speed layer near the surface of the watercolumn, as can be seen in the CTD measurements shown in Figure 2c, and the boundary separating
the cold, fresh water and the underlying warm, brackish water, varied over the course of theexperiment between 8-15 m. Below the fresh water layer, the sound speed increases as a function of
depth and below 100 m it is governed by increasing pressure and constant temperature. The soundspeed profile within the bay is also consistent with historical measurements taken during January andFebruary.
While no sediment characterization was performed as part of the 2020 experiment, historicmeasurements within the bay indicate that above 100 m, the sediment is predominantly silty sandwhile below 100 m, the sediment is silt (4). Sub-bottom profiling and passive fathometermeasurements suggest that the deep silt sediments are in a roughly 7 m thick layer above a harder,potentially rock half space (5).

3. VARIABILITY OF TRANSMISSION LOSS AND REVERBERATION LEVEL
During most of the experiment, the ARMS was oriented so that the main lobe of the directional
array was aligned with the VLA. The ARMS was programmed to transmit a 1-sec-long pulse every 20s while the VLA was continuously receiving. An example of the TL measured on a single hydrophone
at 44 m depth for each ping over a 12-hour period on 28 January 2020 is shown in Figure 3a. Both the
ARMS and the hydrophone were located near the same depth and towards the bottom of thethermocline. Consequently, the measured signal is dominated by sound that is trapped in the surface
duct as can be seen in the ray trace in Figure 2b where only rays with launch angles from -4 to 4
degrees are shown. Above and below these launch angles, the rays are no longer trapped, reflect fromthe seafloor, and at the VLA, their arrivals are distributed sparsely throughout the water column. Thisis consistent with the field intensity simulated using the parabolic equation which is shown in Figure
3c.

Figure 3 – (a) TL measured on a hydrophone at 44 m depth (red) and the incoherent average over all 8hydrophones distributed between 19 m and 56 m (black). The pings transmitted by ARMS were 3.5 kHz
center frequency, 100 Hz bandwidth, 1-s-long LFM pulses. The lower plot shows the RMS wave heightmeasured over the (b) Ray trace for the sound speed shown in Figure 1c for rays with launch angles
between ±4 degrees. (c) Intensity field calculated using a parabolic equation solver for 3.5 kHz.
In the TL for the single hydrophone shown in Figure 3a, there are two types of variability present;
large fluctuations where the TL changes by 10-15 dB over time-scales on the order of 30 min andsmaller fluctuations that occur over very short time scales. Comparison of the TL time series with the
RMS wave height measured over the same period indicates that these small fluctuations occur when
the sea surface is rough. A possible explanation for these fluctuations could be that the sea surface
roughness produces small changes in the propagation paths for those rays that interact with the surface.
This in turn produces phases shifts in the eigenrays arriving at the hydrophone which leads to random
intensity fluctuations due to the interference of the rays. We expect no appreciable increase in the TL
as a result, an expectation that is consistent with the data. In the Kirchhoff approximation, the coherent!"

reflection coefficient is 𝑒 ! " , where 𝜒 = 2𝑘ℎ sin 𝜃, k is the wavenumber, h is the RMS roughness,and 𝜃 is the grazing angle with the surface. For the center frequency of 3.5 kHz, the measured RMS
roughness of 0.1 m, and assuming a grazing angle of 4 degrees, the reflection coefficient is 0.98 or aloss of 0.18 dB per surface interaction. Given that the ray trace indicates that sound trapped in thesurface duct has only 2-3 interactions with the surface between ARMS and the VLA, any increase in
TL due to the rough surface is negligible.
The larger fluctuations which occur over larger timescales, with the intensity varying over 15-60
min. These fluctuations aren’t correlated with the presence of the rough surface and are most likely
due to tidal fluctuations and changes in the surface duct. The tidal range was 3 m over the course of
the measurements shown in Figure 3a and preliminary PE simulations have shown that this may becontributing to these larger fluctuations. Of equal or greater importance, however, may be changes inthe surface duct which are due in large part to changes in the influx of fresh water from rivers andstreams feeding into the bay. ADCP and CTD measurements show that the depth of the boundary
separating the fresh and brackish water increased by 2.5 m over the first 4 hours of the measurements
and then fluctuated with increasing and decreasing in 2-hour intervals. These changes in thickness
were also accompanied by small vertical and horizontal differences in sound speed within the duct,again driven by the influx of fresh water.
Unlike the variations induced by the surface roughness, these longer time variations appear tosuggest large scale changes are being induced in the intensity field structure. For example, the ray
trace in Figure 3b indicates that along the length of the surface duct, the rays, and hence the intensity
field, form patterns similar to convergence zones. Changes in the width of the fresh water layer may
expand or contract these patterns, changing the vertical intensity pattern at the range of the VLA.
These large fluctuations in TL can be reduced 5-10 dB by incoherently averaging over theintensity measured across the hydrophones in the array as shown by the black points in Figure 3a.
Given that the sound travelling inside the duct does not interact with the seafloor and experiences
little loss as a result, the net flux of energy through the vertical aperture should not change with small
compressions of and variations within the duct. The remaining fluctuations are likely the result of thesparseness of the array. This mean loss can be estimated by assuming cylindrical spreading loss andtaking into account the angular portion of the total energy from the source that is trapped in the duct:$%〈𝑇𝐿〉 ≈ 10 log"# 𝑅 − 20 log"# 8
9 = 64.4 dB, where R = 5.5 km and Δ𝜙 = 8°, consistent with the"&#°mean level in Figure 3b.

Figure 4 – (a) Polar plot of reverberation measured on ARMS in Dabob Bay. (b) Comparison of the mean
reverberation measured along the ARMS to VLA line over two-hour periods when the RMS sea surface
height was ℎ ≈ 0 m (black) and when ℎ = 0.12 m. (c) Comparison of incoherent mean (blue) andcoherent mean (red) of the reverberation measured during the calm period. (d) Comparison of incoherent
mean (blue) and coherent mean (red) of the reverberation measured during the rough period.

Using the ARMS as a source for the propagation measurements described above, made it possible
to simultaneously measure monostatic reverberation associated with each of the transmitted pings. In
addition, to these fixed bearing transmissions, the ARMS was also programmed to collectreverberation over its full 270-degree measurement range to provide a polar plot of reverberation
shown in Figure 4a. This angular measurement helps to understand the reverberation features in Figure
4b which shows the incoherent average of the reverberation measured when the ARMS transmitting
towards the VLA, indicated by the black circle in Figure 4a. At ranges less than 1.5 km, thereverberation is high and can be associated with scattering from the sediment, likely sand, on the slope
where the ARMS was deployed. Beyond 1.5 km, the reverberation begins to drop off until 4.5 km,
where the outer edge and side lobes of the source/receive array beam pattern is reflected from Pulali
Point, indicated by the black arrow in Figure 4a.
Unlike the TL measurements on the VLA in the upper water column, the reverberation is driven
not with sound trapped in the duct, but instead with sound leaving at steeper grazing angles and which
scatter from the bay floor. As a result, the sound is weakly affected by the tides, layer thickness, orchanges in the sound speed within the duct. This sound does interact with the sea surface, so thevariations in the TL due to the rough sea surface will impact the RL as well. In Figure 4b, the mean
reverberation measured when the bay is calm does not decrease as the roughness increases. This isconsistent with the observation that the roughness does not appreciably increase the TL. The
roughness does have the effect of “smoothing” the reverberation; while the overall RL level ismaintained, the small-scale structures in the curve are missing. See for example the change in response
between 3-4 km and 5-6 km in Figure 4b.
These changes can be partially understood by looking at the coherent vs. incoherent mean
reverberation during the calm and rough periods. Figure 4c shows the incoherent mean and thecoherent mean of the reverberation over a two-hour calm period. In this case, the two RL curves arevery similar and hence there is high ping-to-ping correlation. When the surface roughness comes up,
the variability in the TL observed on the VLA manifests as a blurring or smearing of the seafloor
scattering. While this does not impact the overall shape of the RL curve, it will have an impact on theability of the mid-frequency sonar to resolve a target.
The reverberation scan in Fig. 4(a) shows several strong highlights around the shores of the bay
when the ARMS beam is aimed close to perpendicular to shore. While these highlights are expected,
the quantitative scattering level at those highlight spots are not fully modeled and understood. Doingso will provide understanding of bottom scattering mechanisms in this unique setting where acquiring
measurements of bottom properties is relatively easy. In addition, these stationary strong scatteringfeatures can serve as known targets. Measurements of scattering strength from these targets undervarying wind and seasonal conditions can help shed light on propagation variability.
The analysis so far indicates that for this winter environment, only those rays propagating at
relative steep angles interact with the bottom. Isolating those ray arrivals on the TL can provide clean
data for inverting bottom properties. Similar inversions can also be attempted using RL data which isdue to scattering by high angle rays.
Finally, and perhaps most importantly, such simultaneous TL/RL measurements offer realistic
opportunities to jointly model transmission and reverberation. Historically, few such joint
measurements are conducted. Consequently, TL and RL modeling are often considered separate
problems, and consistency of the models are seldomly checked. Only by doing so with confidence can
one hope to utilize RL data to make estimates of TL for active sonar.

4. Future Experiments
For studying rough surface impacts on TL and RL, Dabob Bay provides an easily accessible,
repeatable testbed. Using the same equipment as deployed in the 2020 experiment, the impacts of therough sea surface on broadband TL and RL can be studied. Also, by moving the source or VLA todifferent locations, the angle between the acoustic propagation path and the wind wave direction. The
case where the acoustic propagation and wind wave directions are perpendicular is of particularinterest since common 2-D modeling approaches, such as rough surface PE, fail to capture the impactsof the wave field in this case. Moving beyond the 2020 experiment, if a longer VLA is deployed,
preferably one that spans the water column, the propagating acoustic field can be differentiated into
different angles, and spatial coherence under different wind and wave conditions can be fully
examined.

Historical measurements indicate that the sound speed profile in the bay evolves over the courseof the year, with a secondary ducts forming in the Spring and Fall. During these periods, the bay would
be a natural laboratory to study the impacts of the duct on transmission loss and target detection using
the scattered return from the shoreline as discussed in the previous section.

5. SUMMARY
This paper highlights several of the measurements and observations from a 2020 experiment in
Dabob Bay where mid-frequency transmission loss and reverberation level were measured
simultaneously. By conducting the experiment in the bay during a period when regular winds
generated waves along the acoustic propagation path, the impact of the waves on TL and RL could be
separated from variability due to tidal and fresh water intrusion. Joint TL and RL modeling remains
to done, but the extensive environmental characterization and the separation of the different sourcesof environmental variability, should simplify the modeling approaches necessary to reconcile the TL
and RL measurements.

ACKNOWLEDGEMENTS
This work was supported by the U.S. Office of Naval Research.

REFERENCES
1. Rouseff, D. and Tang, D. Internal wave effects on the ambient noise notch in the East China Sea:
Model/data comparison. J Acoust Soc Am 120, 1284–1294 (2006).
2. DeFerrari, H. A. and Nghiem‐Phu, L. Scattering function measurements for a 7‐NM propagation
range in the Florida Straits. J Acoust Soc Am 56, 47–52 (1974).
3. Lynch, J. and D. Tang, Overview of Shallow Water 2006 JASA EL Special Issue Papers, J. Acoust.
Soc. Am. 124, EL63 (2008).
4. Wilson, O.B. Jr and Helton, Robert A. Acoustic Properties of Sediments at Weapons Test Ranges of
the Naval Undersea Warfare Engineering Station, Keyport, Washington, Technical Report, Naval
Postgraduate School, Monterey, California (1979).
5. Siderius, M., Song, H., Gerstoft, P., Hodgkiss, W.S., and Hursky, P. Adaptive passive fathometer
processing. J Acoust Soc Am 127, 2193–2200 (2010).

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0052

Sub-Surface Shear Wave Velocity Images by Ocean Ambient Noise
Hefeng Dong
Department of Electronic Systems, Norwegian University of Science and Technology, Norway

ABSTRACT
Sub-surface shear wave velocity images are particular interesting for geotechnical engineering, assessmentof geohazards and reservoir characterization, and production monitoring. This study analyzes one-hour ocean
ambient noise recorded by a Permanent Reservoir Monitoring System for sub-surface characterization. The
recorded noise is very short with strong directional interference resulting in a bias in travel-time measures
between a pair of sensors using seismic interferometry. A pre-processing technique, an adapted eigenvaluebased filter, is applied to the noise recordings to suppress the interference and obtain reliable Green’s
functions. Scholte-wave phase velocity dispersion curves are extracted from the retrieved Green’s functions.
With a limited recording time, detailed images of the shear-wave velocity structure below the recording
system are obtained by a nonlinear inversion. This study demonstrates the effectiveness of the pre-processing
technique and shows the potential of the technique in ambient noise processing and passive seismic
interferometry.
Keywords: Passive seismic interferometry, Dispersion curves, Green’s function, Shear wave velocity images

1. INTRODUCTION
Knowledge of sub-surface shear-waver velocities is of importance for geotechnical engineering,assessment of geohazards and reservoir characterization, and production monitoring [1-2]. Shearwave velocity can be estimated from dispersion curves of seismic interface waves by active seismic
measurements [3-4]. An alternative is to use passive seismic interferometry that uses the ocean
ambient noise cross-correlations recorded by receiver pairs to reconstruct the subsurface impulse
response. An estimate of the Green’s function between the receivers can be obtained. The waves in
the Green’s function usually carry useful information about the environment between the receivers.
As an environmental-friendly passive imaging technique, passive seismic interferometry significantly
enhances the importance of naturally occurring ambient noise. It makes it possible to image subsurface structure and monitor temporal sub-surface changes [5]. This technique requires that the noise
field be diffuse and equipartitioned [6]. However, the noise field in the real world is usuallycontaminated by strong, directional sources, introducing biases into the retrieved Green’s functions.
Wu et al. [7] reviewed the techniques, and they proposed an adapted eigenvalue-based filter (AEF) to
suppress the interference of strong directional sources and improve the quality of the estimate of theGreen’s function. In this paper, the proposed AEF is applied to perform shear-wave velocity images
on a very short recording of ocean ambient noise that was severely contaminated by strong
interference.

2. DATA ANALYSESThe data used in this study contains 1.02 hours of ocean ambient noise recorded by a permanent
reservoir monitoring (PRM) system installed by Equinor at the Grane oil field in the North Sea [7].
The PRM system consists of 17 cables from 1.5 km to 12.85 km. The cable spacing is 300 m, and thesensor spacing is 50 m. The sampling frequency is 500 Hz, and the water depth is 125 m. Each sensor
station consists of 4 components (pressure and three components of particle velocity). Only thepressure component is analyzed in this paper. The noise recordings are severely contaminated bystrong directional events (airgun signals, earthquakes, and other events with higher energy than theˆ(f)background noise), as shown in Figure 1. The AEF is applied to the sample covariance matrix R
to suppress the noise and retrieve reliable noise cross-correlations:Kˆ ( f )   ˆ vˆ vˆ H 
R
k k k
k 1

N'

 ˆ vˆ vˆ

k  K 1

ˆ  R
ˆ  R
ˆ
 R
s
d
i

k

k

H
k



N

 ˆ vˆ vˆ

k  N ' 1

k

k

H
k

(1)

ˆ is related to the strong directional noise, Rˆ to the diffused noise, and R
ˆ to the uncorrelated
where Rsdinoise [8]. The key is to determine the eigenvalues K and N’ values. Figure 2 illustrates the energy
distribution of beamforming of a gather (30 sensors) before (left column) and after (right column)
applying the AEF. It is clearly shown that there are strong directional events in Figure 2(a) and 2(c),while it is more uniformly distributed after applying the AEF (Figure 2(b) and 2(d)). Reliable Green’s
functions of a gather (30 sensors) can be retrieved, and the dispersion curves can be extracted. With
25-sensor overlapping, 537 gathers are obtained, and 537 sets of dispersion curves are extracted forthe entire PRM system.

Figure 1. Noise recording of one sensor (a) andits spectrogram (b).

Figure 2. Beamforming before (a and c) and after(b and d) applying the AEF.

3. RESULTS AND DISCUSSION
Nonlinear inversion, adaptive simplex simulated annealing (ASSA) [9], is applied to the extracted
537 sets of dispersion curves (only fundamental mode is used) to perform 3D shear-wave velocity
structure in the area of the PRM system. It is assumed that the geoacoustic model is a homogenous
layered model. The shear-wave velocity and layer thickness of each layer are estimated parameters,
and P-wave velocity and density in each layer are related to shear-wave velocity by the empiricalformulas [8]. The shear-wave velocity images beneath the PRM system are obtained. The inversion
results indicate a 4-layered sub-bottom structure: the top layer is a thin and soft layer with a thicknessof ~15 m and a shear-wave velocity of ~ 300m/s; the thickness and shear-wave velocity of the second
and the third layers are ~100 m and ~500-600 m/s, and ~260 m and ~800-900 m/s, respectively. Theinterfaces of the top layers are clear, and the shear-wave velocity distribution is relatively
homogeneous over range. The layer thickness of the bottom layer is larger than 500 m, and it is ahigh-velocity layer with a shear-wave velocity spanning 900-1300 m/s. The velocity value can be evenhigher in several areas, indicating a more complicated structure than the top three layers. The
estimated shear wave velocity sub-structure is compared with the active-source reflection tomography[10] in the same field, and they are very similar. However, the active-source reflection method gives
better resolution due to its higher energy level.

4. CONCLUSIONS
Shear-wave velocity tomography of the sub-surface is performed using only 1.02 hours of ocean
ambient noise. The noise is severely contaminated by strong directional events. An adapted
eigenvalue-based filter (AEF) is proposed to suppress the severe interference. The technique shows
good effects both in beamforming and noise cross-correlation results. The nonlinear inversion is used
to perform the sub-surface shear-wave velocity tomography. The inversion results indicate a fourlayer sub-bottom structure with a generally increasing velocity as depth. The distribution of thevelocity values in the first three layers is relatively homogeneous, while the fourth layer shows a more
complicated distribution, indicating a heterogeneous structure under 500 m. The comparison of theimaging result with the active-source reflection method demonstrates that the tomography obtained inthis paper is reasonable and encouraging even though the ambient noise is short and contaminated.

ACKNOWLEDGEMENTS
The author would like to acknowledge Grane license partners (Equinor ASA, Petoro AS,
ExxonMobil E&P Norway AS and ConocoPhillips Skandinavia AS) for allowing to present this work.
The views and opinions expressed here are those of the author and are not necessarily shared by thelicense partners. This work was partially supported by the Norwegian Research Council and theindustry partners of the GAMES consortium at NTNU for financial support (grant no. 294404) andpartially supported by the SFI CGF (Centre for Geophysical Forecasting) under grant 309960.

REFERENCES
1. Smith D. T. Geotechnical characteristics of the seabed related to seismo-acoustics. Ocean SeismoAcoustics. New York: Plenum; 1986. p. 483–500.
2. Stoll R. D, Batista E. New tools for studying seafloor geotechnical and geoacoustic properties. J
Acoust Soc Am. 1994; 96: 2937–2944.
3. Hovem J.M, Dong H, Frivik S.A. Estimation of shear wave velocity in seafloor sediment by
seismo-acoustic interface waves: A case study for geotechnical application. Theoretical andComputational Acoustics. Singapore: World Scientific; 2006. p. 33–43.
4. Dong H, Dosso S.E. Bayesian inversion of interface-wave dispersion for seabed shear-wave speed
profiles. IEEE J Ocean Eng. 2011; 36: 1–11.
5. De Ridder S, Biondi B. Daily reservoir-scale subsurface monitoring using ambient seismic noise.
Geophys Res Lett. 2013; 40: 2969–2974.
6. Weaver R.L. Equipartition and retrieval of Green’s function. Earthq Sci. 2010; 23: 397-402.
7. Wu G, Dong H, Ke G, Song J. An adapted eigenvalue-based filter for ocean ambient noise
processing. Geophysics. 2019; 85: KS29–KS38.
8. Dosso S, Wilmut M, Lapinski A.L. An adaptive-hybrid algorithm for geoacoustic inversion. IEEE
J Ocean Eng. 2001; 26: 324–336.
9. Wu G, Dong H, Ke G, Song J. Shear-Wave Tomography Using Ocean Ambient Noise with
Interference. Remote Sensing. 2020; 12: 2696. doi:10.3390/rs12182969.
10. Hicks E, Hoeber H, Houbiers M, Lescoffit S.P, Ratcliffe A, Vinje V. Time-lapse full-waveform
inversion as a reservoir-monitoring tool - A North Sea case study. Lead. Edge. 2016; 35: 850-858.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0128

Geoacoustic inversion at sea made easy using simple
hand-deployable acoustic systems
Julien BONNEL1; Andrew R. McNEESE2; Preston S. WILSON2, Stan E. DOSSO3
1

Applied Ocean Physics and Engineering Department,

Woods Hole Oceanographic Institution , Woods Hole, MA, USA
2

Department of Mechanical Engineering & Applied Research Laboratories,
University of Texas at Austin, Austin, Texas

3

School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada

ABSTRACT
Ocean acoustic experiments often rely on complex experimental assets. Standard equipment used forgeoacoustic inversion usually includes at least a horizontal or vertical line array of hydrophones and apowerful sound source (e.g., a towed transducer or an explosive/implosive system), as well as a large
research vessel. In this article, we propose a simple and low-cost pair of experimental assets to collectmarine acoustic data for geoacoustic inversion: a “TOSSIT” sensor mooring and a “RIUSS” (Rupture
Induced, Underwater Sound Source). The TOSSIT is a hand-deployable tetherless bottom lander with asingle-hydrophone recorder and an acoustic release, while the RIUSS is an expendable hand-deployable
acoustic source system that produces a high-amplitude impulsive signal. The combination of RIUSS andTOSSIT enables state-of-art single-hydrophone geoacoustic inversion, e.g. using warping and transdimensional methods. To validate the system, a geoacoustic experiment was performed using these assets
from a comparatively small and inexpensive fishing vessel in March 2021 on the New England Mud Patch.
Inversion results compare favorably with previous results obtained using data collected with standard
equipment on the same track during the 2017 Seabed Characterization Experiment.

Keywords: Acoustic instrumentation, Geoacoustic inversion, New England Mud Patch, SBCEX, warping

1. INTRODUCTION
In shallow water and littoral environments, sound propagation is greatly influenced by the seabed
properties. Estimating these properties in situ using ocean acoustics is an active research field, andvarious geoacoustic inversion methods have been developed. Standard equipment used forgeoacoustic inversion usually includes at least a horizontal or vertical line array of hydrophones anda powerful sound source (e.g., a towed transducer or an explosive/implosive system), as well as alarge research vessel. The deployment of such systems is often challenging and expensive, which
effectively reduces the number of arrays that can be deployed for a given experiment, and thenumber of experiments that can be performed over a given time period.
In this article, we propose a simple and low-cost pair of experimental assets to collect marine
data for geoacoustic inversion: it relies on the use of a “TOSSIT” passive acoustic mooring and a“RIUSS” (Rupture Induced, Underwater Sound Source).

1

jbonnel@whoi.edu

2. TOSSITA TOSSIT is a custom-made mooring developed at the Woods Hole Oceanographic Institution(WHOI). It consists of a Soundtrap acoustic recorder (ST300) and a VEMCO acoustic release(V2R). It is mounted on a rope-free and shackle-free frame.
The TOSSIT’s simple rope-less concept removes strumming noise and entanglement risk formarine life. Thanks to their simplicity, TOSSITs can easily be deployed from small (e.g. rigid-hull
inflatable) boats or from large oceanographic vessels. All the TOSSIT components are depth rated toat least 500 m.
Figure 1 shows a TOSSIT picture as well as its assembly design. A detailed description of the
TOSSIT can be found in Ref. [1].

Figure 1: A TOSSIT mooring.

3. RIUSSThe RIUSS is a simple device used to create high amplitude, broadband acoustic pulses forunderwater acoustics experiments and surveys [2]. It uses a submersible vacuum chamber and arupture disc, which is an expendable diaphragm that breaks under a given static pressure.
A RIUSS is a fully mechanical system without any electronic components. To deploy a RIUSS,
the unit is simply lowered or dropped through the water column until the pressure differential
between the two faces of the disc exceeds that of the disc’s pressure rating. Upon rupture of the disc,a quick inrush of water collapses into the evacuated chamber, which creates a high amplitude,
broadband acoustic pulse.
In this article, we consider an expandable version of the RIUSS [3] which can be hand-deployed
in a quick and easy expendable manner from any kind of vessel. A picture of this system is shown in
Fig. 2.

Figure 2: An expandable RIUSS.

4. AT SEA EXPERIMENT AND INVERSIONA TOSSIT/RIUSS experiment was performed on May 21 2021 on the New England Mud Patch(NEMP), about 100 km south of Martha's Vineyard, MA, USA. The experiment was performed
from a 22.5 m long scallop fishing vessel, the F/V Kathryn Marie.
Several TOSSIT/RIUSS were deployed by hand by two operators, and two CTD casts were
performed using an autonomous instrument attached to a fishing winch. At the end of theexperiment, the TOSSIT were recovered by the same operators using a pole with a hook. The wholeexperiment, once the NEMP experimental site was reached, was run in less than four hours.
The data collected on a single TOSSIT/RIUSS pair is considered here. Time-frequency modal
dispersion was estimated using warping. Those dispersion data were subsequently used as input forBayesian trans-dimensional geoacoustic inversion. The inversion results, which will be presented indetail during the conference, compare favorably to results obtained with data collected on the same
track with traditional assets (e.g. a vertical line array) during the 2017 Seabed Characterization
Experiment.

ACKNOWLEDGEMENTS
Work supported by the Office of Naval Research.

REFERENCES
1. Zitterbart DP, Bocconcelli A, Ochs M, Bonnel J. TOSSIT: A low-cost, hand deployable, ropeless and acoustically silent mooring for underwater passive acoustic monitoring. HardwareX.
2022; 11, e00304.
2. McNeese AR, Lee KM, Sagers JD, Lee MJ, Wilson PS. Experimental observations of a rupture
induced underwater sound source. J Acoustic Soc Am. 2020; 148(4), EL370-EL374.
3. McNeese AR., Bonnel J, Wilson, PS. Towards a field deployable alternative to underwater
explosive charges. . J Acoustic Soc Am. 2021;150(4), A116-A116.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0151

Application of noise interferometry to acoustic characterization of
the coastal ocean on different time scales
Tsu Wei TAN1; Oleg A. GODIN 2
1

Department of Marine Science, ROC Naval Academy, Kaohsiung 81345, Taiwan (ROC)
2

Physics Department, Naval Postgraduate School, Monterey, CA 93943, USA

ABSTRACT
Noise interferometry takes advantage of uncontrolled, spatially distributed noise sources to extract
environmental information by cross-correlating acoustic pressure recorded by hydrophone pairs. Empiricalestimates of the Green’s function are obtained from two-point noise cross-correlation functions (NCCFs),
which serve as the signal to probe the ocean. For each hydrophone pair, detailed and robust environmental
information is retrieved by using the warping transform to separate normal mode components of NCCF andmeasure their dispersion curves. The methodology has been applied to the data acquired in the Straits ofFlorida and on the continental shelf off New Jersey. Geoacoustic parameters of the seabed are found fromNCCFs evaluated using long averaging times of about 1–2 weeks. Similar averaging times are employed tocharacterize subseasonal variations in the sound speed profile (SSP) in water. In dynamic environments, thesesound speed profiles describe time averages of the sound speed. It is found that long-range NCCFs can bealso reliably retrieved with noise averaging times as short as one minute, which suggests the ability topassively monitor ocean dynamics in real time. The inversion results obtained with long and short averaging
times will be compared and discussed. [Work supported by NSF, ONR, and MOST.]
Keywords: Noise Interferometry, Time Warping, Passive Inversion

1. INTRODUCTION

Acoustic noise interferometry (NI) (1-3) exploits ambient and shipping noise as a signal to probe
the ocean environment and offers a way to measure its physical parameters without using any
controlled sound sources. This passive technique relies on time averaging to retrieve empirical Green’s
function from noise cross-correlations using hydrophone pairs. We obtain noise cross-correlation
functions (NCCFs) on different time scales using the data acquired in two experiments (4, 5). In eachexperiment, 1–2 weeks of noise were recorded on near-bottom hydrophones, and cross-correlation
functions were calculated up to distances of about 8–10 km, or 100 water depths (Sec. 2.1 and Sec.
2.2). A signal processing technique, time-warping transform (6), was applied to extract dispersion
curves of normal modes from the obtained NCCFs (Sec. 2.3). The passively measured dispersion
curves have been inverted for the geoacoustic properties of ocean bottoms (7, 8), and to characterize
the sub-seasonal sound speed variations (9) (Sec. 3).

2. EXPERIMENTS AND NOISE CROSS-CORRELATION FUNCTIONS (NCCFs)
2.1

Florida Straits 2012 Experiment (FL12) with one NCCF from 6-day noise time series
The experimental site of FL12 noise interferometry experiment is illustrated in Fig. 1(a). The
acoustic noise recorded by two hydrophones A and B, separated horizontally by 5.01 km on thecontinental shelf close to the 100-m isobath in the Straits of Florida [Fig. 1(b)] were employed toconstruct the NCCF shown in Fig. 1(c). The negative- and positive-time-delay parts of the NCCF,
abbreviated as N-NCCF and P-NCCF, respectively, approximate the Green’s functions, which1

ttan1@nps.edu

correspond to sound propagation in opposite directions between hydrophones A and B. The largest
NCCF amplitudes and best signal-to-noise ratio (SNR) for retrieving the empirical Green’s functions
are found at time delays 3.20 s < |τ| < 3.80 s as the signal portions within the dashed-box indicated inFig. 1(c). With NI, this two-point cross-correlation function of noise from hydrophones A and B serveas the probing signals and replace the Green’s function measured in traditional active acoustic remotesensing.
2.2 Shallow Water 2006 Experiment (SW06) with 32 NCCFs from 15-day noise records
The multi-institutional SW06 experiment was carried out on a continental shelf about 120 nm fromNew York Harbor illustrated in Fig 1(d). The SW06 site is known for strong and rapid variations of
the sound speed in the water column due to energetic internal gravity waves, including internal tides
to make the application of NI more challenging. In the previous Sec. 2.1, only one hydrophone pair
was employed to develop one NCCF. Here a single hydrophone receiving unit (SHRU) and ahorizontal line array (HLA) of 32 hydrophones shown in Fig. 1(e) were employed. The spacing of
HLA hydrophones is 15 m, and the distance between SHRU and the HLA hydrophones ranges between
3.4 and 3.8 km. With 32 hydrophone pairs, 32 NCCFs illustrated in Fig.1(f) were obtained.

Figure 1 – Noise cross-correlation functions (NCCFs) obtained from two experiments on different time scales.
(a-c) The FL12 noise interferometry experiment, two bottom-moored hydrophones A and B form onehydrophone pair separated by 5 km were used to record the entire 6-day time series of acoustic pressure to
calculate the negative- and positive-time-delay parts of the NCCF, abbreviated as N-NCCF (blue) and PNCCF (red) (7). (d-f) The SW06 experiment. A hydrophone SHRU and a HLA of 32 hydrophones were
employed to calculate 32 N-NCCFs and 32 P-NCCFs as functions of time delay τ with a 15-day noise timeseries (8).
2.3 Time warping to retrieve normal mode travel times from NCCF
A single-receiver modal filtering method, the time-warping transform, was applied to a NCCF toseparate contributions of individual normal modes and extract the modal dispersion curves. The
dispersion curves are sensitive to environment and thus suitable as input data for an inverse problem
to retrieve geoacoustic properties of ocean bottom, the sound speed profile (SSP) in the water column,
etc.
The negative-time-delay part, N-NCCF between SHRU and the first HLA hydrophone in Fig.1(f) wasused as an input signal to interrogate the ocean environment, with its modal decomposition processillustrated in Fig. 2 to retrieve mode 2 dispersion curve as an example. This procedure was repeated,
and frequency dependence of the travel time was determined for the first three modes using optimum,
mode-, and HLA hydrophone-specific time-frequency masks for all measured N-NCCFs in Fig. 1(f).

Figure 2 – Application of the time-warping transform to a measured NCCF to separate its normal-modal
components and passively measure dispersion curves of the normal modes. The upper panel (a), (b) and (c)are signals in the time domain, with its respective spectrogram shown in the lower panel (a1), (b1), and (c1).
(a) The same negative-time-delay part, N-NCCF between SHRU and the first HLA hydrophone shown in
Fig. 1(f). The signal portion within the dashed box is used as estimated Green’s function for analysis. (a1)Spectrogram of (a) with spectral density shown in dB relative to an arbitrary reference. Overlaid white lines
display estimated dispersion curves of the first three normal modes that cannot be retrieved before time
warping due to modal interference. (b1) Spectrogram of the N-NCCF in (b) after time warping. Contributionsof individual normal modes are indicated. The area within the white dashed lines is the time-frequency mask
applied to isolate mode 2 as an unwarping stage of signal processing. (c1) Spectrogram of the mode 2
waveform after modal separation process and unwarping. White line shows the mode 2 dispersion curve as
frequency dependent travel time retrieved from the spectrogram.

3. INVERSION RESULTS AND CONCLUSION
3.1

Geoacoustic inversion

Assuming a geoacoustic model with five unknown parameters [Fig. 3(a)], the noise crosscorrelations obtained in the SW06 experiment were employed to characterize the ocean bottom. The
travel times and group speeds of normal modes were passively measured from the NCCFs of 15-day
averaging by the time warping technique outlined in Sec. 2.3. Mode dispersion curves were inverted toestimate the five unknown geoacoustic parameters. As an illustration of the inversion process, Figure 3(b)presents a sensitivity analysis of the model-data mismatch to sound speed in the sediment layer. The
inversion results from SW06 are largely consistent with earlier geoacoustic inversions employing
controlled sound sources in the same general area (10).
3.2Sound speed profile (SSP) inversion

Temporal variations of the water-column SSP have been evaluated using the noise measurements
in two consecutive 15-day observation periods, 08/03–08/17 and 08/18–09/01/2006 [Fig. 3(c)], andthe geoacoustic model, which was obtained (8) from the noise time series acquired in the second
observation period. Assuming the time-averaged SSP2 [Fig. 3(d)] in the second observation period tobe known, we further inverted the differences between the dispersion curves obtained from the two
15-day datasets for the variation of the time averaged SSP during the SW06 experiment. As shown in
Fig. 3(d), the passively inverted ssp1 is close to the ground truth SSP 1, which is provided by the time
average of the SSPs that were measured in situ during the first observation period (9).
3.3Conclusion

It is demonstrated that noise interferometry can be successfully used to acoustically characterize
the seafloor and time-averaged SSP on continental shelfs at different time scales despite strong soundspeed variations.

Figure 3 – Geoacoustic parameters and time-averaged sound speed profile inversion results by employing
noise only. (a) The geoacoustic model implied in the inversion process with the SSP in water and the water
depth assumed to be known. The unknown parameters to be determined are geoacoustic characteristics of thefluid seabed: sediment layer thickness H, sound speeds cs and cb in the sediment and basement, and the ratios
ρs and ρb of the densities of sediment and basement to that of water. (b) Sensitivity of the data-model mismatch
to individual parameters of the environmental model, using sediment sound speed cs as an example. The redcircle indicates the position of the cost function minimum and the best inferred value. The red dashed line
corresponds to the error bound. (c) Time-dependence of the sound speed in water measured in the SW06
experiment using a thermistor chain located near the HLA position shown in Fig.1(e). The first and second
15-day observation periods are indicated by the blue and red boxes, respectively. (d) Comparison of thereference 15-day average SSP2, ground-truth SSP1, and the retrieved ssp1 with error bars superimposed.

ACKNOWLEDGEMENTS

This research has been supported by the Office of Naval Research, awards N00014-18-WX-01725
and N00014-19-WX00462, the National Science Foundation, award OCE1657430, and the Taiwan
Ministry of Science and Technology, Grant No. 110-2611-M-012-003.

REFERENCES

1. Roux P, Kuperman W, Group N. Extracting coherent wave fronts from acoustic ambient noise in the ocean.
The Journal of the Acoustical Society of America. 2004;116(4):1995-2003.
2. Sabra KG, Roux P, Kuperman W. Emergence rate of the time-domain Green’s function from the ambient
noise cross-correlation function. The Journal of the Acoustical Society of America. 2005;118(6):3524-31.
3. Godin OA. Recovering the acoustic Green's function from ambient noise cross correlation in an
inhomogeneous moving medium. Phys Rev Lett. 2006;97(5):054301.
4. Brown MG, Godin OA, Williams NJ, Zabotin NA, Zabotina L, Banker GJ. Acoustic Green's function
extraction from ambient noise in a coastal ocean environment. Geophysical Research Letters.
2014;41(15):5555-62.
5. Newhall AE, Duda TF, von der Heydt K, Irish JD, Kemp JN, Lerner SA, et al. Acoustic and oceanographic
observations and configuration information for the WHOI moorings from the SW06 experiment. WOODSHOLE OCEANOGRAPHIC INSTITUTION MA; 2007.
6. Bonnel J, Thode A, Wright D, Chapman R. Nonlinear time-warping made simple: A step-by-step tutorial
on underwater acoustic modal separation with a single hydrophone. J Acoust Soc Am. 2020;147(3):1897926.
7. Tan TW, Godin OA, Brown MG, Zabotin NA. Characterizing the seabed in the Straits of Florida by using
acoustic noise interferometry and time warping. The Journal of the Acoustical Society of America.
2019;146(4):2321-34.
8. Tan TW, Godin OA, Katsnelson B, Yarina M. Passive geoacoustic inversion in the Mid-Atlantic Bight in
the presence of strong water column variability. The Journal of Acoustical Society of America.
2020;147(6):EL453-9.
9. Tan TW, Godin OA. Passive acoustic characterization of sub-seasonal sound speed variations in a coastal
ocean. J Acoust Soc Am. 2021;150(4):2717-37.
10. Bonnel J, Pecknold SP, Hines PC, Chapman NR. An experimental benchmark for geoacoustic inversion
methods. IEEE Journal of Oceanic Engineering. 2020;46(1):261-82.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea
Extended abstractABS-0268

Geoacoustic inversion using air gun sounds measured by a singlehydrophone in the East Siberian Sea
Dae Hyeok Lee1; Jee Woong Choi2; Dong-Gyun Han3; Hyoung Sul La4; Eun Jin Yang5
1,2

Department of Marine Science and Convergence Engineering, Hanyang University ERICA, Republic of Korea
2

Department of Military Information Engineering, Hanyang University ERICA, Republic of Korea
3,4,5

Division of Ocean Sciences, Korea Polar Research Institute, Republic of Korea

ABSTRACT
Korea Polar Research Institute had operated after installing a bottom-moored single hydrophone in the East
Siberian continental margin in the Arctic Ocean for about one year from August 2019 for long-term
underwater noise monitoring. In addition, a seismic survey using an air gun was conducted in September
2019, and the air gun sounds were received by the hydrophone. The modal dispersion curves were observedin the result of spectrogram analysis of the air-gun signals received at distances of several tens of km between
the sound source and the hydrophone. We extracted the dispersion curves from the spectrogram using a timewarping signal processing. Inversion of geoacoustic parameters such as sound speed and density in thesediment layer was carried out by minimizing the mismatch between extracted dispersion curves and thereplicas predicted by the KRAKEN normal-mode propagation program. The results will be discussed throughcomparison with the sedimentary environment of the East Siberian Sea.
Keywords: Air gun sounds, Dispersion curves, Geoacoustic inversion, Single hydrophone, Arctic ocean

1. INTRODUCTION
The East Siberian is one of the least studied regions in the Arctic Sea, located between the Laptev
Sea and the Chukchi Sea. The East Siberian Sea is a very wide, flat, and shallow hard bottom shelfregion with an average water depth of ~58 m (1). In this ocean environment, where the waveguide isnearly range-independent, low-frequency impulsive sound propagating over several kilometers isdispersive and can be described as the sum of several modal components by normal mode theory (2,3).
In this study, the seabed geoacoustic parameters was estimated by matching the modal dispersion
curves of air gun sounds measured at distances of several tens of km with the model predictions.

2. FIELD MEASUREMENTS
The long-term underwater noise monitoring was conducted in the East Siberian continental marginfor one year from August 2019. A single self-recording hydrophone (AURAL-M2, Multi-Electronique
Inc., Canada) was deployed 13 m above the seafloor at location 74° 37.327'N, 174° 56.397'E by theicebreaking research vessel R/V Araon, operated by Korea Polar Research Institute. The mooring site
was an almost flat area at a depth of 70 m. The acoustic data were sampled at a 32,768 Hz for 10
minutes every hour. A seismic survey using the air gun installed on the R/V Araon was conductedfrom September 2 to 10, 2019, within the monitoring period. The low-frequency impulsive air gun
sounds were received by a single hydrophone every ~11 sec. The conductivity-temperature-depth(CTD) casts were made twice on 14 September to measure the sound speed profiles.

1

edh0921@hanyang.ac.kr
choijw@hanyang.ac.kr
3dghan@kopri.re.kr
4
hsla@kopri.re.kr
5ejyang@kopri.re.kr
2

3. RESULTS
The modal dispersion curve of the received signal can be estimated using time-frequency analysisof the air gun signal received at a distance of several tens of km between the sound source and thehydrophone. Figure 1A and B show the time-series signal and its corresponding spectrogram,respectively, received at a range of 18.7 km. First and second modes are clearly visible and groundwave (4) is clearly presented at frequencies below the Airy phase. To estimate geoacoustic parameterssuch as sound speed and density in the sediment layer, the dispersion curves were first extracted fromthe spectrogram of the received signal using a warping method (3,5), and geoacoustic parameters werethe estimated by an optimization method matching the extracted dispersion curves with the replicas
predicted by the KRAKEN normal-mode program.

Figure 1 – (A) Time series of the air-gun signal received at 18.7 km range and (B) its spectrogram.

4. CONCLUSIONS
The geoacoustic inversion is carried out by minimizing the mismatch between the dispersion curves
extracted using the warping method and the simulated replicas. These inversion results may bevaluable in that they provided an indirect measure of geoacoustic parameters of the East Siberiancontinental margin in the Arctic Ocean.

ACKNOWLEDGEMENTS
This research was a part of the project titled ‘Korea-Arctic Ocean Warming and Response of
Ecosystem(K-AWARE, KOPRI, 1525011760)’, funded by the Ministry of Oceans and Fisheries,
Korea and supported by the National Research Foundation of Korea(NRF) grant funded by the Korea
government(MSIT) (2020R1A2C2007772)

REFERENCES
1. Gorbatenko, K.M., Kiyashko, S.I. Zooplankton Composition and Trophic Status of Aquatic Species
in the Laptev and East Siberian Seas. Oceanology. 2019;59:893–902.
2. F. Jensen, W. Kuperman, M. B. Porter, H. Schmidt. Computational Ocean Acoustics. AIP, New York,USA, 1994.
3. Julien Bonnel, Stan E. Dosso, N. Ross Chapman. Bayesian geoacoustic inversion of single hydrophonelight bulb data using warping dispersion analysis. J Acoust Soc Am. 2013;134(1):120-130.
4. Jee Woong Choi, Peter H. Dahl. First-order and zeroth-order head waves, their sequence, and implications
for geoacoustic inversion. J Acoust Soc Am. 2006;119(6):3660-3668.
5. Dae Hyeok Lee, Jee Woong Choi, Dong-Gyun Han, Hyoung Sul La, Eun Jin Yang. Geoacoustic inversion
using explosive air gun signals received by a single hydrophone in the East Siberian Sea, Arctic Ocean. J
Acoust Soc Am. 2021;150:A115.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0288

Geoacoustic inversion using waveform matching as a preliminary
step in dispersion curve analysis to assess bottom attenuation froma single vector sensor
Alexandre L. Guarino, Kevin B. Smith, Kay Gemba, and Oleg A. Godin
Physics, Naval Postgraduate School, USA

ABSTRACT
Geoacoustic inversion using dispersion curves is very popular, but such techniques suffer from intrinsic errors
associated with the separation of normal modes via time-warping methods which, for example, can be
improved using band-pass filter masking. The combination of pressure and vertical velocity dispersion curves
from a vector sensor measurement also can improve results. This paper discusses the value added by using
waveform matching as a preliminary step to improve the quality of the dispersion curve inversion method
and its effect on the inversion performance. In addition, within the waveform matching approach, theestimation of bottom attenuation using the modal phase difference between pressure and vertical velocity is
tested. In this analysis, the KRAKEN propagation model is used for synthetic predictions, including
components of the vector field. The environment is modeled utilizing sound speed profiles measured during
the Monterey Bay 2019 at-sea experiment with a sediment layer overlying a deeper sub-bottom type.
Inversion of experimental data is presented with a hybrid optimization approach used to improve the results
and reduce uncertainty of the genetic algorithm method.
Keywords: geoacoustic inversion, time-warping, dispersion curves, acoustic vector sensor.

1. INTRODUCTION
In shallow water ocean waveguides, sound propagation presents a dispersive behavior, being
greatly influenced by the ocean bottom properties. Such behavior is represented by dispersion curves(DCs) which reflect the dependency of normal mode travel time with frequency, one of the most
broadly used features (1,2,3,4) in ocean remote sensing for the geoacoustic inversion of environmental
parameters. Normal mode properties can be identified using different approaches, and time-warping(TW) is one that has been increasingly adopted by the acoustics community for modal separation. It
is a technique that relies on one sensor using broadband signals, which simplifies the at-sea probingtask. Bonnel, et al. present details about TW in (2).
The benefit of utilizing DC features stems from their lack of sensitivity to certain parameters, suchas bottom attenuation, source spectrum, or source depth. However, this comes at the cost of neglecting
potential information contained in the full time-domain waveform, and at the cost of using data limited
to those parts of the DCs obtained using TW. Moreover, DC inversion techniques suffer from intrinsic
errors and distortions associated with normal mode separation via TW methods, such as [1] the lackof high resolution in time-frequency analysis, [2] the use of an imperfect TW operator based on theideal waveguide, which affects performance in more complex environments, and [3] the abrupt
transitions included in the processing caused by rectangular windows applied both in the time-domain,
before the warping transformation, and in the time-frequency domain using binary masks. Someapproaches have been proposed to attenuate such distortions, like the use of a band-pass filter (BPF)mask (5) and utilization of additional acoustic channels included in vector sensors.
Many researchers have studied vector sensors in geoacoustic inversion, beyond their applicationin direction finding or target tracking. In 2021, Guarino, et al. (5) showed that the modal phasedifference between pressure and vertical velocity can be a possible candidate for the estimation of thebottom attenuation coefficient. This has been traditionally estimated, for example, using pressureonly sensors and mode amplitudes, as done by Duan, et al. (6) in 2016. In 2022, Guarino, et al. (7)discussed the value added by doing multichannel averaging of pressure and vertical velocity DCs to
reduce distortions.
This paper expands the analysis done in (7). The former study investigated the averaging
hypothesis using synthetic signals and data collected on a single vector sensor deployed on thenorthern shelf during the Monterey Bay 2019 experiment. The inversion showed that the combination
adds value to the pressure-only analysis and improved results.
This work uses the same data and environmental model of (7). The inversion utilizes sound speed
profiles measured during the test event and assumes a sediment layer of constant thickness overlying
a deeper sub-bottom type. Since data can be contaminated by other sources of error like noise andmode coupling, these effects can worsen DC quality even more.
In this context, this paper incorporates both DC and waveform matching inversion methods ascomplementary to each other. It discusses the value added by using waveform matching (8) as apreliminary step to improve the performance of the DC inversion, which provides initial estimates ofprimary geoacoustic parameters, while waveform matching inversion will refine those results to
produce a superior solution in the end. The particular way DC and waveform matching approaches
are combined in this study is guided by experience acquired from extensive numerical simulations(7) under conditions approximating the conditions encountered in the Monterey Bay experim ent. The
paper shows that waveform matching can provide a way to check the DC quality and improve DC
inversion. It is also shown that waveform matching results can be refined with simultaneous matchingof pressure and vertical velocity waveforms, and using the modal phase difference for better estimatesof bottom attenuation.
To show this, the KRAKEN (9) normal mode model is applied for synthetic predictions, which
include DCs based on group speed, and full-field and individual mode predictions for both pressureand vertical velocity using the adiabatic approximation. To guarantee optimum results, KRAKEN is
first calibrated using the parabolic equation model RAM (10) as a reference.
As for the optimization approach, the same structure is used as in (7). Genetic algorithms (GAs)can rapidly locate the region in which the global optimum exists. However, it can be slow to locate
the exact local optimum in the region of convergence. A hybrid optimization approach is implemented
to expedite convergence.
This paper is organized as follows: the sea test characteristics, environmental model and DC
inversion scheme are briefly explained, and details can be found in (7). The inversion scheme forwaveform matching is defined. Next, DC inversion is applied using the multichannel combination ofpressure and vertical velocity and BPF mask using TW, as shown in (7) to be the best option.
Waveform matching is then applied individually for pressure and vertical velocity data, and for bothsignals simultaneously. After that, the modal phase difference approach is presented and used to refine
the waveform matching outcomes. The estimated parameters from both DC inversion and waveform
matching inversion are used to generate synthetic DC, and such replicas are compared with the DC
from data to evaluate the quality of the DC inversion.
The results show that in the case of this paper, waveform matching inversion performs better thanthe DC matching inversion, supported by the modal phase difference approach and simultaneous
matching. The manuscript concludes with our analysis of experimental data followed by a discussionof results.

2. SEA TEST CHARACTERISTICS AND ENVIRONMENTAL MODEL
This section presents a description of the sea test characteristics and environmental model. Moredetails can be found in (7). The acoustic data were collected in 2019 in Monterey Bay. Figure 1(a)shows a bathymetric map of the experiment location. The location of two yellow markers labeled Shot
3 and Shot 4 correspond to two light bulb [Figure 1(b)] implosions. The environmental model assumes
a sediment layer of constant thickness overlying a deeper sub-bottom type, as illustrated in Figure1(c).
Prior information from two CTD casts is used in this work: CTD6 and CTD5 are taken at thevicinity of the source (light bulb) and receiver (vector sensor). The CTD casts measured all the watercolumn and were modeled as range-dependent. A reference hydrophone near the source was used,which allowed us to estimate the source depth for Shots 3 and 4 of 29.8 and 29.7 m, respectively. A
Geospectrum M20-105 vector sensor system was used in the sea test. The sensor itself was suspended
from the frame with a nylon rope as shown in Figure 1(d). The system frame was placed on the seafloor
at ~85 m depth [Figure 1(c)]. To mitigate flow noise, the system was covered prior to the deployment
with a fabric.

As for the effective bottom model (4), the sub-bottom has constant sound speed, density andattenuation coefficient, whereas the sediment layer has all parameters constant except for the soundspeed that has a constant gradient. Regarding ground truth information (4), the bottom is composed
mainly by mud, very fine sand, and silt according to the California Seafloor Mapping Program (11).

(a)

(b)

(c)
(d)
Figure 1 - Overall sea test characteristics. (a) Location of the test event in Monterey Bay, showing thepositions of the two light bulb implosions in yellow, CTD castings in white and vector sensor in red.
(b) Light bulb system setup. The implosion is carried out sending a messenger from the boat thattravels along the rope and breaks the light bulb. (c) Range dependent environmental model used in
the analysis. (d) Vector sensor system used in the sea test, model GTI M20-105.

3. INVERSION METHODS
The inversion methods use DCs directly computed by KRAKEN group speed, and full-field andindividual mode predictions, which are used for waveform matching and phase difference approaches,
applying an adiabatic approximation. Since KRAKEN is a normal mode model, the number of range
segments is determined based on the constraint that the maximum bathymetry vertical step must be
less than or equal to the highest frequency component wavelength, as shown in (7). Figure 2 shows acomparison between KRAKEN and RAM for calibration purposes for a certain set of environmental
parameters, according to Figure 1(c).

Figure 2 – Comparison between KRAKEN and RAM for a certain set of environmental parametersaccording to Figure 1(c). Source signature rectangular windowed ranging from 80 to 600 Hz. Bothwaveforms match consistently for calibration purposes.
The hybrid optimization approach applies two GA instances followed by two Bayesian
optimization instances, where the lower and upper bounds of each searched parameter shrink
accordingly as the optimization process moves forward. The optimization tools are based on the
Matlab function “ga” (12) within the Global Optimization Toolbox and on the function “ bayesopt”(13) within the Statistics and Machine Learning Toolbox.
The cost function 𝑑𝐷𝐶 for the DC inversion is based on the minimization of the mean squared error
average between the DC estimate, which is calculated from data, and DC replica travel times withfrequency being the independent variable according to
𝑀

𝑁𝑚

1
1
2
𝑑𝐷𝐶 = ∑∑[𝑡𝑚 (𝑓𝑖 ) − 𝑡𝑚 𝑟 (𝑓𝑖 ) + 𝑑𝑡] ,
𝑀
𝑁𝑚
𝑚=1

(1)

𝑖=1

where 𝑀 is the number of modes, 𝑁𝑚 is the number of points of a specific mode DC, 𝑡𝑚 is the DC
estimate travel time, 𝑡𝑚 𝑟 is the DC replica travel time and 𝑑𝑡 is a time shift included as a searchparameter when the sea test signals are used to compensate for small range mismatches. The costfunction 𝑑𝑊𝑀 for the waveform matching is defined as𝑀

𝑑𝑊𝑀 = √|𝑦 𝑇 . 𝑦𝑟 |12∑[𝑦[𝑖 + 𝑁] − 𝑦𝑟 [𝑖]] (1 −),|𝑦||𝑦𝑟 |
𝑀

(2)

𝑖=1

where 𝑦 are data (pressure or vertical velocity), 𝑦𝑟 is the replica, and 𝑁 is a shift included as a searchparameter to account for range mismatches. The first term is the root mean square error (RMSE) andthe second term includes the correlation coefficient. The calculation is done using only the portion ofdata where signal is present. The choice for both terms is a way to avoid replicas that have good
matching only for the very initial portion of the signal, i.e., to avoid situations where the optimization
forces the remaining portion to ~ zero for better RMSE-only results. Therefore, the choice for bothterms allows for replicas that do not match the whole signal perfectly, working as a balance between
both calculations, with the first part mostly matching amplitude, and the second part mostly matchingphase. This approach was found useful for the employed reduced order bottom model.

4. INVERSION OF DATA USING DCs
Data are inverted using shots 3 and 4. Both shots have high SNR for pressure and vertical velocity
signals. To illustrate, considering shot 4 the SNR values are 38.3 dB and 31.1 dB, respectively. To
improve the separation of modes in the warped-domain, according to Duan, et al. (14), source
deconvolution is applied, which is also essential for the frequency components to be generated at thesame instant. Details about the pressure and vertical velocity full-field and deconvolved signals canbe found in (7). The modes are then separated using TW with a BPF mask, as shown in Figure 3(a)for the pressure signal while Figure 3(b) shows the warped-domain spectrogram for the verticalvelocity signal, and Figure 3(c) shows the pressure and vertical velocity retrieved DCs.

mode 10mode 9mode 7
mode 5mode 4

(a)

(b)

(c)

Figure 3 – Shot 4 data. (a) Warped-domain spectrogram for deconvolved pressure signal using BPF
mask centered in the line indicated by the connected dots. (b) Warped-domain spectrogram fordeconvolved vertical velocity signal. (c) Deconvolved pressure and vertical velocity retrieved DCs
for modes 4, 5, 7, 9, 10.
The warped-time spectrograms and DCs for shot 3 can be found in (7). Table 1 shows the estimatedparameters for shots 3 and 4, and Figure 4 shows the DCs and the full-field collected data comparedwith Table 1’s shot 4 replica. The number of data points for shot 4 in Figure 4(a) varies from 72 to
121 per mode, depending on the mode number. It is limited to those parts of the DCs that was possible
to retrieve using TW.
Table 1 – Estimated parameters for shots 3 and 4 using the combination of pressure and verticalvelocity DCs. cs and cb are the sediment and sub-bottom sound speeds (m/s), c s is the sedimentsound speed gradient (m/s/m), s and b are sediment and sub-bottom densities (g/cm3 ), as andab are the sediment and sub-bottom attenuation coefficients (dB/m/kHz), and Hs is the sediment
thickness (m).
as
Hs
cb
ab
range
c s
s
b
cs
(m/s)

(m/s/m)(g/cm3 )(dB/m/kHz)

(m)

(m/s)

(g/cm3 )(dB/m/kHz)(m)

Shot 3

1574

8.5

1.29

x

5.0

1914

2.00

x

3100

Shot 4

1577

7.5

1.32

x

6.4

1773

1.60

x

3083

The range estimates in Table 1 are different because shots 3 and 4 were taken at slightly different
locations as shown in (7). Figures 4(b,c) show that the parameters found using the DC inversion do
not provide a good matching in the time-domain. It can be noticed in the same figure that the replica
low order modes travel faster than the ones in data, which suggests, for example, that sediment soundspeed is overestimated. This suggests that the DC inversion must be compared with waveform
matching, results for which will be presented in a subsequent section. Prior to that comparison, thenext section presents the modal phase difference approach.

(b)

(c)
Figure 4 – Comparison between data and replica for shot 4 estimated parameters. (a) Pressure channel
DCs for modes 4, 5, 7, 9, 10. (b) Pressure channel full-field. (c) Vertical velocity channel full-field.

5. MODAL PHASE DIFFERENCE APPROACH
The modal phase difference is directly related to the modal attenuation coefficient. Following ananalogous derivation as in (5), the modal pressure (𝑝𝑚 ) using the adiabatic approximation is givenby (15)𝑝𝑚 (𝑟, 𝑧) 

𝑗
√8𝑟

𝑒

−𝑗


4

𝑟

𝑚 (0, 𝑧𝑠 )𝑚 (𝑟, 𝑧)′

𝑒 𝑗 ∫0 𝑘𝑚(𝑟 )𝑑𝑟√𝑘𝑚 (𝑟)′,(3)

where 𝑟 is the range, 𝑧 the receiver depth, 𝑧𝑠 the source depth,  the water density, 𝑚 is the mode
function, and 𝑘𝑚 the horizontal wavenumber.
The modal vertical velocity (𝑢𝑧𝑚 ) is obtained from the modal pressure gradient using the Euler
equation
𝑢𝑧𝑚 =

1 𝜕𝑝𝑚
,
𝑗 𝜕𝑧(4)

which is found to be𝜕𝑚 (𝑟, 𝑧)𝑝𝑚
𝜕𝑧𝑢𝑧𝑚 (𝑟, 𝑧) =,𝑗 𝑚 (𝑟, 𝑧)(5)where  is the angular frequency.
The modal phase difference () can be obtained from the modal vertical intensity (𝐼𝑧 𝑚 ) given by∗

𝐼𝑧 𝑚 (𝑟, 𝑧) =𝑝𝑚 𝑢𝑧 𝑚2∗

𝜕𝑚 (𝑟, 𝑧)|𝑝𝑚 |2𝜕𝑧=𝑗[] = 𝐴 𝑒 𝑗 .
2 𝑚 (𝑟, 𝑧)(6)

In the case of this paper, 𝑚 is obtained using KRAKENC. However, since the propagation model
has been updated to provide both full-field and individual mode components of pressure and verticalvelocity in the time-domain, ultimately using Matlab the calculation is done by = 𝑎𝑛𝑔𝑙𝑒 (𝑓𝑓𝑡(𝑝𝑚 ).∗ 𝑐𝑜𝑛𝑗 (𝑓𝑓𝑡(𝑢𝑧 𝑚 ))).
In the case of data, the modal phase difference is calculated in the same way, based on the modal
pressure and modal vertical velocity signals that are obtained using TW.

6. INVERSION OF DATA USING WAVEFORM MATCHING
The optimization using waveform matching applies the same hybrid optimization approach of theDC matching, except for the cost function that uses Equation 2. The inversion is done using shots 3
and 4, matching pressure and vertical velocity individually, and simultaneously using both signals.
In the case of the simultaneous matching, Equation 2 is calculated for each signal, and then the results
multiplied. Table 2 shows the estimated parameters.
Table 2 – Estimated parameters for shots 3 and 4 using waveform matching.
as
Hs
cb
ab
c s
s
b
cs

range

(m/s)

(m/s/m)(g/cm3 )(dB/m/kHz)

(m)

(m/s)

(g/cm3 )(dB/m/kHz)(m)

S3 (𝑝)

1500

3.3

1.36

0.10

3.5

1670

2.37

0.39

3100

S3 (𝑢𝑧 )

1519

0.7

1.38

0.04

7.9

1673

2.14

1.75

3093

S4 (𝑝)

1501

4.9

1.51

0.02

3.8

1677

2.29

0.67

3081

S4 (𝑢𝑧 )

1508

3.0

1.39

0.06

5.1

1684

2.20

1.43

3089

S3 (𝑝, 𝑢𝑧 )

1503

7.2

1.55

0.24

4.8

1672

2.31

0.66

3090

S4 (𝒑, 𝒖𝒛)

1505

7.7

1.48

0.10

4.1

1672

2.28

0.68

3080

Table 2 shows that the inverted values for shots 3 and 4 using pressure-only have good agreement,
whereas using vertical velocity-only, some parameters seem to be overestimated, e.g., 𝑐𝑠 , 𝐻𝑠 , and 𝑎𝑏 .
The simplicity of the bottom effective model may be responsible for the increase in 𝑎𝑏 , which ends
up being correlated with other parameters that are equally erroneously estimated. Therefore, when it
comes to the estimation of sediment and sub-bottom attenuation coefficients, the results vary
substantially. On the other hand, when simultaneous matching is applied, the results 𝑆3 (𝑝, 𝑢𝑧 ) and𝑆4 (𝑝, 𝑢𝑧 ) have better agreement. The modal phase difference between pressure and vertical velocity

is then used to refine the results of Table 2. To illustrate that, shot 4’s mode 5 is used and its waveforms
for pressure and vertical velocity are separated from the full-field signals as shown in Figure 5. Next,
the modal phase difference is calculated using Equation 6 and the results are shown in Figure 6 forshot 3 in blue and shot 4 in red. Next, KRAKEN is used to generate the individual modes for pressureand vertical velocity for each set of inverted values of Table 2. The phase difference is then calculatedusing Equation 6, and the results shown in Figure 6(a-f), where data and replica can be compared.
𝑆4 (𝑝, 𝑢𝑧 ) is the overall best result. Figure 7 shows the comparison of waveforms between data andreplica using 𝑆4 (𝑝, 𝑢𝑧 ) inverted values.

Figure 5 – Shot 4’s mode 5 waveforms for pressure and vertical velocity after the separation using
TW with BPF mask.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 6 - Modal phase difference between pressure and vertical velocity, including shots 3 and 4, andreplicas using parameters of Table 2. (a) 𝑆3 (𝑝). (b) 𝑆3 (𝑢𝑧 ). (c) 𝑆4 (𝑝). (d) 𝑆4 (𝑢𝑧 ). (e) 𝑆3 (𝑝, 𝑢𝑧 ).
(f) 𝑆4 (𝑝, 𝑢𝑧 ).

(a)(b)
Figure 7 – Comparison of waveforms between data and replica using inverted values 𝑆4 (𝑝, 𝑢𝑧 ). (a)Pressure with correlation coefficient of 0.92. (b) Vertical velocity with correlation coefficient of 0.78.

7. COMPARISON BETWEEN DC AND WAVEFORM MATCHING INVERSIONS
To compare results from both techniques, the waveform matching estimated parameters 𝑆4 (𝑝, 𝑢𝑧 )are used to generate synthetic DCs. Figure 8 shows the comparison considering only the pressure
signal.

(a)(b)

Figure 8 – Comparison between inversion techniques. (a) The DC replica uses DC inversion estimatedparameters. (b) The DC replica uses waveform matching estimated parameters.
The synthetic DCs using waveform matching shown in Figure 8(b) reveal the distortions in data
for mode 4 above 200 Hz, mode 5 above 250 Hz, and modes 7 and 9 above 365 Hz. Such distortions
are caused by intermodal interference in the warped-domain, more pronounced within the low ordermodes at higher frequency components since the modes travel more closely together, as shown in the
spectrogram of Figure 3(a). This effect ends up overestimating sediment sound speed in the DC
inversion from 1505 to 1577 m/s, for instance. Doing the DC inversion again suppressing thoseportions of data, Table 3 compares DC inversion without suppression, DC inversion with suppression,
and waveform matching. As expected, the results improve, getting closer to the ones estimated by the
waveform matching.
Table 3 – Comparison of results between DC inversion and waveform matching suppressing parts of
the DCs to improve results.

Shot 4(DC no suppression)

Shot 4(DC with suppression)

S4 (𝑝, 𝑢𝑧 )(waveform matching)

cs

c s

s

as

Hs

cb

b

ab

range

(m/s)

(m/s/m)(g/cm3 )(dB/m/kHz)

(m)

(m/s)

(g/cm3 )(dB/m/kHz)

(m)

1577

7.5

1.32

x

6.4

1773

1.60

x

3083

1509

1.3

1.44

x

3.6

1674

1.76

x

3090

1505

7.7

1.48

0.10

4.1

1672

2.28

0.68

3080

8. DISCUSSION
The DC inversion using TW to separate modes is broadly used (3,14,16). One of its advantages isrelated to the optimization process that requires less parameters to match, since the travel time ofnormal modes, for example, is not sensitive to the bottom attenuation. On the other hand, smallchanges in the environmental parameters cause small changes in the DC shapes, which makes thetechnique susceptible to the distortions caused by the separation of modes, for signal processing
reasons and physical reasons, like noise contamination. Aside from that, the tendency is to maximize
the DCs energy content, which means to separate the DCs with the longest duration in time as muchas possible to optimize results. However, this paper shows that such distortions can be very subtle.
There is no indication in Figure 3(c) of low quality DCs, for example. On the other hand, when thesame DCs were put against the waveform matching, the distortions became visible. The suppressionof those regions improved results as shown in Table 3 but decreased the energy content of the DCs,
making the optimization less sensitive to some parameters, which affected the estimation of sedimentsound speed gradient and range, for instance.
As for the waveform matching technique, the bottom reflectivity and attenuation affect the mode
amplitudes in the receiver. Then, an erroneous estimation of one can adversely affect the estimationof the other. Therefore, this paper shows that a more robust estimation of bottom attenuation is
obtained when a vector sensor is applied, using the modal phase difference between pressure andvertical velocity. In this paper, such calculation was used to refine the estimated parameters of Table
2. However, it can be easily incorporated to the cost function of the optimization process.
Additionally, Figure 7(b) shows that it is harder to match the vertical velocity signal compared tothe pressure signal, which is shown in Figure 7(a) with a correlation coefficient of 0.92. That can be
related to the simplicity of the bottom effective model. However, the correlation coefficient of 0.78
for the vertical velocity was helpful to the simultaneous matching that generated more stable results
as shown in Table 2.
The estimated parameters are consistent with the ground truth information about the region being
composed by mud, very fine sand, and silt according to the California Seafloor Mapping Program (11).
Finally, the results suggest based on the environment considered and data analysis of this paper,
although waveform matching can be used to improve the DC inversion, when a vector sensor isavailable for geoacoustic inversion, waveform matching performs better.

9. CONCLUSIONSThis paper discussed the value added by using waveform matching as a preliminary step to improve
the performance of the DC inversion method. The separation of modes using TW methods causes
distortions to the retrieved DCs, for signal processing or physical reasons, like noise contamination.
The results showed that waveform matching can be used to check the quality of the DCs retrieved
from data and improve results. Although the waveform matching technique has more parameters tomatch, like the bottom attenuation coefficient, we show that when a vector sensor is available,
waveform matching results can be refined. First, doing the simultaneous matching of pressure andvertical velocity, and second, using the modal phase difference approach. Although in this paper suchapproach was used to refine results after the inversion, if KRAKENC is used for the waveform
matching, full-field and individual mode predictions can be included, and the modal phase difference
between pressure and vertical velocity can be incorporated to the cost function of an optimization
process, which narrows down the bottom attenuation calculation, avoiding erroneous estimations
within correlated parameters. Overall, this study suggests that when a vector sensor is available forgeoacoustic inversion, waveform matching can improve results of the DC inversion technique, but inthe environment considered in this paper, waveform matching is a better option.

REFERENCES
1. Bonnel, Lin, Eleftherakis, Goff JA, Dosso, Chapman, et al. Geoacoustic inversion on the New England
Mud Patch using warping and dispersion curves of high-order modes. J. Acoustic. Soc. Am. 2018;
143(5): EL405-EL411.
2. Bonnel J, Thode A, Wright D, Chapman R. Nonlinear time-warping made simple: A step-by-step tutorial
on underwater acoustic modal separation with a single hydrophone. J. Acoust. Soc. Am.. 2020; 147(3):1897-1926.
3. Bonnel J, Chapman NR. Geoacoustic inversion in a dispersive waveguide using warping operators.

2011; 130(2): EL101-EL107.
4. Chapman NR, Shang EC. Review of geoacoustic inversion in underwater acoustics. J. Theor. Comput.
Acoust. 2021; 29(3): 2130004 (1-66).
5. Guarino A, Smith KB, Godin OA. Bottom attenuation coefficient inversion based on the modal phasedifference between pressure and vertical velocity from a single vector sensor. J. Theor. Comput. Acoust.
2022; 30(2): 2150008(1-16).
6. Duan R, Chapman NR, Yang K, Ma Y. Sequential inversion of modal data for sound attenuation in
sediment at the New Jersey Shelf. J. Acoust. Soc. Am. 2016; 139(1): 70-84.
7. Guarino AL, Smith KB, Gemba K, Godin OA. Geoacoustic inversion with a single vector sensor andmultichannel dispersion. J. Theor. Comp. Acoust. 2022; 1-18: "Submitted for review".
8. Gemba KL, Vazquez HJ, Fialkowski J, Edelmann GF, Dzieciuch MA, Hodgkiss WS. A performancecomparison between m-sequences and linear frequency-modulated sweeps for the estimation of traveltime with a moving source. J. Acoust. Soc. Am. 2021; 150(4): 2613–2623.
9. Porter MB. The KRAKEN Normal Mode Program. [Online].; 1997 [cited 2020 November. Available
from: https://oalib-acoustics.org/AcousticsToolbox/manual/kraken.html].
10. Collins MD. User's Guide for RAM Versions 1.0 and 1.0p.: Naval Research Laboratory; 1999.
11. Dartnell P, Maier KL, Erdey MD, Dieter BE, Golden NE, Johnson SY, et al. Monterey Canyon andVicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., sheet 10, scale
1:24,000. [Online].; 2016. Available from: http://dx.doi.org/10.3133/ofr20161072.
12. MathWorks Support: ga (Find minimum of function using genetic algorithm). [Online]. [cited 2022
February. Available from: https://www.mathworks.com/help/gads/ga.html].
13. MathWorks Support: bayesopt (Select optimal machine learning hyperparameters using Bayesian
optimization).
[Online].
[cited2022
February.
Availablefrom:https://www.mathworks.com/help/stats/bayesopt.html].
14. Duan R, Chapman NR, Yang K, Ma Y. Sequential inversion of modal data for sound attenuation in
sediment at the New Jersey Shelf. J. Acoust. Soc. Am. 2016; 139(1): 70-84.
15. Jensen FB, Kuperman WA, Porter MB, Schmidt H. Computational Ocean Acoustics New York:
Springer; 2011.
16. Godin OA, Katsnelson BG, Tan TW. Normal mode dispersion and time warping in the coastal ocean. J.
Acoust. Soc. Am.. 2019; 146(3): 205-211.
17. Turner SE. Underwater implosion of glass spheres. J. Acoust. Soc. Am. 2007; 121(2): 844-852.
18. Smith KB, Hermand JP, Leijen AV. Estimation of sediment attenuation from measurements of theacoustic vector field. In 8th Int. Conf. Theor. and Comp. Acoust., 2-5 July 2007; Crete, Greece.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0360

Model Selection in Quantitative Geoacoustic Inversion
and Uncertainty Estimation
Stan E. DOSSO1; Julien BONNEL2
1
2

School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada

Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution
Woods Hole, MA, USA

ABSTRACTThis paper examines two important components of quantitative model selection in Bayesian geoacoustic
inversion and uncertainty estimation. In particular, the problems of determining an appropriate geoacousticprofile parameterization and an appropriate data error model are considered and illustrated. Regarding
parameterization, rigorous approaches are applied for representing layered and gradient-based profiles,
consistent with the information content of the data, based on trans-dimensional (trans-D) and Bernsteinpolynomial (BP) formulations, respectively. Trans-D inversion marginalizes over an unknown number of
uniform layers, while BP inversion represents smooth gradients in terms of a polynomial of optimal order as
determined by the Bayesian information criterion. Regarding the error-model, which specifies thelikelihood/misfit function, parametric and non-parametric representations can be formulated, and qualitativeand quantitative statistical tests can be applied a posteriori to ensure an appropriate choice. Choices ofparameterization and error model are not independent, but must be considered in tandem. The various
approaches to model selection (parameterization and error model) are illustrated here for the inversion ofmodal-dispersion data, and have also been applied to a variety of other data types.
Keywords: Bayesian geoacoustic inversion, quantitative model selection, data error models

1. INTRODUCTION
Knowledge of the geoacoustic properties of seabed sediments is needed to understand andmodel/predict acoustic propagation in shallow-water environments, as required for a variety ofgeophysical, geotechnical, biological, and naval applications. Estimating seabed model parameters
via the inversion of ocean acoustic measurements (geoacoustic inversion), represents an attractive insitu alternative to direct (invasive) measurements, such as sediment coring, and has been the subjectof considerable research. However, geoacoustic inversion represents a nonlinear, non -unique inverse
problem for which quantitative estimates and uncertainties require rigorous consideration of theparameterization of the seabed geoacoustic model and the data error model.
In terms of model parameterization, the amount of seabed structure that can be resolved depends
on the information content of the acoustic data, which varies with the physics of the seabed acoustic
interaction, frequency content, measurement and theory errors, etc. Under-parameterizing the model
can preclude seabed structure that could have been resolved, biasing results and under-estimating
uncertainties, while over-parameterization can lead to spurious structure and over-estimating
uncertainties. Quantitative model selection applied to general model parameterizations ensures theinclusion of seabed structure that is reliably sensed by the data while precluding unjustified structure.
To quantify the information content of acoustic data to resolve seabed structure, a Bayesian
inversion formulation is applied that includes rigorous approaches to model selection and da ta error
modelling. Model selection approaches considered here include both layered and gradient
representations of seabed profiles using two different methods: (1) trans-dimensional (trans-D)
1
2

sdosso@uvic.ca
jbonnel@whoi.edu

inversion [1–6], and (2) Bernstein-polynomial (BP) basis functions [6,7], respectively. Trans-D
inversion considers the number of seabed layers as an unknown hyper-parameter in the inversion, andapplies the reversible jump Markov chain Monte Carlo algorithm to construct a Markov chain thatsamples from the posterior probability density of the trans-D parameter space. In BP inversion, theBayesian information criterion (BIC) [7] is applied in model selection to determine polynomial orders
consistent with the information content of the data: higher-order BPs can represent more complex
structure but include more parameters, which is penalized by the BIC.
The error model considered here assumes a multi-variate Gaussian distribution with unknown
variance and covariance; covariance estimation is formulated in terms of trans-D sampling of zerothand first-order autoregressive (AR) processes to avoid over- or under-parameterizing the error model[4]. The validity of these assumptions/approaches can examined with qualitative and quantitative
residual analyses [6]. Inversion results are considered in terms of marginal probability profiles forgeoacoustic properties (marginalized over the number of layers for trans-D inversion), which quantify
the resolution of seabed structure versus sub-bottom depth.
The two inversion approaches (trans-D and BP) are applied here to modal dispersion data collected
at the New England Mud Patch (NEMP) during the 2017 Seabed Characterization Experiment(SBCEX17) [9]. These data include high-order modes extracted via warping time-frequency analysis[5,10] of a pulsed sound-source signal recorded at a vertical line array (VLA) of hydrophones at a
range of approximately 4.5 km along a largely range-independent propagation track. Auxiliary
environmental measurements carried out on the NEMP as part of SBCEX17 include high -resolution
seismic reflection profiles which provide an indication of the sediment layering structure, and shallow
sediment cores, which indicate sediment type at the core sites. These measurements indicate an upper
mud layer approximately 10.7 m thick with increasing sand content near the base of layer, above alayer of sand < 1 m thick; deeper layers were also detected with the seismic data but were not sampled
in coring. The seismic and core measurements can be used for comparison with the geoacoustic
inversion results.

2. RESULTS
The observed modal dispersion data are shown in Fig. 1 in terms of arrival times as a function of
frequency for 18 of the first 21 modes. These data were extracted by applying warping time-frequency
analysis individually to the source recordings at 14 VLA hydrophones, with modal signal-to-noise
ratios (SNRs) assessed to determine which recording (i.e., which receiver depth) best resolved eachmode [5]. The SNR was poor for modes 16, 18, and 20 on all VLA receivers, precluding their inclusion

Figure 1: Observed modal dispersion data (open circles) for 18 of the first 21 modes, with modes
identified in (a). Blue lines show the predicted data as sampled in the Bayesian inversions. The data
fit for trans-D inversion is shown in (a), and for BP inversion in (b). Modified from [6].

in the data set, likely due to the source depth coinciding with nulls for these modes. This figure also
shows the fit to the data achieved by the two inversion approaches: trans -D inversion in Fig. 1(a) andBP inversion in Fig. 1(b). In both cases, the data fit is excellent, with virtually no discernabledifferences between results for the two inversions.
Figure 2 presents the results of the two approaches to Bayesian inversion and model selection
applied to the modal dispersion data, shown in terms of marginal probability profiles for sedimentinterface depths, sound speed c, and density 𝜌. The upper panels show the results of trans-D inversion,
which parameterizes geoacoustic profiles in terms of an unknown number of uniform layers, while
the lower panels show results for a hybrid inversion, which represents the mud layer in terms ofgeneral, smooth gradients parameterized by BPs, overlying deeper layers represented transdimensionally. The BIC indicated a fifth-order polynomial for sound speed and a third-orderpolynomial for density. Figure 2 shows similar geoacoustic profiles for the two inversion approaches,
consisting of a nearly uniform upper layer approximately 8 m thick with a low sound speed of about1470 m/s (with a small increase in the upper 3 m or so), above a region of increasing sound speed
from about 8–11, above a high-speed layer from about 11–13 m (~1800 m/s, but with highuncertainties), with a somewhat reduced sound speed (~1700 m/s) below this depth. This structure isin good agreement with the seismic reflection and core measurements (10.7 m mud layer withincreasing sand near its base overlying a sand layer) although the layer boundaries indicated byseismic reflectors (dashed lines in Fig. 2) are not clearly resolved by the inversion. Of note, the trans D inversion results represent the geoacoustic profile over the mud (to ~11 m depth) in terms of layered
structure, while the BP inversion represents structure with smooth gradients. The BIC d oes notindicate a significant preference of one inversion result over the other, indicati ng that the data are not

Figure 2: Geoacoustic inversion results in terms of marginal probability profiles for sedimentinterface depths (left column), sound speed c (middle column), and density 𝜌 (right column). The top
row shows results for trans-D inversion, while the bottom row shows results for hybrid BP/trans-D
inversion. The dashed lines represent, from top to bottom, the depths of seismic reflectors interpreted
as the base of an upper mud layer, a sand layer, and an unidentified layer. Modified from [6].
able to differentiate between the two results.

The trans-D error model for both of the two inversions indicated standard deviations ofapproximately 2 ms for the modal dispersion data, with some variability between modes; a first-order
AR process with coefficient > 0.5 was favoured for all modes to characterize the error covariance overfrequency. A posteriori statistical tests on data residuals (difference between observed and predicteddata) indicate that the assumptions of Gaussian-distributed errors with variances and covariances
estimated as part of the inversion are generally valid [6].

ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support of the Office of Naval Research in this work.

REFERENCES
1. Green PJ. Trans-dimensional Markov chain Monte Carlo. Oxford Statistical Science Series, Oxford, UK:
Oxford University Press, 2003.
2. Dettmer J, Dosso SE, Holland, CW. Trans-dimensional geoacoustic inversion. J Acoust Soc Am. 2010;
128(6), 3393–3405.
3. Dosso SE, Dettmer J, Steininger G, Holland CW. Efficient trans-dimensional Bayesian inversion forgeoacoustic profile estimation. Inverse Problems. 2014; 30(1), 1–29.
4. Steininger G, Dettmer J, Dosso SE, Holland, CW. Trans-dimensional joint inversion of seabed scatteringand reflection data. J Acoust Soc Am. 2013; 133(3), 1347–1357.
5. Bonnel J, Dosso SE, Knobles DP, Wilson PS. Trans-dimensional inversion on the New England Mud
Patch using high-order modes. IEEE J Oceanic Eng. 2022; 47(3), 607–619.
6. Dosso SE, Bonnel J. Hybrid seabed parameterization to investigate geoacoustic gradients at the New
England Mud Patch. IEEE J Oceanic Eng. 2022;47, 620–634.
7. Quijano J, Dosso SE, Dettmer J, Holland, CW. Geoacoustic inversion for the seabed transition layer usinga Bernstein polynomial model. J Acoust Soc Am. 2016; 140(6), 4073–4084.
8. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978; 4, 461–464.
9. Wilson PS, Knobles DP, Neilsen TB. Guest editorial: An overview of the Seabed Classification
Experiment. IEEE J Oceanic Eng. 2020; 45(1), 1–13.
10. Bonnel J, Thode A, Wright D, Chapman NR. Nonlinear time warping made simple: A step-by-step tutorial
on underwater acoustic modal separation with a single hydrophone. J Acoust Soc Am. 2020; 147(3),1897–1926.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0365

Broadband Acoustic Characterization of Mesopelagic Fishes
Andone C. Lavery1
1

Woods Hole Oceanographic Institution, Woods Hole, MA, USA

ABSTRACTThe mesopelagic zone, spanning depths from 200–1000 m, is one of Earth’s largest ecosystems
and home to a diverse community of marine animals. Mesopelagic fishes are some of the mostabundant vertebrates on earth, and are thought to play an important role in global ocean
biogeochemical cycles. Mesopelagic fishes are difficult to sample, or harvest by commercial fisheries,
due to their often remote and inhospitable habitats, and their ability to efficiently avoid trawl nets andcameras. The mesopelagic zone is characterized by deep sound scattering layers which are observed
to perform daily vertical migration. There has been increased interest in using narrowband shipboard
sonars to survey mesopelagic fishes, though little is known about their target strength. Recent
advances in broadband sonars and in situ platforms are being used to fill the technology gap and infer
target strengths of these elusive mesopelagic fishes. This talk presents an overview of the work donein this area to date, discusses the key challenges that still remain, and assesses the impact of broadband
acoustic techniques in advancing our knowledge of the target strength, abundance, biomass, anddistribution of mesopelagic fishes in the Ocean Twilight Zone. [Work supported by NSF and WHOIOTZ Project.]
Keywords: Broadband Acoustic Scattering, Deep Sound Scattering Layer, Daily Vertical Migration

1. INTRODUCTION
The mesopelagic zone, often referred to as the Ocean Twilight Zone (OTZ), is one of the largest
habitats on Earth, but there exist major gaps in our knowledge of the biology, adaptations, abundance,and distribution of its inhabitants. The OTZ is made up diverse communities of zooplankton,
macrozooplankton, and micronekton, including a variety of small fishes (collectively referred to as“mesopelagic fishes”), cephalopods, crustaceans, and gelatinous organisms, such as siphonophores.
Such mesopelagic organisms are often undersampled by conventional nets as some species are patchy
and sparse, some can effectively avoid capture by nets, and some are fragile and easily destroyed by
net systems [1-3]. As a consequence of this under-sampling, there are major gaps in our knowledgeof the biology, adaptations, abundance, and distribution of mesopelagic organisms, and these habitats
remain one of the least investigated components of ocean ecosystems [4].
The global biomass estimate of mesopelagic fishes based on net catches is approximately one
gigaton ton [5-6]. This estimate is based on net sampling gear and yet is often used in assessments ofecosystem function and the biogeochemistry of the global ocean [7]. There are estimates that onequarter of all known fish species in the Southern Ocean live in the mesopelagic and bathypelagiczones [8-9], and it is thought that the fish genus Cyclothone sp. is likely the most abundant vertebrateon earth [10]. Yet mesopelagic fishes have a notoriously effective avoidance behavior and there isevidence they may be under-sampled by as much as a factor of 10 [2,11]. Furthermore, many speciesof mesopelagic fish migrate at night into the epipelagic zone (0-200m) and Irigoien [11] have shownthat mesopelagic fish biomass and primary production are strongly coupled, suggesting the importanceof mesopelagic fishes in ocean biogeochemical cycles needs to be revised, and may end up playing a
potentially important role in the ocean carbon cycle.
Myctophids (aka lanternfish) are the most widespread mesopelagic fish family [12]. They play asignificant role as consumers of zooplankton in oceanic food webs [13], and are an important foodsource for higher trophic level predators, such as epipelagic and benthopelagic fish [14], including1

alavery@whoi.edu

deep water fish [15-17], seabirds [18], fur seals [19], squid [20,21], and whales [22,23], some of which
are either threatened or endangered. Beaked whales are known to dive and forage to mesopelagic
depths [24], and there is a broad association between the dive depths and acoustic scattering layers,
but whether they are targeting specific species (e.g., squid vs. myctophids) is unknown [25]. The
ability to sample at the same scales as the whales is fundamental to our understanding of predatorprey interactions and the bioenergetics of marine mammals.
While mesopelagic ecosystems are historically under-sampled, their inhabitants lend themselvesto effective characterization with acoustical techniques [e.g. 26-28], and potentially with opticaltechniques. In fact, mesopelagic organisms comprise one of the most “visible” features of the open
ocean, the deep scattering layer, observed on ship’s echosounders for many decades [29i]. Layers 10100s of meters thick blanketing large areas are ubiquitously observed [29ii]. These layers often appear
continuous and uniform, an artifact of large acoustic sampling volumes used in shipboard operations.
Characterizing the spatial and temporal scales of distribution, abundance, and species composition ofthese patterns at finer scales is challenging with existing technologies and yet critical to ourunderstanding of the trophic dynamics and ecology of deep-water ecosystems, and potentially to theharvesting of these resources.
A series of recent studies have provided tantalizing insight into the abundance and ecology ofmesopelagic fish using narrowband shipboard echosounders, employing typical fisheries acoustics
frequencies of 18-200 kHz [e.g. 11-30]. Yet, shipboard acoustics suffer from low range penetration at
higher frequencies, limiting the information available on the acoustic frequency response oforganisms; from large acoustic sampling volumes at the large depths, leading to complicatedfrequency responses due to multiple organisms sampled simultaneously; and from uncertainty
introduced due to the proximity of the lowest frequencies (typically 18 and 38 kHz) to the resonant
frequency of gas-bearing mesopelagic organisms [31]. Furthermore, although biomass is often
dominated by various fishes, the proportional contribution to scattering of different organisms iscomplicated by an acoustically diverse community composition, including both swimbladder-bearing
and non-bearing fish (as well as species that transition from gas-bearing to non-bearing as they growand age), crustaceans, and strongly scattering low-biomass taxa (e.g., siphonophores) [32]. To fully
characterize mesopelagic communities using acoustics, high-resolution information is needed on taxa,size, their vertical distribution and any depth-sensitivity to their scattering properties [33]:information best achieved through the concurrent deployment of wide-band acoustic and optics
sensors in a deep towed platform [34]. Thus, while mesopelagic ecosystems have historically beenchallenging to sample with nets, acoustical and optical systems, especially when deployed in
combination, represent a powerful and non-destructive sampling approach, with the remote and highresolution sampling to large ranges offered by acoustics complemented by high-taxonomic resolution
afforded by optical approaches.
As coastal fisheries around the world have declined, industrial fishing has spread seaward anddeeper [35,36]. Deep-sea fisheries, involving species such as orange roughy, oreos, and cardinalfish,operate globally throughout the world’s oceans, mostly targeting stocks on continental slopes andoffshore seamounts. Studies addressing the sustainability of deep-sea fisheries [37] have found that
few are sustainable [38]. Most deepwater stocks are overfished or even depleted [39], with unknown
impacts on prey and predator populations, including mesopelagic fishes. Yet mesopelagic fishes arethought to be critical in determining the sustainability of deep-sea fisheries [4]. From a fisheries
management perspective, mesopelagic and bathypelagic fishes are seen as an underutilized (aka latent)
resource. With advances in gear technology, the capability to harvest these resources in an
economically feasible way is not far in the future, and there is a fundamental need to acquire even themost basic information on these ecosystems before they become an economic resource.
In recent years, in response to the increasing demands placed on our oceans, NOAA has beenworking to advance an ecosystem-based approach to management (EBM). The importance ofmesopelagic (and bathypelagic) organisms to deep-sea fisheries, and the US implementation of EBM,
has been recently recognized [40,41]. It is important to understanding the structure and function ofthese ecosystems [4,42] to bolster our ability to predict how these communities will respond to fishingefforts and environmental changes brought on by climate change [43].
A wide-band acoustic scattering and two-scale optical imaging system, integrated with a suite ofenvironmental sensors, including an environmental DNA (e-DNA) sampler [44], has been developedat WHOI that addresses the technological void for characterizing mesopelagic ecosystems. Thissystem capitalizes on broadband acoustical approaches with acoustic systems spanning 5-450 kHz

almost continuously, simultaneously providing “full spectrum” information on zooplankton andmicronekton, with the lower frequencies optimized for resonance classification of gas-bearingorganism, such as myctophids and siphonophores, and the higher frequencies optimized for spectralclassification of organism without gas-inclusions, such as euphausiids and salps. The two-scale
imaging system provides images of these organisms over a larger 1-m2 area for taxa/species-levelclassification as well as behavioral observation, including imaging larger and mobile micronekton,
and will ground-truth the synoptic acoustic data. A holographic camera images smaller organism,
spanning 50 m – 2cm. These acoustical and optical systems are integrated into a towed platform
depth-rated to 2,000 m and enabling high-band-width data telemetry and real-time control. This
instrument platform has been deployed in a series of oceanographic expeditions in the New England
shelf break and slope waters, focused on quantifying the biomass and distribution of zooplankton andmicronekton, particularly in relation to environmental variability, such as meso-scale oceanic eddies,
fronts, and upwelling regions, and abrupt topography.

Figure 1. A typical series of dives for the Deep-See towed platform superimposed on the shipboard 18 kHz
narrowband acoustic echosounder data during a summer 2019 cruise to the New England Slope Waters. A
schematic of the bioacoustics mooring is also shown for reference.

2. PLATFORMS
A wide-band acoustic backscattering and a two-scale optical imaging towed platform has beendeveloped at WHOI for assessing the distribution and abundance of zooplankton and micronekton in
the mesopelagic zone. Although these under-sampled deep regions are the primary focus of this effort,
necessitating the large depth-rating (2000 m), the system will be equally useable for epipelagic (0200m; a region into which many mesopelagic organisms migrate on a diel basis) and shelf breakapplications. The entire system is towed with the wide-band acoustic system downward looking andthe optical imaging system forward looking, to mimic a net sampling system. The optical componentof the system complements the acoustical component of the system by allowing individuals to be
identified to the taxa/species level, as well as providing information on their size and angle oforientation, aiding with the interpretation of the acoustic returns. The environmental sensors provide
vital data concerning the water properties, including temperature and salinity. An eDNA samplingsystem allows genetic fingerprints of mesopelagic organisms to be obtained.
The towed instrument platform, named “Deep-See”, has been deployed in a series of oceanographic
expeditions aimed at understanding the New England shelf break and slope waters in water depths
spanning 400 m to 3000m. Fundamental to the design is the integration of the diverse sensors, whichallows for simultaneous and accurate characterization of multiple trophic levels. Acoustics alone
provides synoptic information, but not at the taxa/species level. Optical imaging, while sampling asmaller volume, provides taxa/species level information. Integrating these two technologies with

environmental sensors provides synoptic taxa/species data correlated with environmental parameters.
Pivotal to the acoustic design is the wide range of frequencies (5-450 kHz). Commercially available
systems are either optimized for characterization of the frequency responses of zooplankton or fish,
but not both. The Deep-See frequencies span a wider range than any commercial system and are
optimized for both fish (lower frequencies) and zooplankton (higher frequencies). Also crucial is theuse of broadband technology resulting in continuous frequency coverage. The range of frequencies,
coupled with use of broadband technology and associated spectral classification, significantly
improves the accuracy of characterizing fish and zooplankton over any system commerciallyavailable. Another key element is the design of the optical imaging system, simultaneously spanningtwo scales to accommodate the wide range of organisms sizes: A larger 1-m2 outer area with lower
resolution for imaging larger organisms more prone to avoidance, such as euphausiids, and a smaller
inner area, 2 cm2, with high resolution (O(50m)) for quantification of smaller organisms, such as
copepods or pteropods.
2.1 Wide-Band (5-450 kHz), Split-Beam Acoustic Scattering Systems
The wide frequency coverage requires two separate acoustic sub-systems. The lower portion of therange is optimized for resonance classification of fish [45,46] and the upper portion of the range is
optimized for spectral classification of zooplankton [47]. Split-beam technology at all frequencies isused to provide direct measurements of target strength from resolved individual fish and zooplankton.
Broadband matched-filter-based signal processing techniques, often referred to in the acoustic
scattering community as compressed pulse processing, are used to significantly improve range
resolution and signal-to-noise ratios [48,49]. Analysis of the echo data involves classification ofswimbladder-bearing fish according to the resonance of their swimbladder (lower frequencies) andclassification of the Rayleigh-to-geometric transition of the non-swimbladder-bearing fish and nongasbearing zooplankton (higher frequencies). Sizes of fish and zooplankton are determined from theseresonances or transition frequencies through scattering models. This spectral classification greatly
improve characterization (size, numerical density, and patchiness) of fish and zooplankton [45-47].

Figure 2. A) The combined acoustical and optical towed instrument platform. B) “Deep-See” being deployed
form the NOAA Ship Henry B. Bigelow. C) Data from the Deep-See Airmar acoustics channel (18-47 kHz) [59]. D)
A reconstructed image of a copepod from the holographic camera. E) An image of a Euphausiid from the LAPIS
camera system.

2.1.1 Mid-Frequency Acoustic Subsystem (5-47 kHz)This component was developed at WHOI as split-beam technology is not commercially available
in the lower frequency range. This subsystem utilizes is built around a highly flexible, user-specified
two-channel EdgeTech sub-bottom profiler system. One channel transmits in the frequency band 5-18
kHz (KT-216D transmitter) and the other interfaces with a custom-designed Airmar source in thefrequency band 18.5-47 kHz. EdgeTech built a split-beam receiver array for this project, using a

PVDF-based receiver array. PVDF (Polyvinylidene Fluoride) is a ferroelectric polymer with uniquecapabilities for the design of acoustic arrays. The split-beam array consists of two concentric circles,
split into 4 quadrants, totaling 8 receive elements. Both the outer (1 m diameter) and inner (0.6 m
diameter) circular receive arrays are used with the 5 - 18 kHz source, and the smaller circular receive
array (inner 4 elements) is used with the 18.5 - 47 kHz transducer. This subsystem was calibratedusing a 20-cm diameter spherical Al standard targets following the techniques outlined in Stanton etal. [50]. The signal processing is performed in MATLAB using data acquired from the Edgetech dataacquisition system and control software, JStar, which allows for real-time data visualization andintegrates GPS/UTC data. The processing involves making a series of phase-coherent operations on
the different receive array elements, which results in information on the location of a resolved scatterer
in the beam, and subsequently converted into target strength.
2.1.2 High- Frequency Acoustic Subsystem (50-450 kHz)The high-frequency subsystem utilizes two Kongsberg-Simrad WBT- Tubes and four broadband,
split-beam, Simrad transdusers. Both WBT-Tubes are controlled in real-time though the same EK80
software from a common topside computer, which integrates shipboard GPS data. This software is
triggered by the mid-frequency EdgeTech electronics for the purpose of accurate synchronization.
Four octave-bandwidth split-beam (4-quadrant) Simrad transducers with center frequencies at 70, 120,
200, and 333 kHz span the 50-450 kHz frequency band with small gaps (Table 1). All post processingof EK80 data is performed in Matlab with software already developed at WHOI. These high-frequency
channels are calibrated using a 38.1mm-diameter Tungsten Carbide spherical standard target
following Lavery et al. [51]. There have been a small number of studies using in-situ high-frequency
broadband acoustic approaches [52-54].
Transducer

Nominal Center
Bandwidth
Full Beamwidth
Pulse Duration
Frequency (kHz) (kHz)(Degrees)(ms)ES70-7CD
70
50-90
7
2
ES120-7CD
120
90-16072ES200-7CD
200
160-2607
2
ES333-7CD
333
280-450
7
2
Table 1. High-frequency, split-beam, Simrad transducers mounted on Deep-See.

2.2 Optical Imaging Systems
A two-scale imaging system on Deep-See allows many zooplankton and micronekton to be
simultaneously imaged. The larger-scale component images a volume large enough to sample thelarger, sparsely distributed, and more mobile organisms such as fish and euphausiids, at the sacrificeof the resolution required to image smaller organisms. The smaller-scale imaging system images amuch smaller volume, but with high enough resolution to image and classify the smaller organisms.
There have been a number of optical imaging approaches and systems developed for identificationand monitoring of small zooplankton [55]. Examples include the 3-D Oasis system [55], theZooplankton Visualization System [56] and the Video Plankton Recorder [57,58]. However, theseplankton-imaging devices sample volumes that are too small to be useful for macrozooplankton andmicronekton, necessitating a large-area imaging system. A single-scale large-area plankton imaging
system (LAPIS [1]) with close to 1 m2 areal coverage was developed over a decade ago and a feasibility
demonstration was successfully completed. Through image analysis and computer “training” forautomatic classification, the size, shape, orientation, behavior, and abundance of zooplankton andsmall fish can be determined in real time [58]. Early deployments of this system showed that it waspossible to: i) Infer abundance of individuals per cubic meter, using flowmeter data to calculate
volume imaged during the tows. ii) Measure animal sizes from the images, based on prior calibrationof the field of view. iii) Identify targets to taxa/species, thus allowing species diversity to be assessed.
iv) Determine orientation and behavior of swimming organisms, including observations that theswimming orientation of salps was consistent with their diel migration (headed up at night, down inearly morning), observations of solitary salps releasing their chains of aggregate offspring in surface
waters at night, quantification of the numbers and positions of hyperiid amphipods on salps and othergelatinous organisms, arrangement of tentacles in feeding positions for medusae, siphonophores, andctenophores, and observations of shock-molting behavior and escape swimming in Euphausia superba
schools. However, the original LAPIS system operated over a single larger scale at the sacrifice ofresolution at the smaller scale. Furthermore, significant advances in camera and LED-technology has
rendered the strobe-based LAPIS obsolete.
A new system has been developed, extending and updating the LAPIS design previously
developed at WHOI [1]. The development includes a 24-MPixel camera imaging an area of 1m2 (1mx 1m) so that the larger organisms can be imaged at lower resolution and a holographic imaging systemwith an imaging volume of 1m x 2cm x 2cm so that the smaller organisms can be imaged at higher
resolution (approximately 50m). With the two cameras mounted side-by-side, the smaller imaging
area is within the boundaries of the larger area, providing some overlapping information. The larger
area system is an increase of nearly a factor of four in resolution over the original LAPIS system.
Critical to the design of this system is the optimization of the lighting and camera positions, as wellas the forward-looking towed approach, which, using flowmeter data to calculate volume imaged
during the tows, results in data comparable to abundance data from net tows, except with far greater
resolution of spatial patchiness, and more accurate quantification of fragile organisms. Off-the-shelf
hardware includes cameras, zoom lens, and LED-strobes. Control is executed by ship-board pc’s
through the fiber-optic link, and integrates ship-board GPS.
2.3 Environmental Sensing Systems and Towed Platform
Temperature, conductivity, and depth (CTD) are obtained using a Seabird 49 FastCat CTD, in
addition to obtaining depth/roll/pitch of the towed platform in the power and telemetry underwater
unit. A 500-kHz M3 Multibeam is also available, either pointing forward to monitor avoidance, or
downward looking. An environmental eDNA sampler with 16 separately pumped samples is also
integrated to the Deep-See towed platform to allow genetic fingerprinting of mesopelagic organisms.
All sensors are deployed on the single towed platform to collect simultaneous, co-located data. The
optics, high-frequency acoustics, and environmental sensors especially must be at or near the depthsof the organisms. Deep-See is flooded platform towed at speeds between 2-3 kts. The oil-filled
junction box (J-Box) is a key part of the system, providing power and control/data telemetry to all
systems from the tow cable. Deep-See is towed by the industry standard 0.681" diameter cable withthree layers of outer strength windings, three inner electrical conductors (for sensor power) and three
single mode optical fibers (ethernet control of sensors and data transfer up to 5 Gb/s). The sensors arecontrolled remotely from control computers in the control lab on the deck of the ship. Data fromindividual sensors is transmitted to specific computers and all data have a common timestamp (ship’sGPS).
2.4 Bioacoustics Mooring
To complement the measurements performed with the integrated towed sensor platform Deep-See,a bioacoustics mooring (Fig. 3) was deployed in the New England slope water in 2784 m water depth.
The mooring allowed persistent measurements of acoustic backscattering in order to assess theimportance of season variability in abundance, distribution, and biomass, as well as the impact of
meso-scale physical drivers such as warm-core rings. The mooring consists of an anchor weight, an
acoustic release, and a flotation sphere with the Simrad WBAT and broadband 3-sector transducer(ES38-18CDK, bandwidth ~32-43 kHz). The mooring has no surface expression and the float with thebioacoustics sensors was located at approximately 600m depth, upward looking. The autonomous
acoustic system collects data for 5 min every 30 minutes in a 2-week cycle.

3. RESULTS
The Deep-See platform has been deployed in New England slope waters to study the OTZ in 27003000 m water depth in summer 2018, summer 2019, and summer 2022 from the NOAA Ship Henry
B. Bigelow. Published results can be found in references [59-62] and a summary is shown in Figures
1 and 2. The autonomous bioacoustics mooring was deployed from July 2021-March 2022, and June
2022 – present, also in the New England Slope Waters (38 59.1258 N, 70 12. 1387 W, Water Depth2785). A summary of results is shown in Figure 3.

Figure 3. Bioacoustic Mooring: A broadband split-beam echosounder integrated onto a float with CTD at 600m
allows the Target Strength of individual fishes in the Deep Sound Scattering Layer (DSL) to be measured in situ, aswell monitoring the temporal changes in the Daily Vertical Migration of fishes up from the Deep Sound Scattering
layer during the day to the surface waters at night. This allows season changes in the DSL to be understood, as wellas assessing the impact of mesoscale physical drivers, such as warm core rings. A) The mooring schematic is shown
to the right, together with a picture of the deployment from the R/V Neil Armstrong, B) Target Strength Spectra ofindividual fishes in the Deep Sound Scattering Layer, and C) Acoustic Volume Scattering (dB) over 2 week period
from 8-21 August 2021, showing the DVM of fishes. The bioacoustics mooring was deployed from July 2021 toMarch 2022, and again from June 2022- present.

ACKNOWLEDGEMENTS
The author would like to acknowledge the vast contributions to this work from a large team ofpeople, including WHOI engineers Robert Petitt and Kaitlyn Tradd, MIT-WHOI Joint Program
graduate student Zhoazhong Zhuang, WHOI Postdoctoral Scholar Emma Cotter, WHOI Postdoctoral
Investigator Scott Loranger, WHOI co-PIs Peter Wiebe, Tim Stanton, and Annette Frese-Govindarajan,
Mike Jech at NOAA Northeast Fisheries Science Center, the crew on the NOAA Ship Henry B.
Bigelow and the R/V Neil Armstrong, and the WHOI Moorings Group, especially John Kemp and Jim
Dunn. This work was funded in part by a National Science Foundation Major ResearchInstrumentation Grant and in part by the WHOI Ocean Twilight Zone Project funded as part of The
Audacious Project housed at TED.

REFERENCES
1. Madin, L.,Horgan, E., Gallager, S., Eaton, J., and Girard, A., 2006. LAPIS: A new imaging tool
for macro-zooplankton. IEEE Oceans 2006: 1-5. 10.1109/OCEANS.2006.307106
2. Kaartvedt, S., Staby, A. and Aksnes, D.L., 2012. Efficient trawl avoidance by mesopelagic fishes
causes large underestimation of their biomass. Marine Ecology Progress Series, 456: 1-6.
3. Wiebe, P.H., G.L. Lawson, A.C. Lavery, N.J. Copley, E. Horgan, and A. Bradley. 2013. Improved
agreement of net and acoustical methods for surveying euphausiids by mitigating avoidance using
a net based LED strobe light system. ICES Journal of Marine Science, 70(3): 650-664.
4. Sutton, T.T., 2013. Vertical ecology of the pelagic ocean: classical patterns and new perspectives.
Journal of Fish Biology, 83(6): 1508-1527.
5. Gjøsaeter, J. and Kawaguchi, K., 1980. A review of the world resources of mesopelagic fish. Vol.
193 (Bernan Press).
6. Lam, V. and Pauly, D., 2005. Mapping the global biomass of mesopelagic fishes. Sea Around Us
Project Newsletter 30: 4.
7. Christensen, V., Walters, C.J., Ahrens, R., Alder, J., Buszowski, J., Christensen, L.B., Cheung,
W.W., Dunne, J., Froese, R., Karpouzi, V. and Kaschner, K., 2009. Database-driven models of theworld's Large Marine Ecosystems. Ecological Modelling, 220(17): 1984-1996.
8. Kock, K.H., 1992. Antarctic fish and fisheries. Cambridge University Press.
9. Pusch, C., Hulley, P.A. and Kock, K.H., 2004. Community structure and feeding ecology ofmesopelagic fishes in the slope waters of King George Island (South Shetland Islands, Antarctica).
Deep Sea Research Part I, 51(11): 1685-1708.
10. Nelson, J. S., 2006. Fishes of the World (Wiley, 2006).
11. Irigoien, X., Klevjer, T.A., Røstad, A., Martinez, U., Boyra, G., Acuña, J.L., Bode, A., Echevarria,
F. Gonzalez-Gordillo, J.I., Hernandez-Leon, S. and Agusti, S., 2014. Large mesopelagic fishes
biomass and trophic efficiency in the open ocean. Nature communications, 5.
12. Saito, H. and Murata, M., 1998. Origin of the monoene fats in the lipid of midwater fishes:
relationship between the lipids of myctophids and those of their prey. Marine Ecology Progress
Series, 168: 21-33.
13. Lancraft, T.M., Torres, J.J. and Hopkins, T.L., 1989. Micronekton and macrozooplankton in the
open waters near Antarctic ice edge zones (AMERIEZ 1983 and 1986). Polar Biology, 9(4): 225233.
14. Bulman, C.M., He, X. and Koslow, J.A., 2002. Trophic ecology of the mid-slope demersal fish
community off southern Tasmania, Australia. Marine and Freshwater Research, 53(1): 59-72.
15. Overholtz, W.J., Link, J.S. and Suslowicz, L.E., 2000. Consumption of important pelagic fish andsquid by predatory fish in the northeastern USA shelf ecosystem with some fishery comparisons.
ICES Journal of Marine Science, 57(4): 1147-1159.
16. McClatchie, S. and Dunford, A., 2003. Estimated biomass of vertically migrating mesopelagic
fish off New Zealand. Deep Sea Research Part I, 50(10): 1263-1281.
17. McClatchie, S., Pinkerton, M. and Livingston, M.E., 2005. Relating the distribution of a semidemersal fish, Macruronus novaezelandiae, to their pelagic food supply. Deep Sea Research Part
I: Oceanographic Research Papers, 52(8): 1489-1501.
18. Guinet, C., Cherel, Y., Ridoux, V. and Jouventin, P., 1996. Consumption of marine resources byseabirds and seals in Crozet and Kerguelen waters: changes in relation to consumer biomass 1962–
85. Antarctic Science, 8: 23-30.
19. Cherel, Y., Guinet, C. and Tremblay, Y., 1997. Fish prey of Antarctic fur seals Arctocephalus
gazella at Ile de Croy, Kerguelen. Polar Biology, 17(1): 87-90.
20. Rodhouse, P.G., Arnbom, T.R., Fedak, M.A., Yeatman, J. and Murray, A.W.A., 1992. Cephalopod
prey of the southern elephant seal, Mirounga leonina L. Canadian Journal of Zoology, 70(5):1007- 1015.
21. Phillips, K.L., Jackson, G.D. and Nichols, P.D., 2001. Predation on myctophids by the squid
Moroteuthisingens around Macquarie and Heard Islands: stomach contents and fatty acid analyses.
Marine Ecology Progress Series, 215: 179-189.
22. Azzara, A.J., Thode, A. Norris, T., Benoit-Bird, K., and Biggs, D.C., 2006. Characteristics of the
deep scattering layer in the Gulf of Mexico as they relate to sperm whale diving and foraging
behavior. Journal of the Acoustical Society of America, 120: 3013.
23. Wenzel, F.W., Polloni, P.T., Craddock, J.E., Gannon, D.P., Nicolas, J.R., Read, A.J. and Rosel,
P.E., 2013. Food habits of Sowerby’s beaked whales (Mesoplodon bidens) taken in the pelagic
drift gillnet fishery of the western North Atlantic. Fisheries Bulletin.
24. Arranz P., de Soto, N.A., Madsen, P.T., Brito, A., Bordes, F., Johnson, M.P., 2011. Following a
Foraging Fish-Finder: Diel Habitat Use of Blainville's Beaked Whales Revealed by Echolocation.
PLoS ONE, 6(12): e28353. doi:10.1371/journal.pone.0028353
25. Jones, B.A., Stanton, T.K., Lavery, A.C., Johnson, M.P., Madsen, P.T. and Tyack, P.L., 2008.
Classification of broadband echoes from prey of a foraging Blainville’s beaked whale. Journal of
the Acoustical Society of America, 123(3): 1753-1762.
26. Gorska, N., Ona, E. and Korneliussen, R., 2005. Acoustic backscattering by Atlantic mackerel as
being representative of fish that lack a swimbladder. Backscattering by individual fish. ICES
Journal of Marine Science, 62(5): 984-995.
27. Forland, T.N., Hobæk, H. and Korneliussen, R.J., 2014a. Scattering properties of Atlantic
mackerel over a wide frequency range. ICES Journal of Marine Science, 71(7): 1904-1912.
28. Scoulding, B., Chu, D., Ona, E. and Fernandes, P.G., 2015. Target strengths of two abundant
mesopelagic fish species. Journal of the Acoustical Society of America, 137(2): 989-1000.
29. i) Hersey, J. B. and Backus, R. H., 1962. Sound scattering by marine organisms. The Sea, 1:498–
539. ii) Klevjer, T.A., Irigoien, X., Røstad, A., Fraile-Nuez, E. and Benítez-Barrios, V.M., 2016.

Large scale patterns in vertical distribution and behaviour of mesopelagic scattering
layers. Scientific Reports, 6(1), pp. 1-11.
30. Davison, P.C., Koslow, J.A. and Kloser, R.J., 2015a. Acoustic biomass estimation of mesopelagic
fish: backscattering from individuals, populations, and communities. ICES Journal of Marine
Science, p.fsv023.
31. Davison, P., Lara-Lopez, A., and Koslow, J. A. 2015b. Mesopelagic fish biomass in the southern
California Current ecosystem. Deep Sea Research II, 112: 129–142.
32. Proud, R., Handegard, N.O., Kloser, R.J., Cox, M.J. and Brierley, A.S., 2019. From siphonophores
to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fishbiomass. ICES Journal of Marine Science, 76(3), pp.718-733.
33. Thompson, C. H. and Love, R. H. 1996. Determination of fish size distributions and areal densities
using broadband low-frequency measurements. ICES Journal of Marine Science, 53: 197–201.
34. Kloser, R. J., Macaulay, G. J., Ryan, T. E., and Lewis, M., 2013. Identification and target strengthof orange roughy (Hoplostethus atlanticus) measured in situ. Journal of the Acoustical Society ofAmerica, 134: 97–108.
35. Norse, E.A., Brooke, S., Cheung, W.W., Clark, M.R., Ekeland, I., Froese, R., Gjerde, K.M.,
Haedrich, R.L., Heppell, S.S., Morato, T. and Morgan, L.E., 2012. Sustainability of deep-sea
fisheries. Marine Policy, 36(2): 307-320.
36. Watson, R.A. and Morato, T., 2013. Fishing down the deep: Accounting for within-specieschanges in depth of fishing. Fisheries Research, 140: 63-65.
37. Pauly, D., Christensen, V., Guénette, S., Pitcher, T.J., Sumaila, U.R., Walters, C.J., Watson, R. andZeller, D., 2002. Towards sustainability in world fisheries. Nature, 418(6898): 689-695.
38. Clark, M.R., Althaus, F., Schlacher, T.A., Williams, A., Bowden, D.A. and Rowden, A.A., 2015.
The impacts of deep-sea fisheries on benthic communities: a review. ICES Journal of Marine
Science: Journal du Conseil, p.fsv123.
39. Koslow, J., Boehlert, G.W., Gordon, J.D.M., Haedrich, R.L., Lorance, P. and Parin, N., 2000.
Continental slope and deep-sea fisheries: implications for a fragile ecosystem. ICES Journal of
Marine Science, 57(3): 548-557.
40. Levin, P.S., Kelble, C.R., Shuford, R.L., Ainsworth, C., Dunsmore, R., Fogarty, M.J., Holsman,K., Howell, E.A., Monaco, M.E., Oakes, S.A. and Werner, F., 2013. Guidance for implementationof integrated ecosystem assessments: a US perspective. ICES Journal of Marine Science p.fst112.
41. ICES-FAO Reportftp://ftp.fao.org/Fi/DOCUMENT/rebyc/ices/WGFTFB08.pdf
42. Sutton, T.T., Porteiro, F.M., Heino, M., Byrkjedal, I., Langhelle, G., Anderson, C.I.H., Horne, J.,
Søiland, H., Falkenhaug, T., Godø, O.R. and Bergstad, O.A., 2008. Vertical structure, biomass andtopographic association of deep-pelagic fishes in relation to a mid-ocean ridge system. Deep Sea
Research Part II, 55(1): 161-184.
43. Hays, G.C., Richardson, A.J. and Robinson, C., 2005. Climate change and marine plankton.
Trends in Ecology and Evolution, 20(6): 337-344.
44. Govindarajan, A.F., Francolini, R.D., Jech, J.M., Lavery, A.C., Llopiz, J.K., Wiebe, P.H. andZhang, W., 2021. Exploring the use of environmental DNA (eDNA) to detect animal taxa in themesopelagic zone. Frontiers in Ecology and Evolution, 9, p. 574877.
45. Stanton, T.K., Chu, D., Jech, J.M. and Irish, J.D., 2010. New broadband methods for resonanceclassification and high-resolution imagery of fish with swimbladders using a modified commercial
broadband echosounder. ICES Journal of Marine Science, 67(2): 365-378.
46. Stanton, T., Sellers, C., and Jech, J., 2012. Resonance classification of mixed assemblages of fishwith swimbladders using a modified commercial broadband acoustic echosounder at 1–6 kHz.
Canadian Journal of Fisheries and Aquatic Sciences, 69: 854–868.
47. Lavery, A.C., Chu, D. and Moum, J.N., 2010a. Measurements of acoustic scattering fromzooplankton and oceanic microstructure using a broadband echosounder. ICES Journal of Marine
Science 67 (2): 379-394.
48. Chu, D., and Stanton, T. K. 1998. Application of pulse compression techniques to broadband
acoustic scattering by live individual zooplankton. Journal Acoustical Society of America, 104:
39–55.
49. Ehrenberg, J.E. and Torkelson, T.C., 2000. FM slide (chirp) signals: a technique for significantly
improving the signal-to-noise performance in hydroacoustic assessment systems. Fisheries
Research, 47(2): 193-199.
50. Stanton, T.K. and Chu, D., 2008. Calibration of broadband active acoustic systems using a single

standard spherical target. Journal of the Acoustical Society of America, 124(1): 128-136.
51. Lavery, A.C., Bassett, C., Lawson, G.L. and Jech, J.M., 2017. Exploiting signal processing
approaches for broadband echosounders. ICES Journal of Marine Science, 74(8), pp.2262-2275.
52. Agersted, M.D., Khodabandeloo, B., Liu, Y., Melle, W. and Klevjer, T.A., 2021. Application of an
unsupervised clustering algorithm on in situ broadband acoustic data to identify different
mesopelagic target types. ICES Journal of Marine Science, 78(8), pp. 2907-2921.
53. Khodabandeloo, B., Agersted, M.D., Klevjer, T.A., Pedersen, G. and Melle, W., 2021.
Mesopelagic flesh shear viscosity estimation from in situ broadband backscattering measurements
by a viscous–elastic model inversion. ICES Journal of Marine Science, 78(9), pp. 3147-3161.
54. Khodabandeloo, B., Ona, E., Pedersen, G., Korneliussen, R., Melle, W. and Klevjer, T., 2022.
Mesopelagic fish gas bladder elongation, as estimated from wideband acoustic backscattering
measurements. The Journal of the Acoustical Society of America, 151(6), pp. 4073-408.
55. Jaffe, J.S., Moore, K.D., McLean, J. and Strand, M.P., 2001. Underwater optical imaging: status
and prospects. Oceanography, 14(3): 66-76.
56. Benfield, M.C., Schwehm, C.J., Fredericks, R.G., Squyres, G., Keenan, S.F., and Trevorrow, M.V.
2004. Measurement of zooplankton distributions with a high-resolution digital camera system. In,
L. Seuront and P. Strutton (eds.). Scales in Aquatic Ecology: Measurement, Analysis andSimulation. CRC Press, pp. 17-30.
57. Davis, C. S., Gallager, S. M., Berman, M. S., Haury, L. R., and Strickler, J. R. 1992. The video
plankton recorder (VPR): design and initial results. Archiv fuer Hydrobiologie, Beiheft:
Ergebnisse der Limnologie, 36: 67–81.
58. Davis, C.S., Thwaites, F.T., Gallager, S.M., and Hu, Q. 2005. A three-axis fast-tow digital Video
Plankton Recorder for rapid surveys of plankton taxa and hydrography. Limnology andOceanography, 3: 59-74.
59. Cotter, E., Bassett, C. and Lavery, A., 2021. Classification of broadband target spectra in themesopelagic using physics-informed machine learning. The Journal of the Acoustical Society ofAmerica, 149(6), pp. 3889-3901.
60. Bassett, C., Lavery, A.C., Stanton, T.K. and Cotter, E.D., 2020. Frequency-and depth-dependent
target strength measurements of individual mesopelagic scatterers. The Journal of the AcousticalSociety of America, 148(2), pp. EL153-EL158,
61. Cotter, E., Bassett, C. and Lavery, A., 2021. Comparison of mesopelagic organism abundance
estimates using in situ target strength measurements and echo-counting techniques. JASA Express
Letters, 1(4), p. 040801.
62. Cotter, E., Fischell, E. and Lavery, A., 2021. Computationally efficient processing of in situ
underwater digital holograms. Limnology and Oceanography: Methods, 19(7), pp.476-487.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022 in Gyeongju, Korea

ABS-0441

Preliminary inversions using compressional, shear and interface
waves in the New England Mudpatch
Gopu R. Potty1; James H. Miller1, Julien Bonnel2; Ying-Tsong Lin2, David P. Knobles3, Preston S.
Wilson412

Department of Ocean Engineering, University of Rhode Island, Narragansett, RI 02882, USA

Applied Ocean Physics and Eng., Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
3

Knobles Scientific and Analysis, Austin, Texas 78755, USA
4

University of Texas at Austin, Austin, TX 78713, USA

ABSTRACTThe properties of ocean sediments can significantly impact the acoustic propagation in shallow water. To
understand the effect of fine-grained sediments on acoustic propagation experiments were conducted in 2017and 2022 at the New England Mud Patch. The mud patch is a 13,000 square kilometer area covered by alayer of silty mud sediments. This sediment layer is the only area on the entire eastern United States outer
shelf where surficial sediments contain more than 30 percent silt plus clay. Hence this area offers a uniquelocation to apply the inversion techniques to investigate the geoacoustic properties of fine-grained sediments.
The receiver systems deployed during these experiments include a tetrahedral array of hydrophones and a 3axis geophone and Ocean Bottom Recorders (OBXs). The geophones and OBXs measure particle velocity
in three mutually perpendicular directions and acoustic pressure. Scholte waves on the mud-sand interface
were generated by a bottom-mounted transducer. Measurements of SUS signals on the hydrophones anddirectional sensors will be used to estimate the sediment geoacoustic properties. Preliminary inversions fromthe 2022 experiment will be compared to previous inversions for the same location. [Work sponsored by
Office of Naval Research, Code 322OA].
Keywords: inversion, SUS, dispersion, particle velocity

1. Introduction
The properties of ocean sediments can significantly impact the acoustic propagation in shallow
water. To understand the effect of fine-grained sediments on acoustic propagation experiments wereconducted in 2017 and 2022 at the New England Mud Patch (NEMP) off the United States East Coast(1). The mud patch is a 13,000 square kilometer area covered by a layer of silty mud sediments. This
sediment layer is the only area on the entire eastern United States outer shelf where surficial sediments
contain more than 30 percent silt plus clay (2). Hence this area offers a unique location to apply the
inversion techniques to investigate the geoacoustic properties of fine-grained sediments.
Data collected during the 2017 Seabed Characterization Experiment (SBCEX-2017) triggered large
number of studies and subsequent publications (1, 3, 4). In order to augment this understanding, the2022 experiment collected more data in the same experimental area. The ongoing data analysis andinversions builds on the understanding gained based on the 2017 experiment. This article highlights
some of the data collected and analyzed and presents preliminary results. Specifically, data measured
from broadband SUS sources are analyzed in this study. The receiver systems deployed during theseexperiments include a tetrahedral array of hydrophones and a 3-axis geophone and Ocean Bottom
Recorders (OBXs). The geophones and OBXs measure particle velocity in three mutually1

gpotty@uri.edu

perpendicular directions and acoustic pressure. This study focuses on measurements of SUS signals
on one of the OBXs, which includes data from a hydrophone and 3-axis geophone.

2. Broadband SUS data and modal dispersion
The locations of the SUS charge and the OBX receiver are shown in figure 1 (panel a). Based on
the deployment locations, the range was calculated as 9.95 km. The source to receiver propagation
path is oriented at 1640 (measured anti-clockwise from the east direction) as shown in figure 1 (panela).

Figure 1 – Locations of the source (SUS) and OBX receiver (panel a). The propagation path is oriented
1640 measured anti-clockwise from east. The OBX receiver is shown in panel b. The orientations of theinline, cross line and vertical (marked X, Y and Z) axes are also shown in the panel. The azimuth angle (𝜃)and the elevation angle (𝜙) are defined in panel c.
The OBX is a self-contained sensor package consisting of a 3-axis geophone and a co-locatedhydrophone along with heading sensor, accurate clock, battery and data storage. The OBX measures
particle velocity along three mutually perpendicular directions (inline, cross line and vertical – marked
as X, Y and Z in figure 1 (panel b)). The OBX data can be used to find the azimuth and elevation
angles (as defined in the panel c in figure 1) of the direction of arrival (DOA) of the signal. Date were
measured on the OBX at a sampling frequency of 4 kHz.
2.1 OBX data
The broadband data measured on the OBX located 9.75 km from the source is shown in figure 2.
The figure shows the vertical, inline, cross line velocity components (in mm/s) and the pressure timeseries (in Pa) from the hydrophone channel. The OBX was deployed on the seabed and position shownin the figure is the triangulated location after deployment. The OBX has a heading sensor and theheading measured on that sensor was 2600 (clockwise from N). This heading can be validated by
calculating the DOA from the source and the known source position.

Figure 2 – Broadband data from the OBX. The four subplots (top to bottom) show the vertical, inline andcross line velocity components. The bottom subplot shows the hydrophone data. Note that the inline
component is stronger (~ 5 times) compared to vertical and cross line components.
2.2 Modal dispersion
The arrivals corresponding to the different normal modes can be observed in the time-frequency
representations of the broadband acoustic signals shown in figure 2. Figure 3 shows the modal
dispersion diagrams of the velocity components and the acoustic pressure.

Figure 3 – Time-frequency diagrams of the data shown in figure 2 showing the dispersed arrivals of thenormal modes. The four panels correspond to the vertical, inline and cross line components of the velocity
and the hydrophone data. Note that inline component is stronger (~ 5 times) compared to vertical and crossline components.

Note that the color scale for the inline component is different from the vertical and crossline
components since it comparatively stronger. All of the velocity components and pressure data show
the presence of precursor arrivals and strong dispersed arrivals (~1 second long) corresponding to thefirst four normal modes. The Airy phase (group speed minimum) can be clearly identified in thepressure and the inline velocity component. The velocity components can be used to calculate the
DOA and a time-frequency representation (called ‘azigram’) can be constructed to identify the
direction of acoustic propagation.
2.3 DOA: Azigram
As mentioned earlier the ‘azigram’ provides a visualization of the evolution of DOA as a functionof time and frequency. The DOA of the arrivals is calculated using the velocity components as definedin figure 1 (panel c). The time series is divided into a number of segments and the azimuth andelevation angles are calculated as a function of frequency using equation 1. The azimuth calculated
as a function of time and frequency is shown in figure 3 (bottom panel). The top panel in figure 3
shows the azimuth at 101 Hz.

Figure 3 – Time-frequency visualization of the DOA (azimuth) of the acoustic arrivals shown in figure 2.
The color indicates the azimuth in degrees as mapped according to the color bar. Top panel shows theazimuth at a single frequency (101 Hz). The source receiver positions are shown in figure 1 (panel a).
The active intensity measures the in-phase product of pressure (P) and velocity (𝑣! , 𝑣" ) whichindicates the portion of the acoustic field that is actively transporting acoustic energy through themeasurement point along the direction of the velocity vector. The azigram A(n,m) is defined as (5),%! (',))𝐴(𝑛, 𝑚) = 𝑡𝑎𝑛#$ .% (',))/"(1)

Azigram represents the dominant azimuth from which acoustic energy at time chunk n andfrequency index m is propagating along the horizontal plane. The active intensity along the x and y
direction in equation 1 is defined as shown in equation 2,
𝐼! = 𝑅𝑒{𝑃(𝑛, 𝑚)𝑉!∗ (𝑛, 𝑚)} and 𝐼" = 𝑅𝑒7𝑃(𝑛, 𝑚)𝑉"∗ (𝑛, 𝑚)8(2)

In figure (3) the azimuth is shown only for the times and frequencies at which the active intensity
component is dominant. This is done by calculating the Normalized Transport Velocity (NTV) andusing that to identify the active part of the acoustic arrivals.

2.4

Normalized Transport Velocity (NTV).

The Normalized Transport Velocity (NTV) is defined as the ratio of the active intensity magnitude
to the energy density (5) as shown in equation 3.
𝑁𝑇𝑉 = $〈-.%"# /%!#0〉32$
0〈6# 〉
4 〈-.5"# /5!# 0〉/# %#&% '#(3)

The magnitude of NTV should lie between 0 and 1; with 1 corresponding to propagating plane
wave and 0 corresponding to standing wave. After calculating the azigram using equation 1, the values
corresponding to NTV <0.5 has been removed (white areas in figure 3) which preserves the
propagating part of the wavefield and removes the rest of the wave types.
The azimuth values shown in figure 3 indicates that the direction of propagation of the acoustic
energy corresponding to the broadband source is along the inline axis (X direction) of the OBX(azimuth measured from X axis ~ 0). A slice of the azigram along 101 Hz (dashed line) confirms thisas shown in the upper panel in figure 3.

3. Preliminary estimation of geoacoustic model using H/V ratio method
Horizontal to vertical particle velocity ratio (H/V) method (6) calculates the spectral ratio between
the horizontal and vertical components of particle velocity measured by a single particle motion sensor.
The ratio between the horizontal and vertical axes of the ellipse of particle motion is called ellipticity.
The H/V curve exhibits a single peak corresponding to the resonance frequency of the site. For asensor on the seabed, it is widely accepted that the resonance frequency is linked to a peak in interface
wave ellipticity for sites with a large shear-wave impedance contrast between the surficial sedimentlayers and the underlying bedrock [7]. If the site has multiple layers of sediment, the H/V curve may
exhibit multiple peaks. The principle of the method is to calculate the ratio of the horizontal (vx , vy )and vertical (vz) Fourier spectra of ambient seismic vibration recordings of a single three-component
seismic sensor. The natural frequency (fR ) of a homogeneous sediment layer of thickness (d) and shear
speed (Vs) is approximately given by equation 4.
"

𝑓! = #$!

(4)

When the peak frequency is estimated from the H/V curve, the shear speed or the depth to bedrock(depth to impedance mismatch in the case of multiple peaks) can be calculated knowing one of them.
Ambient noise data measured on the OBX was used to calculate the H/V ratio and identify theresonances. Since this is a passive technique, an algorithm used extensively in the geophysicalcommunity (RayDec algorithm) was used to identify and confirm the presence of Scholte waves. The
H/V ratio is then computed from the data using the RayDec algorithm (8) and is shown in figure 4(black curve). The RayDec algorithm is based on the random decrement technique, which is commonlyused to characterize the dynamic parameters, i.e., resonance frequency and damping. This technique
isolates the Scholte wave arrivals in the data and uses that to calculate the H/V ratio. The RayDec
algorithm is described in detail in [8].
The RayDec algorithm also finds out the DOA of Scholte wave arrivals by searching the data (thevertical and horizontal components of velocity) at various times and frequencies and identifying the
900 phase shift between them (vertical and horizontal components) which is characteristics of the
Scholte waves. Right panel in figure 4 shows the frequencies dominated by Scholte waves in theambient noise data analyzed and the associated azimuths. The azimuth corresponding to the Scholte
wave arrivals seems to be 3000 which is nearly opposite to the direction of the SUS data propagation
direction. Data from this frequency band identified as Scholte wave arrivals are used to calculate the
H/V ratio as described earlier.
The H/V ratio can also be predicted using modeled acoustic particle motion in the horizontal andvertical directions. The green curve in figure 4 (left panel) is the H/V ratio calculated using predictedparticle velocities. The particle motions were predicted using Lai-Rix model (9). The inputs to thismodel are the bottom parameters such as the shear and compressional wave speeds and densities invarious layers of sediments, and the layer thicknesses. The model output shown in figure 4 is basedon a geoacoustic model as shown in figure 4 (middle panel). The multiple resonant peaks in the data
indicates a number of layers of sediment (both in mud and sand depositional packages). The shear
speeds in the mud were 25 m/s (0 to 3 m) and 40 m/s (3 to 6 m). Shear speeds in the sand layer below
the mud increased from 200 m/s to 250 m/s (in three steps) over a layer thickness of 20 m. The shear
speed in the basement was 500 m/s. The compressional wave speeds in the were assumed as 1450 m/s
and 1520 m/s in the two mud layers, and in the as 1700 m/s, 1700 m/s and 1750 m/s respectively inthe three sand layers. The compressional wave speed in the basement was input as 1800 m/s. Densities
varied from 1.65 kg/m3 to 2 kg/m3 in the mud and sand layers.

Figure 4 – The ellipticity (H/V ratio) calculated for the ambient noise data using the RayDec algorithm(black curve). The green curve in the left panel is calculated using predicted Scholte wave motion for abottom model shown in the middle panel. The right panel shows the frequency band where the Scholte
wave arrivals are dominant, and the color corresponds to the azimuth in degrees.

4. CONCLUSIONSPreliminary analysis of acoustic data measured during the Seabed Characterization Experiment ispresented in this paper. The broadband acoustic pressure and particle velocity data show dispersive
behavior with adequate signal to noise ratio (SNR) are long ranges (~ 10 km). Standard time-frequency
techniques (Fourier and wavelet analyses) were able to extract the modal arrival pattern for the lower
order modes. Higher order modes can be identified, and arrival times extracted by the application ofhigh-resolution techniques (warping transform). The orientation of the sensor was measured with goodprecision by the heading sensor and was validated with DOA estimates using SUS data. H/V ratio
technique was applied to ambient noise data using an algorithm (RayDec method) which identifies
and uses particle motion data corresponding to Scholte wave arrivals. The Scholte wave arrivals are
identified in the 20 to 30 Hz frequency band coming from an azimuth of ~3000 which is nearly opposite
direction to the SUS arrivals analyzed in this study. The simple sediment description (multiple mudand sand layers over basement) provided estimates of Scholte wave particle motion which then wasused to calculate H/V ratio for comparison with data derived H/V ratios. The resonant frequencies in
the 0-10 Hz band were matched to a lesser degree compared to the peaks in the 10-20 Hz band. It
should be noted that this match was achieved by iterating the model runs without using any inversion
algorithm. We plan to continue the inversion with better constraints on the shear speeds and layer
thicknesses based on available data (cores, seismic data and prior inversions) and using advanced
inversion techniques.

ACKNOWLEDGEMENTS
This work is supported by United States Office of Naval Research (ONR), code 322 OA. Authorsacknowledge the scientists, students, captain crew of R/V Armstrong who participated in the field
experiment.

REFERENCES
1. P. Wilson, D. Knobles, T. Nielsen, Guest Editorial, An Overview of the Seabed Characterization
Experiment, IEEE J. Oceanic Eng., 45,1-13, 2020.
2. Goff J., J. D. Chaytor, A. H. Reed, G. Gawarkiewicz, P. S. Wilson, and D. P. Knobles, Stratigraphic analysis
of a sediment pond within the New England mud patch: New constraints from high-resolution chirp
acoustic reflection data, Mar. Geol., 412,81-94, 2018
3. G. R. Potty and J. H. Miller, Effect of shear on modal arrival times, IEEE J. Ocean. Eng., vol. 45, 103-115,
2020.
4. J. Bonnel, Y-T Lin, D. Eleftherakis, J. A. Goff, S. Dosso, R. Chapman, J. H. Miller, and G. R. Potty ,"Geoacoustic inversion on the New England Mud Patch using warping and dispersion curves of highorder modes", The Journal of the Acoustical Society of America 143, EL405-EL411 (2018)
5. A. M. Thode, T. Sakai, J. Michalec, S. Rankin, M. S. Soldevilla, B. Martin, and K. H. Kim, “Displaying
bioacoustic directional information from sonobuoys using “azigrams”, J. Acoust. Soc. Am. 146 (1), July
2019.
6. Y. Nakamura, “A method for dynamic characteristics estimation of subsurface using microtremor on the
ground surface,” Quart. Rep. Railway Tech. Res. Inst. Tokyo, vol. 30, no. 1, pp. 25–33, 1989.
7. M. Hobiger , N. Le Bihan, C. Cornou, and P-Y Bard, “Multicomponent signal processing for Rayleighwave ellipticity estimation,” IEEE Signal Processing Magazine, 29-39, 2012. doi:
10.1109/MSP.2012.2184969
8. M. Hobiger, P.-Y. Bard, C. Cornou, and N. Le Bihan, “Single station determination of Rayleigh wave
ellipticity by using the random decrement technique (RayDec),” Geophys. Res. Lett., vol. 36, 2009.
9. C. J. Lai and G. J. Rix., Simultaneous Inversion of Rayleigh Phase Velocity and Attenuation for NearSurface Site Characterization, Georgia Institute of Technology Report, National Scince Foundation and
U. S. Geological Survey, July, 1998.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0504

Propeller Cavitation Localization applying Sparse Signal Recovery
Hamin CHOI1; Haesang YANG1; Sockkyu LEE3; Woojae SEONG1,2
1
2

Naval Architecture and Ocean Engineering, Seoul National University, Korea

Research Institute of Marine System Engineering, Seoul National University, Korea
3

Mechanical Engineering R&D, LIG Nex1, Korea

ABSTRACT
Conventional cavitation localization methods have problems in terms of accuracy or inability to distinguishadjacent sources because they have low resolution. In this study, we propose a method to apply sparse signal
recovery to the localization of cavitation, which can reconstruct the location of the cavitation with highresolution. In many cases, the cavitation noise generated by the propeller has a sparse property, so thecavitation localization problem is suitable for applying the sparse signal recovery technique. The proposed
method showed high-resolution and noise robustness due to the advantages and characteristics of the sparse
signal recovery method, and these advantages were illustrated in this study as the results of localization.
Keywords: Sparse signal recovery, Cavitation localization

1. INTRODUCTION
Propeller cavitation is a significant source of noise in the marine environment, affecting thesurvivability of warships as well as the comfort of passengers. It's crucial to identify and detect
cavitation in order to lessen the harmful impacts of propeller cavitation. Optical techniques have
limitations for locating the locations of the incipient TVCs since the bubbles that create them are tootiny to be seen [1]. As an alternative, acoustic measurements that incorporate noises from developing
TVCs have been applied to the cavitation localization [2-4].
There are two types of cavitation localization methods: Matched field processing (MFP) -based
methods and sparse signal recovery (SSR)-based methods. MFP-based methods are a traditional andwidely used methods for cavitation localization. MFP is a method to generate the virtual pressure field(replica) for the search space and to evaluate the similarity between the replica and acoustic
measurements. However, the MFP has a limitation in that it cannot distinguish adjacent sound sources
due to its low resolution. Meanwhile, in the case of SSR methods such as CS and SBL, high-resolution
results can be obtained, so studies have been conducted to localize cavitation by applying them.
Therefore, studies have been conducted to localize cavitation by applying SSR method such ascompressive sensing (CS), which can obtain high-resolution results [3-4]. Despite recent
advancements, the CS-based methods have a disadvantage that localization performance is dictatedby hyper-parameter. On the other hand, SBL is not dependent on the hyper-parameter and is generally
known to outperform CS. Herein, we propose an SBL-based cavitation localization method and verify
the performance of the proposed method with experimental results.

2. SPARSE REPRESENTATION
Cavitation noise was assumed to be mono-pole source in which sound propagates omnidirectionally.
fThen, the signal ym received by the 𝑚-th hydrophone can be expressed asymf ei (2 f / c ) rm f
x  nmf ,
4 rm

(1)

where the superscript 𝑓 is the frequency of the source, 𝑐 is the sound speed, 𝑟𝑚 is the distance𝑓from the source to the 𝑚 -th hydrophone, 𝑥 𝑓 is the source amplitude, and 𝑛𝑚 is the noise.
Assuming that there is a virtual source at each grid point by dividing the three -dimensional search
space, the signal vector composed of the 𝑚-th hydrophone signal can be expressed in the following
vector form.

yf  Af xf nf .

(2)

𝐱 𝑓 is estimated by SBL reconstruction scheme. The final localization result is compiled by adding
all 𝐱 𝑓 obtained in each process.

3. RESULTTo check the validity of the proposed method, a model-scale experimental data that conducted at
the Korea Research Institute of Ships and Ocean Engineering (KRISO) is used. The array consists of
7 hydrophones, the grid points were distributed with grid interval 0.02 m over the 3-D search space(x-axis:-0.15 to 0.15 m, y-axis:-0.20 to 0.20 m, z-axis:-0.20 to 0.20 m).

Figure 1 - Experimental results: (a) Image from high speed camera, (b) SBL localization result(frontview), (c) SBL localization result(side view), (d) MFP localization result(front view), (e) MFP
localization result(side view),
Fig 1(a) is image data captured from high speed camera video data, and cavitations are observedin the areas indicated by red and orange circles. Fig 1(b) and Fig 1(c) are front view and side view of
SBL localization result, respectively. Fig 1(d) and Fig 1(e) are front view and side view of MFP
localization result, respectively. The red dots in Fig 1(b) and Fig 1(c) indicate the positions of thehydrophones, the black circle represents the propeller diameters, and the estimated cavitation
positions are indicated by yellow and blue dots.
MFP results (Fig 1(d) and Fig 1(e)) have high ambiguity due to low resolution, making it difficult

to specify the location of cavitation. In addition, it can be seen that the vertical resolution is very poor
because the sensors are on the top. On the other side, the results of estimating the location of cavitationby the proposed method (Fig 1(b) and Fig 1(c)) coincide with the cavitation inception area (red circle
and orange circle in Fig 1(a)) and have high resolution.

4. CONCLUSION
In this paper, we proposed a method that applies SBL to localize cavitation. Through experimental
data results, it was confirmed that the proposed method obtains results with better resolution thanconventional method MFP. In addition, MFP has very poor vertical resolution and cannot localize
cavitations, whereas the proposed method shows results consistent with images obtained from highspeed cameras.

ACKNOWLEDGEMENTS
This research is funded by LIG Nex1 under Contract No. LIGNEX1-2020-0938(00) and was
supported by the Defense Acquisition Program Administration and the Defense Industry Technology
Center under Contract No. UC200001D.

REFERENCES
1. Lecoffre Y. Cavitation: Bubble Trackers. 1st ed. Boca Raton, Florida, USA: CRC press; 1999.
2. Kim D, Seong W, Choo Y, Lee J. Localization of incipient tip vortex cavitation using ray based matchedfield inversion method. J Sound Vib. 2015; 354:34-46.
3. Choo Y, Seong W. Compressive spherical beamforming for localization of incipient tip vortex cavitation.
J Acoust Soc Am. 2016;140:4085-4090.
4. Park M, Park Y, Lee K, Seong W. Incipient tip vortex cavitation localization using block-sparsecompressive sensing. J Acoust Soc Am. 2020;147:3454-3464.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0615

Demonstration of a physics-based sediment property inversion
algorithm applied to multibeam echosounder data.
Brian T. HEFNER1; Guangyu XU1; Darrell JACKSON1; Anatoliy IVAKIN1; Gorm WENDELBOE2
1

Applied Physics Laboratory, University of Washington, Seattle, WA 98105
2

Teledyne-RESON A/S, 3550 Slangerup, Denmark

ABSTRACT
Leveraging several decades of research on high-frequency sediment acoustics, a physics-based inversion
algorithm has been developed to invert multibeam echosounder data for geoacoustic parameters of theseafloor. This approach begins by generating synthetic data using a sonar-equation model and finds theinterface and volume scattering strengths that produce the best fit to the data along each beam. Physics-based
scattering models are then fit to the interface scattering strength to determine the roughness spectrum and thereflection coefficient for the sediment. The reflection coefficient is then used to determine the sediment
properties via a set of regression relations. To evaluate the performance of the algorithm for a range of
sediment types, sonar and ground-truth data were collected in 2019 at 10 sites in Sequim Bay, a shallow,
protected bay along the northern coast of the Olympic peninsula in Washington State. A second echosoundersurvey was conducted in the fall of 2021 to demonstrate the inversion’s ability to map sediment properties
over a wide area. Details of the inversion and results from the two field tests will be discussed.
Keywords: Sound, Insulation, Transmission

1. INTRODUCTION
Due to their high-resolution and ease of deployment, multibeam echosounders (MBES) havebecome popular tools for sediment characterization. These sonar systems, however, have been
designed and marketed primarily for the measurement of bathymetry and as a result, inversion of thesonar data typically relies on ad-hoc techniques that attempt to correlate features of the backscatteredsignal with ground-truth measurements of the seafloor properties. These ground truth measurements
can include the vibracores, grab samples, or images of the seafloor. These approaches are often
designed around a single sonar system and migrating the approach to a new sonar can be difficult andpotentially lead to differences in the seafloor characterization.
To overcome these issues and reduce the reliance on ground-truth data, a physics-based MBES
inversion has been developed which leverages decades of research on high-frequency scattering ofsound from the seafloor (1). This inversion builds on previous techniques developed for single-beamechosounders (2,3) and utilizes the time-series of the backscattered pressure along multiple beams toestimate the roughness and volume scattering strength of the sediment, from which the geoacoustic
properties of the seafloor can be determined from empirical relations (Figure 1a).
This inversion algorithm has been developed using data from three field tests: The Target andReverberation Experiment in 2013, the St. Andrews Bay Experiment in 2014, and a field test in
Sequim Bay in 2019. In these experiments, both MBES data and ground truth data were collected toso that the inversion could be evaluated and improved. A fourth field test in Sequim Bay was
conducted in November 2021. This field test focused on the application of the inversion to wide-areasurvey data to evaluate both the inversion performance and techniques to visualize the inversion
output and the uncertainties in that output.

1

hefner@apl.washington.edu

Figure 1: (a) Flow chart for the MBES inversion algorithm. (b) Mean grain size and porosity inversion
outputs compared to ground-truth data collected in the 2019 Sequim Bay field test. (c) Density ratio
determined using the MBES inversion along survey lines from the 2021 Sequim Bay field test.

2. SEQUIM BAY FIELD TESTS
Sequim Bay is located at the northern end of the Olympic Peninsula in Washington State, U.S.A.
The bay is connected at its northern end to the Juan de Fuca Strait through a small opening (see thebackground satellite image in Figure 1c) and is roughly 1.5 km wide and 3 km long. For the 2019 field
test, 10 sites within the northern portion of the bay were chosen to cover a range of sediment types
and depths. At each site, ground truth data was collected from diver cores, an in-situ velocimeter, alaser-line profiler, and a conductivity probe. Multibeam echosounder data was also collected along
200-m-long survey lines that passed through each of the sites. An example of the inversion output
compared to the ground truth measurements is shown in Figure 1b.
During the 2021 field test, multibeam echosounder data were collected along survey lines whichspanned the northern portion of the bay as well as longer lines which extended approximately along
the north-south axis of the bay. An example of the inversion output using all of the 2021 survey lines
is shown in Figure 1c.

3. SUMMARY
This talk will discuss in greater detail the physics-based inversion algorithm shown in Figure 1aand the results of the Sequim Bay field tests.

ACKNOWLEDGEMENTS
The Authors would like to thank Dr. Dana Woodruff at the Pacific Northwest National Laboratory-Sequim for her help in obtaining permitting and in conducting the field tests in Sequim Bay. Thiswork was supported by the U.S. Strategic Environmental Research and Development Program(SERDP).

REFERENCES
1. Hefner, B. T., Jackson, D. R., and Ivakin, A. N., Inversion of High Frequency Acoustic Data for
Sediment Properties Needed for the Detection and Classiﬁcation of UXO’s. Final Report. Strategic
Environmental Research and Development Program (SERDP).
2. E. Pouliquen and X. Lurton, “Seabed identification using echo-sounder signals,” ECUA 1992, pp.
535–538, (1992)
3. Sternlicht, D. D. and de Moustier, C. P., “Remote sensing of sediment characteristics by optimized
echo-envelope matching,” J. Acoust. Soc. Am., vol. 114, pp. 2727–2743, (2003).

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0666

Sparse frequency analysis of passive sonar signals using theadaptive learned iterative thresholding algorithm
Myoungin SHIN; Wooyoung HONG; Keunhwa LEE; Youngmin CHOO*
Department of ocean systems engineering, Sejong University, Republic of Korea

ABSTRACTIn this paper, we introduce the sparse frequency analysis using neural networks based on the iterative solversof adaptive learned iterative shrinkage thresholding algorithm (Ada-LISTA) that receives pairs of signals andtheir corresponding dictionaries as inputs, and learns the model. The Ada-LISTA has acceleration benefits oflearned solvers and incorporate the dictionary as part of the input data at both training and inference time,allowing for adaptivity to different models. In order to improve the performance of the Ada-LISTA basedfrequency analysis, we train the model using simulation data (pre-training step), and then reflect the physicalcharacteristic of the real-data, we retrain the model using the real-data with the cost function consideringsystem model of frequency analysis (fine-tuning step). Experiments using the underwater in-situ datameasured near the Korea peninsula show the superior restoration performance and processing speed of the
Ada-LISTA compared with the performance of the fast Fourier transform and the sparse Bayesian learning.
Keywords: Sparse frequency analysis, Ada-LISTA

1. Introduction
Passive sonar systems received the acoustic signals that emitted from marine objects such assubmarine and surface ship, and accurate estimation of frequency components is crucial to identifying
the target. The tonal signals which are generated by operation of machinery of ship are composed of
the sparse frequency component at low frequency region[1]. The frequency analysis problem can be
expressed by a linear equation of the form:y = Ax + n(1)

where 𝐱 ∈ ℝ𝑛 is the unknown frequency components, 𝐀 ∈ ℝ𝑚×𝑛 is the dictionary matrix, 𝑦 ∈ ℝ𝑚is the received signals which mainly composed of the tonal signals, and 𝐧 is the noise.
In order to solve the linear inverse problem with sparsity, compressive sensing and sparse Bayesianlearning (SBL) have been used to reconstruct the sparse solution [2-4]. However, they are hard to be
applied in real-time owing to high computation complexity. To overcome the limitation, in this study,
we apply neural networks based on iterative solvers of adaptive learned iterative shrinkage
thresholding algorithm (Ada-LISTA[5]). We compare the restoration performance from fast Fouriertransform (FFT), SBL and Ada-LISTA using the underwater in-situ data measured near the Korea
peninsula.

2. Methods
To solve (1), we apply the Ada-LISTA [5]:𝐱 𝑘+1 = 𝒮𝜃𝑘+1 ((𝐈 − 𝛾𝑘+1 𝐀𝑇 𝐖2𝑇 𝐖2 𝐀)𝐱 𝑘 + 𝛾𝑘+1 𝐀𝑇 𝐖1𝑇 𝐲),(2)

where 𝐖1,2 are weights, 𝛾 is a step size, 𝜃 is a scalar thresholding value, and K is number of unfold
layer. The unknown parameter Θ = (𝐖1 , 𝐖2 , {𝛾𝑘 , 𝜃𝑘 }𝐾𝑘=1 ) are learned through training the neuralnetworks using stochastic gradient descent. In order to improve performance, we train the Ada-LISTA
with two step: 1) pre-training step using simulation data, and 2) fine-tuning using real-data. In thefirst step, the loss function is written as
𝑁
2

2

(3)

‖𝐲(𝑖) − 𝐀ℱ𝐾 (𝐲 (𝑖) , 𝐀; Θ)‖2 .

(4)

min ∑‖𝐲 (𝑖) − 𝐀ℱ𝐾 (𝐲(𝑖) , 𝐀; Θ)‖2 + ‖ℱ𝐾 (𝐲(𝑖) , 𝐀; Θ) − 𝐱 (𝑖) ‖2 + 𝜆‖ℱ𝐾 (𝐲(𝑖) , 𝐀; Θ)‖1.
Θ

𝑖=1

In second step, the loss function is as
𝑁

min ∑
Θ

2

𝑖=1

𝑁 is the number of training example, 𝜆 is the hyperparameter controlling the sparsity penalty, andℱ(𝐲, 𝐀; Θ) outputs frequency components using the Ada-LISTA.

3. Results
The training data in pre-training step are randomly generated using (1); activating frequency
component are from 25 to 100, and SNRs vary from -15dB to 15dB. We used 20,000 simulation data
and 200 real-data for pre-training and fine-tuning, respectively.
The restoration results from the FFT, Ada-LISTA, and SBL are displayed in Figure 1. The
frequency analysis using the FFT is smeared by the overall noise. The Ada-LISTA and SBL improvethe detection results in terms of the resolution and denoising; the overall noise is significantly reducedby the sparse estimation, which is beneficial to the detection clarity. For the simulation in Figure 1,
the SBL require computational time of 7600 second under the computational environment of Intel(R)Core (TM) i9-9900K CPU, therefore it is hard to be applied in real-time. However, in case of the AdaLISTA, once the model is trained in advance, we can obtain the frequency analysis results of Figure
1 in almost 0.061 second.

Figure 1 – Simulation results using in-situ data measured near the Korea peninsula.

REFERENCES
1. Abraham, D.A. Underwater Acoustic Signal Processing. Springer, Switzerland, AG; 2019. p. 225-230
2. Donoho, D. Compressed sensing. IEEE Trans. Signal Process. 2006;52(4):1289-1306.
3. Tipping, M. E. Sparse Bayesian learning and Relevance Vector Machine. J. Mach. Learn Res. 2001;1:211244.
4. Shin, M. Hong, W. Lee, K. Choo, Y. Frequency analysis of acoustic data using multiple-measurement
sparse Bayesian learning. Sensors. 2021;21(17):5827.
5. Aberdam, A. Golts, A. Elad, M. Ada-LISTA: learned solves adaptive to varying models. IEEE transactions
on pattern analysis and machine intelligence (Early Access). 2021;

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0730

Geoacoustic inversion of an ocean waveguide using Bayesian
optimization with a Gaussian process surrogate model
William JENKINS1; Peter GERSTOFT1
1

Scripps Institution of Oceanography, University of California San Diego, La Jolla, Calif., USA

ABSTRACT
Geoacoustic model optimization and inversion are computationally expensive endeavors. In cases where aparameter grid search is prohibitively expensive, optimization produces an approximated solution throughsampling techniques. Recent work proposes sampling with a Bayesian approach that uses a Gaussian process
as a surrogate model of the objective function. In this study, the objective function is defined as a Bartlett
processor which measures the correlation between a received and replica pressure field on a vertical line
array. The replica field is calculated using a propagation model whose parameters are selected from theparameter search space. The surrogate model represents the posterior on the objective function and is updatedwith each model evaluation. Optimization is performed with sequential model evaluations, with anacquisition function guiding the next point in parameter space to be evaluated. Expected improvement is usedas the acquisition function, as it incorporates the uncertainty in the posterior to select the next evaluation
point and adaptively balances exploitation (densely sampling around the best observed value of the objectivefunction) and exploration (sampling in areas of high variance in the objective function posterior). Results
indicate that Bayesian optimization using a Gaussian process surrogate model converges rapidly on an
approximated optimal solution.
Keywords: Estimation, Bayesian, Optimization

1. INTRODUCTION
A popular and conventional approach for underwater acoustic source localization is matched fieldprocessing (MFP), which uses beamforming to match a sound pressure field 𝐝 measured by an arraywith replica fields 𝐝∗ produced by a forward acoustic propagation model with parameters 𝐦.[1]–[3]The conventional beamformer output is the Bartlett processor:
𝑓(𝐦) = '𝐰 " 𝐝'#(1)where 𝐰 = 𝐝∗ /‖𝐝∗ ‖. Where the measured and replica fields are most highly correlated indicates the+ of the source’s range and depth. MFP uses a grid search through the parameters 𝐦best estimate 𝐦
to find the maximum correlation produced by the Bartlett processor. One of the downsides to MFP isthat it is computationally intensive, consisting of an exhaustive grid search through parameter space,in this case range and depth. Another limitation to grid search is that the sampling is restricted to thepoints on the grid and may miss important features that do not lie on the grid. Finally, the information
used in MFP is not used for subsequent decision making about where to sample next.
To improve sampling efficiency and utilize the information learned upon each sample evaluation,
we use a Bayesian optimization method to estimate the optimal parameters. Previous Bayesian
optimization techniques include simulated annealing and genetic algorithms, though these techniques
are also computationally intensive and rely on Monte Carlo sampling of the objective function.[4],[5] In this study we treat the objective function in a Bayesian approach that incorporates previous
sample results in the decision of where to sample next, thereby improving sampling efficiency andconverging on the optimal solution more rapidly.[6]–[8]1wjenkins@ucsd.edu

Figure 1. (a) Ambiguity surface from matched field processing (MFP) for a 201 Hz source located at 3km range and 62 m depth. (b) Ambiguity surface sliced at the source depth of 62 m.

2. SEQUENTIAL OPTIMIZATION METHOD
2.1 Gaussian Process Surrogate Model
The objective of the optimization is to maximize the objective function in Eq. (1) to find the
optimal parameters by:+ = argmax[𝑓(𝐦)].
𝐦

(2)

𝐦

To accomplish this, we simplify the optimization by approximating the objective function with aGaussian process, which is characterized by the posterior distribution of the objective function. A
Gaussian process is defined as a collection of random variables, any finite number of which are jointly
distributed.[9], [10] Consider N samples from D-dimensional parameter space 𝐗 %×' = [𝐱( , … , 𝐱' ]whose observations are 𝐟 = [𝑓(𝐱( ), … , 𝑓(𝐱' )]) ; the Gaussian process is described by a mean function:
𝛍 = 𝔼[𝐟] = [𝜇( (𝐱), … , 𝜇' (𝐱)]) ,(3)

where 𝜇(𝐱) is the mean at 𝐱; and a covariance function:)

𝚺*+ = 𝔼<(𝑓(𝐱, ) − 𝜇(𝐱, ))(𝑓>𝐱- ? − 𝜇(𝐱- ))@ = 𝒦(𝐱, , 𝐱- ),(4)

where 𝒦 is a kernel function measuring the similarity between two samples 𝐱, and 𝐱- . The Gaussian
process is thus described as 𝐟~𝒩(𝛍, 𝚺). Consider a dataset of observations with added Gaussian noise
𝒟 = {(𝐱. , 𝑦. ), 𝑛 = 1: 𝑁 } = {𝐗, 𝐲} where 𝑦. = 𝑓(𝐱. ) + 𝜖, 𝜖~𝒩(0, 𝜎/# ). The joint distribution of thenoisy observations and the noise-free predictive distribution at new points {𝐗 ∗ , 𝐟∗ } is:𝐊1𝐲𝛍0𝑝(𝐲, 𝐟∗ |𝐗, 𝐗 ∗ ) = T𝐟 U = 𝒩 VT 𝛍 U , W )
𝐊 0,∗
∗
∗

𝐊 0,∗
YZ,
𝐊 ∗,∗

(5)

where 𝛍 and 𝛍∗ are the mean functions at 𝐗 and 𝐗 ∗ , respectively; and𝐊 1 = 𝐊 0,0 + 𝜎/# 𝐈 = 𝒦(𝐗, 𝐗)'×' + 𝜎/# 𝐈
𝐊 0,∗ = 𝒦(𝐗, 𝐗 ∗ )'×'∗
𝐊 ∗,∗ = 𝒦(𝐗 ∗ , 𝐗 ∗ )'∗×'∗ .

(6)
(7)
(8)

The posterior predictive distribution at 𝐗 ∗ is:𝑝(𝐟∗ |𝒟, 𝐗 ∗ ) = 𝒩(𝐟∗ |𝛍∗ , 𝚺∗ )(9)𝛍∗ = 𝔼[𝐟∗ |𝒟, 𝐗 ∗ ] = 𝑚(𝐗 ∗ ) + 𝐊 )0,∗ 𝐊 3(
1 (𝐲 − 𝛍0 ))3(𝚺∗ = 𝐊 ∗,∗ − 𝐊 0,∗ 𝐊 1 𝐊 0,∗ .

(10)
(11)

where

Range [km]Figure 2. Top panels: the Gaussian process posterior distribution (blue) of the true objective function (red).
Bottom panels: expected improvement acquisition function. The next sample point is denoted by the black
vertical dotted line.
The kernel function is one of the design parameters and can be treated as a hyperparameter.[9]For this study, the Matern kernel with 𝜈 = 5/2 provided the best results. Hyperparameters that require
tuning are the variance and the lengthscale in the Matern kernel, which are obtained by calculating
the marginal likelihood given the parameters and performing gradient descent to find the optimal
hyperparameters.[9], [10]2.2 Acquisition Function
The next important component to this method is to algorithmically determine which point inparameter space to evaluate next. Acquisition functions seek to balance exploitation of the best
observed values so far with exploration of regions where little is known. In this study, we utilize theexpected improvement (EI) algorithm [6], [11]as it robustly balances between exploitation andexploration, thus avoiding convergence on local optima and sufficiently exploring regions of highvariance. Expected improvement is defined as:
𝛼45 (𝐱) = (𝑚(𝐱) − 𝑓 6 )CDF>𝑓 6 ; 𝑚(𝐱), 𝜎 # (𝐱)? + 𝜎(𝐱)𝒩(𝑓; 𝑚(𝐱), 𝜎 # (𝐱))(12)Where 𝑓′ is the best observed value so far and the CDF is the cumulative distribution function at 𝐱.
The point which is to be evaluated next is given by:𝐱∗ = argmax[𝛼(𝐱)].
𝐱(13)

Figure 2 shows the Bayesian optimization in progress over four different iterations. In each
iteration, the top panel shows the mean function of the Gaussian process plotted with a blue line andthe standard deviation plotted with a shaded blue curve; the true objective function is in red, andsamples are indicated with black “x” marks. In the bottom panel, the EI acquisition function is plotted.
The vertical black dotted line indicates where the next sample will be evaluated, as determined by the
maximum value of the acquisition function. In this example, the optimization locates the optimal
solution after 50 iterations.

3. RESULTS
Initial simulations indicate rapid convergence on one-dimensional range estimation problems andtwo-dimensional range-depth localization problems. Further simulations are being conducted for
additional geoacoustic parameters to estimate ocean bottom layers and their properties. Thesesimulations are conducted using the KRAKEN normal mode propagation model.[12] In addition tosimulations, this technique is being applied to real data collected during the SWELLEX96 underwater
acoustics experiment, which took place off the coast of Southern California. The impacts of various
configurations such as noise levels and source range are under investigation.

ACKNOWLEDGEMENTS
This work is funded by the Office of Naval Research (ONR) through the National Defense Scienceand Engineering Graduate Fellowship and ONR grant 1006812-1.

REFERENCES[1] M. J. Hinich, “Maximum likelihood estimation of the position of a radiating source in a waveguide,” The
Journal of the Acoustical Society of America, vol. 66, no. 2, pp. 480–483, Aug. 1979, doi:
10.1121/1.383099.
[2] R. G. Fizell, “Application of high‐resolution processing to range and depth estimation using ambiguity
function methods,” The Journal of the Acoustical Society of America, vol. 82, no. 2, pp. 606–613, 1987.
[3] A. B. Baggeroer, W. A. Kuperman, and H. Schmidt, “Matched field processing: Source localization incorrelated noise as an optimum parameter estimation problem,” The Journal of the Acoustical Society ofAmerica, vol. 83, no. 2, pp. 571–587, Feb. 1988, doi: 10.1121/1.396151.
[4] P. Gerstoft, “Inversion of seismoacoustic data using genetic algorithms and a posteriori probability
distributions,” The Journal of the Acoustical Society of America, vol. 95, no. 2, pp. 770–782, Feb. 1994,
doi: 10.1121/1.408387.
[5] P. Gerstoft and C. F. Mecklenbräuker, “Ocean acoustic inversion with estimation of a posteriori
probability distributions,” The Journal of the Acoustical Society of America, vol. 104, no. 2, pp. 808–819,
Aug. 1998, doi: 10.1121/1.423355.
[6] J. J. Beland and P. B. Nair, “Bayesian Optimization Under Uncertainty,” 31st Conference on Neural
Information Processing Systems (NIPS 2017), 2017.
[7] B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas, “Taking the Human Out of the Loop:
A Review of Bayesian Optimization,” Proc. IEEE, vol. 104, no. 1, pp. 148–175, Jan. 2016, doi:
10.1109/JPROC.2015.2494218.
[8] J. Snoek, H. Larochelle, and R. P. Adams, “Practical Bayesian Optimization of Machine Learning
Algorithms,” Advances in Neural Information Processing Systems, vol. 25, 2012.
[9] C. E. Rasmussen and C. K. I. Williams, Gaussian processes for machine learning. Cambridge, Mass: MIT
Press, 2006.
[10] K. P. Murphy, Probabilistic machine learning: An introduction. MIT Press, 2022. [Online]. Available:
probml.ai[11] D. R. Jones, “A Taxonomy of Global Optimization Methods Based on Response Surfaces,” Journal of
Global Optimization, vol. 21, no. 4, pp. 345–383, 2001.
[12] M. B. Porter, “The KRAKEN normal mode program,” SACLANT Undersea Research Centre
Memorandum (SM-245)/Naval Research Laboratory Memorandum Report 6920, Sep. 1991.

ABS-0931

Gridless sparse DOA estimation for ocean acoustic localization
Yongsung PARK(1) , Peter GERSTOFT(2)(1) University of California San Diego, La Jolla, CA, USA, yongsungpark@ucsd.edu(2) University of California San Diego, La Jolla, CA, USA, gerstoft@ucsd.edu

ABSTRACTThis work presents an estimation method for direction-of-arrivals (DOAs) based on sparse signal processing.
Sparse methods for DOA aim to express beamforming output as a sparse signal in the angular domain andachieve high resolution. The proposed method obtains sparse solutions through an iterative covariance-fitting
using alternating projections and is a gridless sparse method. Compared to gridless sparse DOA estimators,
the proposed method is computationally efficient and deals with non-uniformly configured linear arrays. In
addition, high-resolution and reliable DOA estimation is achieved even with single-snapshot data and coherent
sources. We evaluate the method using data from a real ocean experiment. The real data involve a time sequenceof single snapshot data, stationary sources over multiple snapshots but coherent due to multipath effects, andco-prime array configuration, a popular non-uniform linear array.

Keywords: Direction-of-arrival (DOA) estimation, Sparse signal recovery, Variational Bayesian estimation

1

INTRODUCTION

The problem of direction-of-arrival (DOA) estimation, beamforming, is to retrieve the direction of (usually few)
sound sources from measurements of the wavefield with an array of sensors. Sparse signal processing, including
compressive sensing, reconstructs signals from measurements using sparsity [2]. Compressive beamforming [3,4, 5, 6, 7] obtains a sparse solution that provides sharp estimates of the beamforming spectrum. The advantagesof compressive beamforming are 1) it offers higher resolution than traditional methods, 2) it handles any numberof snapshots, including a single snapshot, 3) it is not sensitive to coherent arrivals, and 4) its extensions areapplicable, e.g., sequential processing [8, 9].
We solve the compressive beamforming with gridless sparse processing [10, 11]. The gridless method overcomes the basis mismatch [12, 13, 14] and does not assume that the true DOAs lie on angular search grids.
Challenges of the gridless method involve accelerating solvers and handling arbitrary array geometries.
We propose a method using alternating projections (AP) [15, 16] for gridless sparse DOA estimation basedon covariance fitting, AP-Covariance. Covariance fitting-based sparse processing constructs a structured matrix
that has the best fit to a sample covariance matrix (SCM) and pursues the DOAs. AP-Covariance has the objective and constraint of obtaining a low-rank positive semi-definite (PSD) Toeplitz matrix for a DOA-dependent
SCM. AP consists of a sequence of two projections: low-rank Toeplitz projection and PSD projection.
We study the performance of AP-Covariance with single- and multiple-snapshot data, incoherent and coherent arrivals, and non-uniform linear arrays.

2

ARRAY DATA MODEL

We consider narrowband sources with K DOAs θk ∈ [−90◦ , 90◦ ) are in the far-field of a uniform linear array(ULA) with M sensors. Let the kth DOA of the complex source amplitude for the lth snapshot sk,l ∈ C,

k = 1, . . . , K, l = 1, . . . , L. The array data Y ∈ CM×L and the SCM R̃ ∈ CM×M are modeled as
K

K

k=1

k=1

T
Y = ∑ a(θk )sT
k + E = ∑ ck a(θk )φ k + E,

R̃ = YYH /L,

(1)
(2)

L
M×L is the additive noise,
where sk = [sk,1 . . . sk,L ]T ∈ CL , ck = ksk k2 > 0, φ k = c−1
k sk ∈ C with kφ k k2 = 1, E ∈ C
and the steering vector,
iT
h 2π
2π
(3)
a(θ ) = e− j λ d1 sin θ . . . e− j λ dM sin θ ∈ CM ,where λ is the wavelength. The distance between sensors 1 and m is dm = δm d, m = 1, . . . , M, where δm is ascale factor in units of sensor-spacing d (here, d = λ /2). A uniform linear array (ULA) has δ = [0 1 . . . M − 1]T ,and a non-uniform linear array (NUA) consists of real number δm .

3

GRIDLESS SPARSE DOA ESTIMATION

Assuming the sources sk and the noise E are uncorrelated and E{sl } = diag(p) and E{el } = σ 2 I, the SCM (2)becomes,K

R̃ = ∑ c2k a(θk )aH(θk )+ σ 2 I =Adiag(p)AH + σ 2 I.

(4)

k=1

Without correlated terms, (2) converges to (4) as L → ∞ [17]. DOAs are estimated from R̃ under statisticalassumptions [18, 19, 20, 21].
Gridless sparse DOA estimation based on covariance fitting uses a parameter R ∈ CM×M and fits it to SCM
R̃(= Adiag(p)AH + σ 2 I) (4). The covariance fitting (handles both nonsingular and singular (L < M) R̃) is 21
accomplished by minimizing R− 2 R̃ − R[18, p. 553], [22].
F
Sparse processing is achieved minimizing principal components in Y (1), expressed using the atomic l0
norm [13] as()
kY∗ kA ,0 = inf

K

ck ,θk ,φ k

K : Y∗ = ∑ ck a(θk )φ T
k .

(5)

k=1

Minimizing (5) is equivalent to minimizing the rank of R∗ = Y∗ Y∗H /L [13, 18]. Given (4), covariance fittingbased gridless sparse DOA estimation solves the rank minimization
RR̃  0.
min rank(R − σ 2 I) subject to P = 
(6)
R,Z
R̃ Z
For the details and a convex relaxation method, see [1].

4

ALTERNATING PROJECTIONS

We formulate the problem (6) with the objective, projecting R in a low-rank Toeplitz matrix, and the constraint,
projecting R in a PSD matrix. AP-Covariance consists of PSD projection and K-rank Toeplitz projection.
4.1 PSD projection
The projection of P onto the PSD set P is achieved from the eigendecomposition of P ∈ C2M×2M with its
Heigenvalues µi and eigenvectors qi , P = ∑2Mi=1 µi qi qi [15, 16],
2M

PP (P) = ∑ max{0, µi }qi qH
i .
i=1

(7)

(a)

(c)

Full ULA

(d)

(b)
(e)

Co-prime array(f)

Figure 1. DOA estimation using acoustic data from the SWellEx-96 experiment. Single-snapshot processing for(a) CBF, (b) AP- Covariance. (c) Multi-snapshot processing for CBF, MUSIC, SBL, and AP-Covariance. (d)The full ULA structure. (e) Co-prime array processing for CBF, MUSIC, SBL, and AP-Covariance. (f) The
co-prime array structure.
4.2 K-rank Toeplitz projection
The projection of R onto the K-rank Toeplitz set R is achieved [23, 24],K

PR (R) = ∑ ĉ2k a(θ̂k )aH (θ̂k ) = Âdiag(p̂)ÂH ,k=1†

p̂ = diag Â R(Â† )H ,

(8)
(9)

where Â = [a(θ̂1 ) . . . a(θ̂K )] ∈ CM×K, Â† is the Moore-Penrose pseudo-inverse, and diag(A) is the column vector
containing the diagonal elements of matrix A.
The factorization (8) is the Vandermonde decomposition [18, Sec. 11.6.2], which recovers DOAs. APCovariance iteratively updates R projecting it onto SCM (7) and formulating it into the low-rank Toeplitz matrix(8). The procedure for AP-Covariance is summarized in [1, Secs. IV and V].

5

EXPERIMENTAL RESULTS

The gridless sparse method demonstrates DOA estimation performance on ocean acoustic measurements forsource tracking. The data is from the the SWellEx-96 (shallow water evaluation cell experiment 1996) experiment [9, 25, 26], conducted approximately 12 km from the tip of Point Loma near San Diego, CA. During
the Event S5, from 23:15–00:30 GMT on 10–11 May, two sources, a shallow and a deep, were towed simultaneously from 8.65 km southwest to 2.90 km northeast of the vertical uniform linear array (VLA). The VLA
has M = 64 sensors with spacing d = 1.875 m and was spanning 94.125–212.25 m depth. (Element 43 wascorrupted and thus excluded.)We focus on the deep source towed at 54 m depth at frequency 148 Hz. The data have a sampling frequencyof 1500 Hz. The duration data covers 0.5 min before and 1 min after the point when the source was closest to
the VLA, and the data is divided into 63 % overlapping 87 snapshots. Each snapshot data is Fourier transformed
with 212 samples.
The performance results are shown for a single snapshot, multi-snapshot with coherent arrivals, and a coprime array, see Fig. 1. The figure includes results for CBF, MUSIC, SBL, and AP-Covariance. AP-Covarianceworks well with single-snapshot [Fig. 1(b)], and sparse signal processing, SBL and AP-Covariance, work wellwith multi-snapshot data with coherent arrivals [Fig. 1(c)]. The co-prime array has M1 = 21, M2 = 2 (M = 24)[Fig. 1(e)–(f)]. Using co-prime arrays, we can reduce the number of sensors and obtain good results. APCovariance and SBL can accurately estimate DOAs using the co-prime array.

6

CONCLUSION

We have proposed a gridless sparse DOA estimator, which deals with a single snapshot, multiple snapshotswith coherent arrivals, and arbitrary array geometries. Our evaluation based on real data indicates favorable
performance compared to previous methods. For more results and derivation, please see [1].

ACKNOWLEDGEMENTS
This research was supported by the Office of Naval Research under Grant N00014-21-1-2267.

REFERENCES[1] Y. Park and P. Gerstoft, “Gridless sparse covariance-based beamforming via alternating projections including
co-prime arrays,” J. Acoust. Soc. Am., vol. 151, no. 6, pp. 3828–3837, Jun. 2022.
[2] P. Gerstoft, C. F. Mecklenbräuker, W. Seong, and M. Bianco, “Introduction to compressive sensing inacoustics,” J. Acoust. Soc. Am., vol. 143, no. 6, pp. 3731–3736, Jun. 2018.
[3] D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Trans. Signal Process., vol. 53, no. 8, pp. 3010–3022, Aug. 2005.
[4] P. Stoica, P. Babu, and J. Li, “SPICE: A sparse covariance-based estimation method for array processing,”
IEEE Trans. Signal Process., vol. 59, no. 2, pp. 629–638, Feb. 2011.
[5] P. Gerstoft, A. Xenaki, and C. F. Mecklenbräuker, “Multiple and single snapshot compressive beamforming,”
J. Acoust. Soc. Am., vol. 138, no. 4, pp. 2003–2014, Oct. 2015.
[6] Y. Park, Y. Choo, and W. Seong, “Multiple snapshot grid free compressive beamforming,” J. Acoust. Soc.
Am., vol. 143, no. 6, pp. 3849–3859, Jun. 2018.
[7] R. R. Pote and B. D. Rao, “Robustness of sparse Bayesian learning in correlated environments,” in Proc.
IEEE ICASSP, 2020, pp. 9100–9104.
[8] F. Meyer, Y. Park, and P. Gerstoft, “Variational Bayesian estimation of time-varying DOAs,” in Proc. IEEEFUSION, 2020, pp. 1–6.
[9] Y. Park, F. Meyer, and P. Gerstoft, “Sequential sparse Bayesian learning for time-varying direction ofarrival,” J. Acoust. Soc. Am., vol. 149, no. 3, pp. 2089–2099, Mar. 2021.
[10] P. Pal and P. P. Vaidyanathan, “A grid-less approach to underdetermined direction of arrival estimation via
low rank matrix denoising,” IEEE Signal Process. Lett., vol. 21, no. 6, pp. 737–741, Jun. 2014.
[11] Y. Chi and M. Ferreira Da Costa, “Harnessing sparsity over the continuum: Atomic norm minimization
for superresolution,” IEEE Signal Process. Mag., vol. 37, no. 2, pp. 39–57, Mar. 2020.

[12] Y. Chi, L. L. Scharf, A. Pezeshki, and A. R. Calderbank, “Sensitivity to basis mismatch in compressedsensing,” IEEE Trans. Signal Process., vol. 59, no. 5, pp. 2182–2195, May 2011.
[13] G. Tang, B. N. Bhaskar, P. Shah, and B. Recht, “Compressed sensing off the grid,” IEEE Trans. Inf.
Theory, vol. 59, no. 11, pp. 7465–7490, Nov. 2013.
[14] E.J. Candès and C. Fernandez-Granda, “Towards a mathematical theory of super-resolution,” Commun.
Pure Appl. Math., vol. 67, no. 6, pp. 906–956, Apr. 2014.
[15] S. Boyd and L. Vandenberghe, Convex optimization, Cambridge university press, Cambridge, U.K., 2004.
[16] J. Dattorro, Convex optimization & Euclidean distance geometry, Meboo Publishing USA, Palo Alto, CA,
USA, 2010.
[17] P. Gerstoft, R. Menon, W. S. Hodgkiss, and C. F. Mecklenbräuker, “Eigenvalues of the sample covariancematrix for a towed array,” J. Acoust. Soc. Am., vol. 132, no. 4, pp. 2388–2396, Oct. 2012.
[18] Z. Yang, J. Li, P. Stoica, and L. Xie, “Sparse methods for direction-of-arrival estimation,” in Academic
Press Library in Signal Processing: Array, Radar and Communications Engineering, vol. 7, chapter 11, pp.
509–581. Academic Press, Jan. 2018.
[19] P. Gerstoft, C. F. Mecklenbräuker, A. Xenaki, and S. Nannuru, “Multisnapshot sparse Bayesian learning
for DOA,” IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1469–1473, Oct. 2016.
[20] B. D. Rao and K. V. S. Hari, “Performance analysis of root-MUSIC,” IEEE Trans. Acoust., Speech, Signal
Process., vol. 37, no. 12, pp. 1939–1949, Dec. 1989.
[21] H. Qiao and P. Pal, “Guaranteed localization of more sources than sensors with finite snapshots in multiple
measurement vector models using difference co-arrays,” IEEE Trans. Signal Process., vol. 67, no. 22, pp.
5715–5729, Nov. 2019.
[22] Z. Yang and L. Xie, “On gridless sparse methods for line spectral estimation from complete and incomplete
data,” IEEE Trans. Signal Process., vol. 63, no. 12, pp. 3139–3153, Jun. 2015.
[23] M. Wagner, Y. Park, and P. Gerstoft, “Gridless DOA estimation and root-MUSIC for non-uniform linear
arrays,” IEEE Trans. Signal Process., vol. 69, pp. 2144–2157, Mar. 2021.
[24] S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,”
IEEE Trans. Signal Process., vol. 57, no. 7, pp. 2479–2493, Jul. 2009.
[25] K. L. Gemba, S. Nannuru, and P. Gerstoft, “Robust ocean acoustic localization with sparse Bayesianlearning,” IEEE J. Sel. Topics Signal Process., vol. 13, no. 1, pp. 49–60, Mar. 2019.
[26] F. Meyer and K. L. Gemba, “Probabilistic focalization for shallow water localization,” J. Acoust. Soc. Am.,
vol. 150, no. 2, pp. 1057–1066, Oct. 2021.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0950

Effect of Water Column Variability on Normal-Mode Based GeoAcoustic Inversion
Lin WAN 1; Mohsen BADIEYElectrical and Computer Engineering, University of Delaware, USA

ABSTRACT
Normal mode characteristics of broadband acoustic signals have been utilized in estimating the sound speed
and attenuation in sediment at the New England Mud Patch during the 2017 Seabed Characterization
Experiment (SBCEX2017), which was conducted when the water column had an almost depth-independent
temperature profile. From early May to early June of 2022, when the water column showed strong temporaland spatial fluctuations, researchers revisited the New England Mud Patch and performed a series ofexperiments (SBCEX2022) using the same experimental setup as that of SBCEX2017. The environment
measurements and acoustic data from both SBCEX2017 and SBCEX2022 enable us to study the effect ofwater column variability on geo-acoustic inversion. In this research, the effect of water column variability is
examined by (1) comparing the difference of measured water column temperature profiles and (2) evaluating
the variation of the normal mode characteristics (e.g. Airy phase structure and modal arrival time) due to thedifferent water column conditions. Finally, the appropriate inversion approaches minimizing the watercolumn effect are discussed. [Work supported by ONR Ocean Acoustics.]
Keywords: Normal mode, Geoacoustic inversion, SBCEX

1. INTRODUCTION

Normal mode characteristics of broadband acoustic signals have been utilized in estimating thesound speed and attenuation in sediment at the New England Mud Patch during the 2017 Seabed
Characterization Experiment (SBCEX2017) [1-4]. The SBCEX2017 was conducted from early Marchto early April of 2017 when the water column was well mixed and had an almost depth-independent
temperature profile. From early May to early June of 2022, a following experiment (SBCEX2022)
was performed at the SBCEX2017 site, but in a different water column environment with strong
temporal and spatial fluctuations. As a part of the SBCEX2022, broadband signals were transmitted
and received using the same source-receiver configuration as that of SBCEX2017. One of therationales for this unique experimental design is to study the effect of water column variability on
normal-mode based geo-acoustic inversion. This research focuses on the low-frequency analysis
including the Airy phase region of normal modes. The measured water column temperature profiles
from both SBCEX2017 and SBCEX2022 are presented in Sec. 2, followed by the analysis of normal
mode arrivals and discussion in Sec. 3.

2. WATER COLUMN TEMPERATURE PROFILES

A vertical line array equipped with acoustic hydrophones and environmental sensors was deployedat the same location during both SBCEX2017 and SBCEX2022 by the University of Delaware. The
detailed information about the array can be found in Ref. [1,2]. The left panel of Figure 1 shows themeasured temperature profiles as a function of geo-time during SBCEX2017 (top panel) and
SBCEX2022 (bottom panel). The right panel of Figure 1 are the selected examples of the temperature
profile as a function of depth for SBCEX2017 and SBCEX2022. The times of measurements of these
two temperature profiles are indicated by the back dash lines in the left top and bottom panel,
respectively. It is noted that these two temperature profiles will be used as inputs for the calculationof modal arrival times in Sec. 3.
1

wan@udel.edu

The following information can be obtained from Figure 1. (a, left top panel) The water column was
well mixed and had a depth independent water column temperature profile during most of the
SBCEX2017. The water temperate varied from 4o C to 6 o C. (b. left bottom panel) The water column
in SBCEX2022 was much stratified due to the surface warming and a bottom warm layer. The watertemperature ranged from 8 o C to 14o C and higher than that in SBCEX2017. The difference of measured
water column temperature profiles cause the variation of the normal mode characteristics (e.g. Airyphase structure and modal arrival time), which is evaluated in Sec. 3.

Figure 1. The measured temperature profiles as a function of geo-time during SBCEX2017 (left
top panel) and SBCEX2022 (left bottom panel). Right panel: the selected examples of thetemperature profile as a function of depth for SBCEX2017 and SBCEX2022. The times ofmeasurements are indicated by the back dash lines in the left top and bottom panel, respectively.

3. WATER COLUMN EFFECT ON NORMAL MODE ARRIVALSIn this section, the variation of the normal mode characteristics is studied using the different
temperature profiles from SBCEX2017 and SBCEX2022. In particular, the two profiles in the rightpanel of Figure 1 are used as examples. It is noted that these two profiles correspond to two SUS
charges detonated and received using the same experimental configuration during SBCEX2017 and
SBCEX2022, respectively. This research focuses on the numerical simulation and the acoustic data
analysis will be reported in a separate paper.
Figure 2 shows the calculated modal arrival times for the first three modes below 80Hz using thetemperature profiles in the right panel of Figure 1. The input parameters for the sediment are the samefor the two cases. In other words, the arrival time difference is only due to the water column changes.
The source-receiver distance is 15.45km and the details of the input parameters for the calculation ofmodal arrival times can be found in Ref. [1, 2].
The results in Figure 2 indicate that (1) the normal modes travelled faster in SBCEX2022 due tothe warmer water column, (2) the Airy frequency corresponding to the minimum group velocity foreach mode is not sensitive to the water column variation, but sensitive to the sound speed in thesediment [1]. The calculated Airy frequency is listed in Table 1. (3) the cut-off frequencies are very
close for the two cases. Based on (2) and (3), the model arrivals below Airy frequency could be used
in a very dynamic water column environment for geo-acoustic inversion. The resulting inversion
algorithm could minimize the water column effect.

SBCEX2017
SBCEX2022

Table. 1 Airy frequency
Mode 1Mode 2
12 Hz
28 Hz
12 Hz
28 Hz

Mode 344 Hz
46 Hz

Figure 2. Comparison of modal arrival time using the water column temperature profiles fromSBCEX2017 and SBCEX2022.

ACKNOWLEDGEMENTS

The efforts of all SBCEX 2017 and SBCEX 2022 participants are gratefully acknowledged. This
work is sponsored by the Office of Naval Research (ONR), Ocean Acoustics Program (322OA) via
grants N00014-21-1-2424 and N00014-20-1-2653.

REFERENCES

1. Wan, L., Badiey, M., Knobles, D. P., Wilson, P. S., The Airy Phase of Explosive Sounds in Shallow Water.
J. Acoust. Soc. Am. 2018;143(3): EL199-EL205.
2. Wan, L., Badiey, M., Knobles, D. P.,Wilson, P. S., Goff, J. A., Estimates of Low-Frequency Sound Speedand Attenuation in a Surface Mud Layer Using Low-Order Modes. IEEE JOE. 2020;45(1):201-211.
3. Bonnel, J., Lin, Y-T., Eleftherakis, D., Goff. J.A., Dosso S., Chapman, R., Miller, J.H., Potty, G.R.,
Geoacoustic inversion on the New England Mud Patch using warping and dispersion curves of high-ordermodes. J. Acoust. Soc. Am. 2018;143(5): EL405-EL411.
4. Potty, G.R., Miller, J.H., Effect of Shear on Modal Arrival Times. IEEE JOE. 2020;45(1):103-115.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0067

Distortions of the vector sensor measurements by diffraction ofsound
Oleg A. GODIN 1
1

Physics Department, Naval Postgraduate School, Monterey, CA 93943, USA

ABSTRACT
Deployment of acoustic receivers from mobile, autonomous platforms offers significant new opportunities
for long-term, cost-effective observations of acoustic fields in the ocean and atmosphere. In ocean acoustics,
the autonomous platforms include unmanned underwater vehicles and float-equipped moorings. Acoustic
observations from free-flying stratospheric and tropospheric balloons play an increasingly important role inatmospheric sciences. The acoustic quantities measured by a mounted sensor differ from their values in thefree field because of wave scattering from the platform carrying the sensor. While the effect of large“platforms” such as solid ground, seabed, or ship hull on mounted sensors has been studied in some detail,measurement distortions due to compact platforms, dimensions of which are of the order of acoustic
wavelength or smaller, remain poorly understood. Using platforms with simple geometric shapes tostreamline the diffraction analysis, this paper quantifies the effects of acoustically compact platforms onscalar and vector sensors. It is found that vector sensors are more susceptible to the distortions than pressure
sensors. Strong distortions of particle velocity are likely to occur under conditions representative of theatmospheric and underwater applications. Possible approaches to compensation of the distortions andretrieval of the free-field acoustic quantities will be discussed.
Keywords: Vector Sensors, Sound Scattering, Infrasound

1. INTRODUCTION

Measured acoustic quantities deviate from their ambient values because of the sound diffractionby, or scattering from, the platform used to deploy the sensors in the water column. In underwater
acoustics, the relation between ambient fields and the measurements made with mounted hydrophones
and vector sensors is usually studied with the platform modeled as an infinite plane or a layered
structure. This is a good approximation at high- and mid-frequencies and for hull-mounted sensors.
However, at the low frequencies of primary interest in noise interferometry and seismo-acoustics, theentire platform may be small compared to the wavelength. The platform may be acoustically compact
even at mid-frequencies when sensors are carried by gliders or autonomous underwater vehicles. The
relation between ambient acoustic fields and measurements made by various sensors mounted on acompact underwater platform has been investigate theoretically in the recent paper (1) in ageometrically simple setting that admits an analytic treatment of sound diffraction. The present paper
expands the analysis presented in (1) to include balloon-borne infrasound sensors in the atmosphere.
There is a growing interest in the atmospheric sciences community in supplementing ground-based
and satellite observations by measurements at different altitudes in the atmosphere by placing various
sensors, including microbarometers and accelerometers, on free-flying, long-living balloons. Placing
additional sensors at stratospheric heights promises new insights into acoustic-gravity wave fields inthe atmosphere and their coupling to physical process in the ocean, solid earth, and ice shelves as wellas advances in detection and characterization of natural and man-made infrasonic sources. Using
balloon-borne acoustic-gravity wave and infrasound sensors is also actively studied for explorationof Venus and other planets. While allowing the measurements to be made at desired altitudes andsuppressing the flow noise by moving with the wind, balloons also scatter waves and inevitably distort
the ambient wave field. It is demonstrated in the present paper that balloon-induced measurement
distortions are rather different from the well-understood effect of a rigid boundary on ground-based1

oagodin@nps.edu

sensors.

2. CONCLUSIONS

It is sometimes argued, incorrectly, that the spatial scale of acoustic field variation is of the orderof wavelength, and therefore small probes do not distort the ambient field. In fact, diffraction cansignificantly affect low-frequency acoustic fields in a vicinity of a compact object even when theobject is small compared to the wavelength.
In the underwater acoustics context, we have shown that strong, frequency-dependent pressure
perturbations occur in the vicinity of a small sphere when its compressibility is large compared to thatof water. This leads to strong distortions in measurements when a hydrophone is placed near floats
that employ thin, gas-filled shells. On the other hand, floats made of syntactic foam-type materials
create only modest distortions of the free-field, low-frequency acoustic pressure values. Frequency
dependence of the measurement distortions that occur on compact platforms proves to be rather
different than in the previously studied limit of platforms that are large compared to wavelength.
Scattering-induced low-frequency perturbations in oscillatory velocity and acceleration are
generally much larger than pressure perturbations in the vicinity of sub-wavelength objects, leading
to strong distortions of vector sensor measurements. Distortions of measurements of the tangential tothe platform and normal components of oscillatory velocity by mounted vector sensors prove to berather different. As a result, mounted vector sensors correctly measure neither the direction nor
amplitude of the free-field oscillatory velocity. We have shown, however, that, away from resonances,
the scattering-induced distortions in low-frequency vector sensor measurements can be readilycorrected for, without any a priori information about the ambient acoustic field.
In application to balloon-borne infrasound sensors in the atmosphere, neither zero- and superpressure helium balloons nor high-altitude hot air balloons are predicted to support a Minnaert
resonance. This is beneficial for broadband wave measurements. Platform-induced distortions of theambient wave field are found to be non-negligible for stratospheric balloons. This is illustrated in
Figure 1.

Figure 1 – Frequency dependence of the response of a balloon-borne radial velocity
sensor
The figure shows the ratio of measured and ambient values of the radial velocity for different wave

frequencies and three directions of a plane wave arrival: from nadir (red curves), 75° from nadir(green) and 60° from zenith (blue); k is the acoustic wavenumber. The vector sensor is located beneatha zero-pressure spherical helium balloon of radius a at the distance r = 1.5a (solid lines) or r = 2a(dashed lines) from its center (panels a and b) and r = a (solid lines) or r = 3a (dashed lines) from its
center (panels c and d). Panels b and d show details of low-frequency behavior.
The natural frequencies of gondola suspension that fall into the infrasonic frequency range are an
important factor controlling ambient wave field distortion by the high-altitude balloons. Infrasounddiffraction and balloon vibration effects should be considered together with propagation (2, 3) andwind-induced absorption anisotropy (4) effects in order to understand the differences between
measurements (2, 3) that are made with airborne and ground-level sensors.
Distortion of the ambient acoustic field by a sensor platform does not necessarily degrade thesensor performance. When understood and accurately modeled, amplitude distortions can be exploited
to increase sensor sensitivity in a desired frequency band. Low-frequency phase distortions can be
exploited to improve source bearing estimates.

ACKNOWLEDGEMENTS

This work was supported by the Office of Naval Research, Award Nos. N00014-19WX00462 andN00014-22WX01287.

REFERENCES

1. Godin OA. Fidelity of low-frequency underwater acoustic measurements by sensors mounted on compact
platforms. J. Acoust. Soc. Am. 2019; 146(5): EL405–EL411.
2. Bowman DC, Lees JM. A comparison of the ocean microbarom recorded on the ground and in thestratosphere. J. Geophys. Res. Atmos. 2017; 122(18): 9773–9782.
3. Bowman DC, Albert SA. Acoustic event location and background noise characterization on a free flying
infrasound sensor network in the stratosphere. Geophys. J. Int. 2018; 213(3): 1524–1535.
4. Godin OA. Dissipation of acoustic-gravity waves: An asymptotic approach. J. Acoust. Soc. Am. 2014;
136(6): EL411–EL417.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0306

Underwater acoustic communication using a vector sensor
from a moving source in shallow water
Kang-Hoon CHOI1; Jee Woong CHOI2; Sunhyo KIM3; Peter H. DAHL4; David R. DALL’OSTO5
1, 2
2
3

Marine Science and Convergence Engineering, Hanyang University, Republic of Korea

Department of Military Information Engineering, Hanyang University ERICA, Republic of Korea

Maritime Security Research Center, Korea Institute of Ocean Science and Technology, Republic of Korea
4, 5Applied Physics Laboratory, University of Washington, Seattle, USA

ABSTRACT
For the optimal performance of underwater acoustic communication, it is important to minimize the effect of
the Inter-Symbol Interference due to the delay spread of the multipath channels formed by multiple
interactions with the ocean boundaries. The multipath channels have time-varying characteristics due totemporal variations of ocean environments. Especially, the communication channels on moving platforms
have high Doppler frequency shifts and low channel coherence times due to spatial environment changes.
Recently, acoustic vector sensors have been applied to underwater acoustic communication. A vector sensor
can simultaneously measure x, y, and z-components of acoustic vector quantities as well as acoustic pressure
with different channel characteristics and signal-to-noise ratios, and therefore, it can be used as a single-input
multiple-output communication system. Underwater acoustic communication experiments using an acoustic
vector receiver were conducted off the coast of Geoje island in Southeast Korea during the Korea
Reverberation Experiment (KOREX-17) in May 2017. In this talk, communication techniques that can givethe best performance using acoustic pressure signal and particle velocity signals measured by a single vector
sensor in time-varying channel are investigated.
Keywords: Underwater acoustic communication, Acoustic vector sensor, Directionality.

1. INTRODUCTION
In shallow-water acoustic communication, various techniques utilizing spatial diversity gain, suchas Multichannel combining with Decision feedback equalizer (M -DFE) (1-3) and multichannel Time
Reversal combining with Decision Feedback Equalizer (TR-DFE) (4-7), have been studied toovercome the large delay spread induced by Inter-symbol Interference (ISI) by time-varying
multipaths in shallow water. A spatially separated receiver array is required to improve spatial
diversity gain, For this reason, a receiver array used in an underwater communication system isdifficult to be accommodated in a compact underwater communication platform.
Acoustic vector sensor can measure three orthogonal components of acoustic vector quantities,
such as acoustic particle velocity and acceleration, as well as acoustic pressure, making it a goodalternative for applications on small platforms (8-10). In the case of a separated hydrophone array,
the acoustic pressure signal received from each channel has multipath characteristics with different
delay times and amplitudes. On the other hands, vector channels obtained a single vector sensor
exhibit the channel characteristics with different amplitudes but similar delay times. Therefore, whenusing a single vector sensor, the communication performance can be improved only through the gainby the directionality of the vector channel, i.e., Directional diversity gain. In this paper, the underwaterchoikh0210@hanyang.ac.kr
choijw@hanyang.ac.kr
3
sunhyo@kiost.ac.kr
4
dahl@apl.washington.edu
5dallosto@apl.washington.edu
12

acoustic communication performance using a single vector sensor was verified through exper imental
data acquired during the KOREX-17.

2. COMMUNICATION EXPERIMENT USING A SINGLE VECTOR SENSOR
Underwater acoustic communication experiment was conducted off the south coast of Geoje islandof Korea in May 2017. An omni-directional transducer (Neptune D-11) was used as an acoustic source,which was deployed at a depth of 13 m from the R/V Mirae and then towed away from the vector
sensor system while transmitting communication signals. The communication signals were measured
by in-situ acoustic vector receiver of Applied Physics Lab., Univ. of Washington, for which the
combined pressure and accelerometer-based vector sensor was positioned 1 m above the seafloor at awater depth of 29 m. During the experiment, the horizontal range between the source and the vector
sensor measured by the GPS increased from 143 to 670 m.
The communication packet was composed of a linear frequency-modulated (LFM) signal andcommunication sequence. The LFM signal had signal duration of 0.1 s and bandwidth of 2.5-3.5 kHz,
which was used to synchronize the communication sequence. The communication sequence is aquadrature phase-shift keying (QPSK) modulated signal lasting 8 s with a carrier frequency of 3 kHz
and a symbol rate of 500 symbols/s. The total communication signals consisted of 4000 symbols
including a sequence of 400 training symbols prior to the information symbols.

3. RESULTS
In order to compensate for the Doppler shift caused by the movement of the acoustic source, an
initial Doppler shift was estimated from the training symbols of the acoustic pressure signal, thereceived acoustic pressure and the three components of acoustic particle velocities were resampled in
passband. After that, a noise normalization process was performed since the noise levels of theacoustic pressure and the particle velocity channels were different (8). Then, three types of time
reversal techniques were used for communication demodulation: Passive time reversal (PTR), Blockbased time reversal (BTR) (4, 11-13) and Bidirectional block-based time reversal (BiBTR). In a timevarying channel environment, the communication performance of the PTR may be degraded, and in
this case, the BTR generally performs better than the PTR. Because the channel impulse responses ofparticle velocities and acoustic pressure measured at a single point have similar delay times,
directional diversity of particle velocity channels can provide relatively lower gains than spatial
diversity by spatially separated receiver array.

Figure 1 – The comparison of communication performances of (a) Passive time reversal withresampling process, (b) Block-based time reversal and (c) Bidirectional block-based time reversal
obtained with a vector sensor for a Doppler shift of -0.62 Hz.
In this talk, we propose BiBTR, which combines BTR and Bidirectional equalization (14). The
Bidirectional equalization can exploit the difference in error propagation of nonlinear equalizer as aform of diversity, as it combines the soft outputs of forward and backward equalizations. Figure 1
shows the comparison of communication performances by three types of time reversal methods using
acoustic pressure and particle velocity signals in the case of Doppler shift of -0.63 Hz. Delay spreadof the pressure signal channel was ~12 ms in this case. In all three methods, a single-phase tracking
and Decision Feedback Equalization (DFE) with adaptive algorithm of Recursive least squares wereapplied. The number of feedforward filter taps in the DFE was set to sufficiently cover the channel
delay, and the number of feedback filter taps was half the number of the feedforward filter taps. The
block size of symbol for the BTR was 200 symbols (0.4 s). The results showed that the BiBTR
communication system with resampling process provides the best performance with error -free and an
output signal-to-noise ratio (SNR) of 14.4 dB.

ACKNOWLEDGEMENTS
This work was supported by the Agency for Defense Development, Korea, under Grant
UD200010DD. The APL-UW team was funded by the U.S. Office of Naval Research.

REFERENCES
1. Kilfoyle DB, Baggeroer AB. The state of the art in underwater acoustic telemetry. IEEE J Ocean Eng.
2000;25(1):4–27.
2. Stojanovic M, Catipovic J, Proakis JG. Adaptive multichannel combining and equalization for underwater
acoustic communications. J Acoust Soc Am. 1993;94(3):1621–1631.
3. Stojanovic M, Catipovic J, Proakis JG. Reduced-complexity spatial and temporal processing of underwater
acoustic communication signals. J Acoust Soc Am. 1995;98(2):961–972.
4. Song HC. Peer-reviewed technical communication: an overview of underwater time-reversal
communication. IEEE J Ocean Eng. 2016;41(3):644–655.
5. Yang TC. Temporal resolutions of time-reversal and passive-phase conjugation for underwater acoustic
communications. IEEE J Ocean Eng. 2003;28(2):229–245.
6. Yang TC. Differences between passive-phase conjugation and decision-feedback equalizer for underwater
acoustic communications. IEEE J Ocean Eng. 2004;29(2):472–487.
7. Stojanovic M. Retrofocusing techniques for high rate acoustic communications. J Acoust Soc Am.
2005;117(3):1173–1185.
8. Song A, Abdi A, Badiey M, Hursky P. Experimental demonstration of underwater acoustic communication
by vector sensors. IEEE J Ocean Eng. 2011;36(3):454–461.
9. Wang C, Yin J, Huang D, Zielinski A. Experimental demonstration of differential OFDM underwater
acoustic communication with acoustic vector sensor. Appl Acoust. 2014;91:1–5.
10. Kim S, Kim H, Jung S, Choi JW. Time reversal communication using vertical particle velocity andpressure signals in shallow water. Ad Hoc Netw. 2019;89:161–169.
11. Song HC. Time reversal communication with a mobile source (L). J Acoust Soc Am. 2013;134:2623–
2626.
12. Song HC, Kuperman WA, Hodgkiss WS. Basin-scale time reversal communications. J Acoust Soc Amer.
2009;125:212–217.
13. Song HC. Time reversal communications in a time-varying sparse channel. J Acoust Soc Amer.
2011;130:EL161–EL166.
14. Song HC. Bidirectional equalization for underwater acoustic communication. J Acoust Soc Amer.
2012;131:EL342–EL347.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0629

Potential and kinetic energy of underwater noise measured below a
passing vessel and response to sub-bottom layeringPeter H. DAHL1 and David R. DALL’OSTO1
1

Applied Physics Laboratory University of Washington, USA

ABSTRACTFor underwater sound energy emitted by a passing vessel and received at a relatively steep angles, such as
vessel directly overhead, there can be a significant excess in potential acoustic energy relative to kinetic
energy with the opposite also occurring.
Observations when expressed as a ratio of kinetic to potential
energy in decibels are interpretable upon inspection, yielding an estimate of the sensor height above theseabed, and information on properties of the seabed. The effect was studied as part of the recently completed
experiment on the New England Mud Patch. The R/V Neil Armstrong underwent controlled transits directly
overhead a vector sensor, positioned about 1. 5 m above the seabed. A model for kinetic and potential energy
developed from method of images combined with a layered seabed, is used to invert the data. The lowspeed mud-layer is identified along with higher-speed transition layer separating the mud substrate from asediment basement. The inverted seabed parameters consistent with other recent studies. Observations madein Puget Sound over a higher-impedance seabed near the Kingston Arch are also presented to illustrate thestanding wave features of such data.
Keywords: Underwater Acoustics, Kinetic and Potential Energy

1. INTRODUCTION

When a vessel passes directly above, it can dominate the underwater sound field below. At closest
point of approach (CPA) the sound field is reinforced by multiple reflections from the seafloor andsea-surface establishing a standing wave. Under such CPA conditions, potential energy can be an orderof magnitude greater than kinetic energy in certain frequency bands. Given that ship noise is abroadband source, the ratio between potential, E p, and kinetic, Ek , energy presents a frequency pattern,linked primarily to the position above the seafloor and the geoacoustic properties and structure of themarine sediments.
Here, such conditions are addressed in controlled experiments where two research vessels conducttransits directly above a vector sensor, positioned about 1.5 m above the seabed, resulting in a CPA
range of 0 (Fig. 1). One case involves R/V Neil Armstrong (length 73 m) operating in waters of depth
75--80 m within the New England Mud Patch (NEMP) and the other R/V Robertson (length 18 m)
operating in waters of depth 42 m in Puget Sound. The noise in the 50-2000 Hz band shows a
distinct pattern in the energy ratio; differences are due to the sediment type and layering within theseabed at the two experimental sites.

2. MODEL

The method of images is used to compute an approximate Green's function g(z,z0 ,f) for a harmonicsource (time dependence exp(-iωt) being suppressed) near the air-water interface depth at z0, andreceiver directly below it at depth z=z r , within a waveguide of depth H (see Fig. 1). This is modified
from an exact form (1). for a rigid, or infinite impedance, boundary condition at the seabed,by setting the receiver range equal to 0 and replacing the reflection coefficient R b = 1 with finite
impedance, frequency dependent form R b(f), to be evaluated at normal incidence (grazing angle 90 o).
The impedance translation theorem (2) is used to construct the plane-wave reflection coefficientfor fluid seabed consisting of two layers of thickness L i, i = 1,2 characterized by density ρ i and soundspeed, c i, and terminated by a half space characterized by ρ3 and c3 .

Figure 1 (a) Closest point of approach (CPA) of
R/V Jack Robertson directly over the vector
sensor platform (red triangle). Importantparameters of the problem identified: H water
depth, z r vector receiver depth, and L1 , L2 layer
thicknesses associated with first and second subbottom reflections, respectively. (b) Picture of
IVAR-2 lander with vector receiver highlighted
in yellow, ∆z is the height of the sensor above
the bottom.

3. INVERSION AND RESULTS

For comparison with the deterministic model the vertical component of kinetic energy Ekz is parsed
into active and reactive components, their sum called coherent vertical kinetic energy. This is kinetic
energy associated with the combination of vertical velocity precisely in phase with pressure (active)
and precisely in-quadrature or 90o out of phase with pressure (reactive) (3). We notionally define thedifference between total and coherent kinetic energy as diffuse kinetic energy, although further
interpretation of this quantity is beyond the scope of this study, and it is not fundamental tounderstanding these results. The process of isolating coherent vertical kinetic energy in noisy data
involves the squared coherence between pressure and vertical velocity.
A geoacoustic inversion is applied to the ratio data expressed in decibels as function of frequency,
using a Bayesian framework (4), with model-data mismatch equal to the decibel difference between
each sequence which tends to be approximately centered about 0 dB.
Results of inverting the observations from Puget Sound (water sound speed 1478 m/s) reproduces
key features of the data (Fig. 2a), such as approximate decibel range and frequency variation. The
model is based on the following inverted geoacoustic parameters: c 1=1750 m/s L1 = 3.65 m,
c 2 = 2230 m/s L 2 = 0.45 m, c3 = 2720 m/s, which is consistent with a shallow Holocene (5) sedimentlayer over harder substrate.
For the NEMP observations Sound (water sound speed 1486 m/s) again the model tends toreproduce key features of the data (Fig. 2b). The model is based on the following inverted
geoacoustic parameters: c1 =1440 m/s L 1 = 10 m, c2 = 1590 m/s L2 = 2.6 m, c3 = 1640 m/s. A
prominent feature of the seabed in this location is the low-speed mud layer commencing at the waterseabed interface with thickness of order 10 m (6).

Data

(a)

10

Model

0

Ratio KEz to PE, dB

-10

200

400

600

800

1000

1200

1400

1600

Frequency (Hz)

Data

(b)

10

Model

0

Ratio KEz to PE, dB

-10

200

400

600

800

1000

1200

1400

1600

Frequency (Hz)Figure 2. (a) Results for Puget Sound: Model (red, dashed line) for ratio E kz to E p as a functionof frequency and expressed in decibels compared with observations (black line). (b) Results for the
NEMP.

ACKNOWLEDGEMENTS

This work was supported by the U. S. Office of Naval Research

REFERENCES

1. Frisk G.V., Ocean an seabed acoustics: a theory of wave propagation, Prentice Hall, New
Jersey,1994 pp 80--83.
2. Brekhovskikh, L.M. , Waves in Layered Media, Academic Press, 1960, pp. 56-60.
3. Mann and Tichy Acoustic intensity analysis: Distinguishing energy propagation and wave‐front
propagation, The Journal of the Acoustical Society of America, 90 pp 20-25, 1991
4. Bonnel, J., S. Dosso and R. Chapman, Bayesian geoacoustic inversion of single hydrophonelight bulb data using using warping dispersion analysis, Journal of the Acoustical Society ofAmerica, 134, pp 120-130, 2013.
5. Mace, G. M, and K. M. Keranen, Oblique fault systems crossing the Seattle basin: Geophysicalevidence for additional shallow fault systems in the central Puget lowland, " Journal ofGeophysical Research, Solid Earth, 117, 2012
6. Wilson P. W, D. P. Knobles and T. B. Neilsen, "Guest editorial: an overview of the seabedcharacterization experiment, IEEE Journal of Oceanic Engineering, 45, pp 1-13, 2020

ABS-0669

Modal-MUSIC for passive mode extraction on a partially-spanning array
F. Hunter AKINS1 and W.A. KUPERMAN1
1 Scripps Institution of Oceanography, UC San Diego, U.S.A.

ABSTRACT
Modes extracted from data can be used as input for geoacoustic inversion or for source localization. There are
a number of active source methods for mode extraction. However, passive mode extraction from surface noise
has been limited to the case of full water-column spanning arrays, in which case the orthogonality of the modes
permits their separation with the singular value decomposition of the covariance matrix. With knowledge of thesound speed profile (SSP) in the water column, the Multiple Signal Classification (MUSIC) algorithm can beadapted to the problem of mode (rather than plane wave) extraction: “modal MUSIC”. Modal-MUSIC has norequirement that the modes be orthogonal on the array, and the modes can theoretically be extracted whenever
the number of array elements exceeds the number of modes. The method is demonstrated by extracting modes
from simulated and real data.

Keywords: Normal modes, passive mode extraction, underwater acoustics

1

INTRODUCTION

Normal modes are a convenient and sparse description of low-frequency underwater acoustic fields. In a rangeindependent, knowledge of the normal modes Φi (z) and their associated horizontal wavenumbers ki is sufficient
to model the field between any source-receiver pair in the water. Mode extraction methods estimate the normal
modes and the wavenumbers from data, either using the field produced by ship/surface noise or by an active
source. Once the modes have been extracted, they can be used as input for applications of localization andgeoacoustic inversion [1, 2, 3, 4]. Modal-MUSIC is a passive mode extraction method for partially-spanning
arrays, using noise from a ship-of-opportunity.
From a modeling perspective, normal modes are typically computed by solving a boundary value problem(BVP) framed by the sound speed profile (SSP) in the water column, the surface boundary condition (which canbe modeled as pressure release), and a geoacoustic model consisting of a physical description of the medium
beneath the seafloor. The shooting method [5] is one numerical approach that solves the BVP by numericallyintegrating solutions down from the surface for candidate eigenvalues (wavenumbers). The value of the candidate solution is compared to the boundary conditions at the bottom, and the eigenvalue is adjusted until the
candidate mode satisfies the physics enforced by a geoacoustic model. Rather than using a geacoustic model,
modal-MUSIC compares candidate solutions from shooting to the data from a ship-of-opportunity using theMUSIC algorithm. This requires knowledge of the SSP from the surface down to the bottom of the array. The
modes supported by the waveguide and excited by the source show up as peaks in the modal-MUSIC spectrum.

2

MUSIC

MUSIC is a subspace-based array processing method for identifying and classifying an unknown number ofradiating sources [9]. The data are assumed to follow a linear modeld = As + n ,(1)

where d is a vector containing the measurements made on the array, A is a matrix whose columns are thesignal waveforms sampled by the array, s is a vector of the source amplitudes, and n is the noise, which isassumed to be uncorrelated white Gaussian noise with covariance matrix σn2 I (II is the identity matrix). If there
are N array sensors and M sources, then d is N × 1, A is N × M, s is M × 1, and n is N × 1. For the plane
wave estimation problem, the jth column of A is the plane wave associated with the jth source sampled at thearray locations.
The data covariance matrix obeys
A + σn2 I ,E(dd d H ) = AH E(ss sH )A(2)

where E denotes expectation and (·)H denotes complex transpose. Assuming that M < N and that none of thesources are fully correlated, there will be N − M repeating eigenvalues of value σn2 [9]. The corresponding“noise eigenvectors” will be orthogonal to the columns of A . Collecting the noise eigenvectors into a matrix
E n and denote the plane wave associated with source angle θ sampled on the array as a (θ ), then the MUSIC
spectrum is−1
PMU (θ ) = a (θ )H E n E H,(3)
n a (θ )and will have peaks at the source angles.

3

MODAL-MUSICIn a range-independent underwater acoustic environment, the signal radiated by a moving source, filtered to asingle frequency, can be written in the same form (1). However, now the columns of A are the normal modes
evaluated on the array rather than plane waves, and the signal time series s (t) now contains time dependence ofeach mode. Viewed another way, each mode can be thought of as a virtual source that has a time dependence
determined by its horizontal wavenumber and the motion of the source. The waveform produced by each virtual
source on the array is no longer a plane wave, but rather the associated normal mode wavefunction sampled at
the array depths.
Although for a coherent source/environment the modes are statistically dependent, as the source moves inrange they will become uncorrelated. This means that E(sss H ) will have full rank as long as the source moves
a significant range (see [6] for details). Therefore the eigenvalues of the covariance matrix due to a moving
source will allow for identification of the noise subspace (which is then organized as the column space of thematrix E n ). The noise subspace will be orthogonal to the normal modes Φi supported by the waveguide andexcited by the source.
Finally, to compute the modal-MUSIC spectrum as (3), we need a means of computing the “array manifold”.
To do so, we use knowledge of the water column sound speed profile to numerically integrate normal modes
from the pressure release surface down to the bottom of the array via the shooting method [5]. This allows us
to compute Φ(kr ) for any horizontal wavenumber kr on a mesh in the water column. By sampling the candidate
normal modes at the array depths, stored in a vector Φ (kr ), the modal-MUSIC spectrum can be computed as−1
PMoMU (kr ) = Φ (kr )H E n E H.
n Φ (kr )(4)

The spectrum will have peaks at the horizontal wavenumbers of the medium. No information on the source
range is required for the method to work, besides the knowledge that it has transited a significant range. Therefore the method is suitable for application to noise from a ship-of-opportunity.

4

Final remarks

The shooting method combined with knowledge of the water column SSP allows one to compute normal modesin the water column for any horizontal wavenumber kr . Typically, a geoacoustic model is then used to selectthe normal modes that satisfy the physical constraints imposed by the seafloor. By viewing each normal mode
excited in the waveguide as a virtual source, the mode extraction problem becomes a signal estimation problem
relevant for the MUSIC algorithm. Modal-MUSIC combines the shooting method with MUSIC, using datafrom a ship-of-opportunity to select the modes that are actually present in the waveguide (as opposed to using
a geoacoustic model). The extracted modes can be used for source localization or geoacoustic inversion.

ACKNOWLEDGEMENTS
This work was funded by the TFO program of the United States Office of Naval Research. Bill Hodgkiss andDave Ensberg facilitated data access for the SWellEx-96 experiment.

REFERENCES[1] Bonnel J, Lin YT, Eleftherakis D, Goff JA, Dosso S, Chapman R, et al. Geoacoustic inversion on the New
England Mud Patch using warping and dispersion curves of high-order modes. The Journal of the AcousticalSociety of America. 2018;143(5):EL405–EL411. Available from: https://doi.org/10.1121/1.5039769.
[2] Rajan SD, Lynch JF, Frisk GV. Perturbative inversion methods for obtaining bottom geoacoustic parametersin shallow water. The Journal of the Acoustical Society of America. 1987;82(3):998–1017. Available from:https://doi.org/10.1121/1.395300.
[3] Yang TC. Source depth estimation based on synthetic aperture beamfoming for a moving source. The
Journal of the Acoustical Society of America. 2015;138(3):1678–1686. Available from: https://doi.
org/10.1121/1.4929748.
[4] Niu H, Gerstoft P, Zhang R, Li Z, Gong Z, Wang H. Mode separation with one hydrophone in shallow
water: A sparse Bayesian learning approach based on phase speed. The Journal of the Acoustical Societyof America. 2021;149(6):4366–4376. Available from: https://doi.org/10.1121/10.0005312.
[5] Porter MB, Reiss EL. A Note on the Relationship between Finite-Difference and Shooting Methods for
ODE Eigenvalue Problems. SIAM Journal on Numerical Analysis. 1986;23(5):1034–1039. Available from:https://doi.org/10.1137/0723071.
[6] Neilsen TB, Westwood EK. Extraction of acoustic normal mode depth functions using vertical line arraydata. The Journal of the Acoustical Society of America. 2002;111(2):748–756. Available from: https://doi.org/10.1121/1.1432982.
[7] Bonnel J, Thode A, Wright D, Chapman R. Nonlinear time-warping made simple: A step-by-step tutorial
on underwater acoustic modal separation with a single hydrophone. The Journal of the Acoustical Societyof America. 2020;147(3):1897–1926. Available from: https://doi.org/10.1121/10.0000937.
[8] Frisk GV, Becker KM, Rajan SD, Sellers CJ, von der Heydt K, Smith CM, et al. Modal Mapping Experiment and Geoacoustic Inversion Using Sonobuoys. IEEE Journal of Oceanic Engineering. 2015;40(3):607–
620.
[9] Schmidt R. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas andPropagation. 1986;34(3):276–280.

PROCEEDINGS of the24th International Congress on Acoustics
October 24 to 28, 2022, in Gyeongju, Korea
Extended abstractABS-0706

Enhancement of Green’s function using the waveguide invariant
theoryDaehwan KIM1; Donghyeon KIM2; Jeasoo KIM3
1
2

Department of Ocean Engineering, Korea Maritime and Ocean University, Korea

Department of Convergence Study on the Ocean Science and Technology, Korea Maritime and Ocean
University, Korea
3

Department of Ocean Engineering, Korea Maritime and Ocean University, Korea

ABSTRACTThis paper presents a method to achieve signal-to-noise ratio (SNR) improvements of the Green’s function.
This work is motivated by a recent paper [Song and Byun, J. Acoust. Soc. Am 149, 2150-2158 (2020)] thatderived an analytic relation between Green’ s functions at adjacent ranges using the waveguide invariant, 𝛽.
For a known value of 𝛽, Green’s functions from multiple ranges can be extrapolated to a single range.
Subsequently, the extrapolated Green’s functions are coherently combined to enhance a Green’s function
with low SNR. The proposed method is demonstrated for a ship of opportunity moving radially away fromthe vertical array at ranges of 2.06-2.16 km in 100-m water depth.
Keywords: Green’s function, Waveguide invariant, SAVEX15

1. INTRODUCTION
Acoustic waves in an ocean waveguide are greatly influenced by the characteristics of theunderwater sound channel. Thus, the environment’s physical characteristics (i.e., the Green’s
function) between the source and receiver is typically required for various remote sensing applications(1). Ray-based blind deconvolution (RBD) can be an excellent method to estimate the Green’s function
with low computational complexity (2-4). However, usually the signal of interest is recorded alongwith undesired signals (noise), as a result, it can be challenging to estimate the Green’s function
properly.
In this letter, we present a method for improving the signal-to-noise ratio (SNR) of the Green’s
function using the waveguide invariant. This work is motivated by a recent report that derived a simple
analytic relation using the waveguide invariant, denoted by 𝛽(5,6), to extrapolate Green’s functions
to adjacent ranges(7). Our approach is to exploit this relationship to extrapolate Green’s functions
from adjacent ranges to a single range. The extrapolated Green’s functions then can be coherently
combined to enhance a Green’s function with low SNR. The performance of the proposed method is
examined through a recent shallow water experiment conducted in the northeastern East China Sea(ECS) [Shallow water Acoustic Variability EXperiment (SAVEX15)].

2. SAVEX15
SAVEX15 was conducted in the northeastern ECS using R/V Onnuri during May 2015(8). The
experimental site has a nearly flat sandy bottom and a water depth of approximately 100 m. Both fixed
and towed source transmissions were carried out to two moored vertical line arrays (VLAs) overranges of 1-10 km.
To investigate the approach proposed, we analyze a segment of data during a source-tow run on
JD 146 (May 26). The schematic of the source-tow run is illustrated in Figure 1(a). The bottom1

oceankim823@gmail.com
donghyeon.ual@gmail.com
3
jskim@kmou.ac.kr
2

moored VLA consisted of 16 elements spanning 56.25 m aperture with 3.75-m element spacing,covering about half the water column (from 25 to 81.25 m) in about 100-m deep water. The SSP
displayed in Figure 1(a) is an average of two CTD (conductivity, temperature, and depth) casts
collected around JD 146. The R/V Onnuri was towing two broadband sources (3-10 kHz and 12-32
kHz) simultaneously at a speed of 1.5 m/s along the radial track (2060-2160 m) as shown in Figure1(b). Here our interest is in the Green’s function from the R/V radiating low frequency rand noise(e.g., < 1 kHz) which did not interfere with the high frequency waveforms broadcast by the twocontrolled sources (3-10 kHz and 12-32 kHz)at 50-m depth.
The simulated Green’s function in the frequency band of interest (200-900 Hz) for a point sourcenear the surface (5 m) at range 2060 m is displayed in Figure 2(a). A normal mode program(9) is used
to simulate the Green’s function assuming a range independent environment with a simple half-space
sandy bottom whose geoacoustic parameters are indicated in Figure 1(a).
The Green’s function from the source of opportunity can be estimated via ray-based blind
deconvolution (RBD)(2-4). A 2-s window ship noise (200-900 Hz) was used to obtain the Green’s
function for the selected track in Figure 1(b). The Green’s function estimated along the VLA at 2060
m range is shown in Figure 2(b). Compared to the simulated Green’s function in Figure 2(a), the first
five wavefront arrival structures are in great agreement with simulated Green’s function, whereas thefollowing arrivals are highly attenuated and too weak to distinguish between noise.

Figure 1 – (a) Schematic of a source-tow run on JD 146 (May 26). (a) Global Positioning System (GPS) ship
track of the R/V Onnuri, transiting radially away from the VLA (black) at a speed of 1.5 m/s.

3. COMBINING THE GREEN’S FUNCTION
The combined Green’s function can be expressed as the sum of Green’s functions of adjacent
ranges extrapolated to a single range 𝑟! via waveguide invariant, as follows%

1𝑔" (𝑟! , 𝑡) =,[𝑔(𝑟# , 𝛼# 𝑡) ∗ 𝛿(𝑡 − Δr$ /c)]
𝑁+1
#&!

(1)

where 𝑁 is the number of adjacent ranges (𝑖 = 0 … 𝑁). The term in the bracket denotes the Green’s
functions extrapolated to 𝑟! by a two-step procedure(7): (1) resampling the Green’s function with afactor of 𝛼 = 1 + 𝛽(Δ𝑟/𝑟) and (2) a time-delay, where 𝑐 is the nominal speed of sound 1500 m/s.
In this paper, we take a Green’s function at range 2060 m as a reference (𝑟! ) and extrapolate 100m of Green’s functions to a closer range (i.e., Δ𝑟 < 0) to coherently combine, which requires atemporal dilation along with a time advance of the range varying eigenray arrival times. The combined
Green’s function is presented in Figure 2(c). Compared to the original in Figure 2(b), the eigenrayarrival times of the five early groups show good agreement.
The processing gain (PG) of combining the Green’s function can be expressed as a function of the
SNRs of the original and combined Green’s function [PG = 10 logASNR ' /SNR ()* E], where the SNR is
defined as the ratio of the signal power to the noise floor(10).We select a segment where the ray arrival
no longer arrives as the noise floor (𝑡 > 200 𝑚𝑠). Using 100-m of adjacent Green’s functions (i.e.,
𝑁 = 11), the performance is about 10 dB. The SNR improvement is remarkable, as the arrivals masked
by noise can be recognized.

Figure 2 – Green’s function at 2060-m range from the ship radiated noise (200-900 Hz) (a) model, (b)RBD, and (c) combined.

4. SUMMARY
The relationship between Green’s functions at adjacent ranges can be derived by the waveguideinvariant: 𝑔(𝑟 + Δ𝑟, 𝑡) ≈ 𝑔(𝑟, 𝛼𝑡) ∗ 𝛿(𝑡 − Δ𝑟/𝑐), where 𝛼 + 1 + 𝛽(Δ𝑟/𝑟). In this paper the feasibilityof improving the SNR of the Green’s function is investigated using this relationship. 100-m ofextrapolated Green’s function were coherently combined for enhancement of about 10 dB. The
proposed method was demonstrated for a ship of opportunity moving radially away from the verticalarray at 1.5 m/s at range 2060-2160 m in 100-m shallow water.

ACKNOWLEDGEMENTS
This work was supported by National R&D Program through the National Research Foundation of
Korea (NRF) funded by the Ministry of Science and ICT(2021M3F6A1085889).

REFERENCES
1. Dowling D, Sabra K. Acoustic remote sensing. Annu Rev Fluid Mech. 2015;47:221-243.
2. Sabra K, Dowling D. Ray-based blind deconvolution in ocean sound channels. J Acoust Soc Am.
2010;127(2):EL42-EL47.
3. Byun S, Verlinden C, Sabra K. Blind deconvolution of shipping sources in an ocean waveguide. J Acoust
Soc Am. 2017;141(2):797-807.
4. Song H, Cho C, Byun G, Kim J. Cascade of blind deconvolution and array invariant for robust sourcerange estimation. J Acoust Soc Am. 2017;141(5):3270-3273
5. Chuprov S. Interference structure of a sound field in a layered ocean. Acoustics of the Ocean edited by
Breakhovskik L, Andreevoi I. 1982:71-91
6. Jensen F, Kuperman W, Porter M, Schmidt H. Computational Ocean Acoustics. New York, USA:
Springer; 2011.
7. Song H, Byun G. Extrapolating Green's functions using the waveguide invariant theory. J Acoust Soc Am.
2020;147:2150-2158.
8. Song H, Cho C, Hodgkiss W, Nam S, Kim S, Kim B. Underwater sound channel in the northeastern East
China Sea. Ocean Eng. 2018;147:370-374 .
9. Porter M. The acoustic toolbox. available at http://oalib.hlsresearch.com/AcousticsToolbox/.
10. Durofchalk N, Sabra K. Analysis of the ray-based blind deconvolution algorithm for shipping sources. J
Acoust Soc Am. 2020;147(3):1927-1938.

ABS-0780

Deep learning-based dispersion curve detector in the spectrogram
Jongkwon CHOI(1) ; Youngmin CHOO(2) ; Keunhwa LEE(3)(1) Sejong University, Republic of Korea, jkchoi.sju@gmail.com(2) Sejong University, Republic of Korea, ychoo@sejong.ac.kr(3) Sejong University, Republic of Korea, nasalkh2@sejong.ac.kr

ABSTRACT
A time-warping transform is a nonlinear resampling method that compensates for dispersion curve and is used
for mode separation. This method has the advantage that mode separation is possible in a single hydrophone andit is usually robust against mismatch between the warping operator and the ocean environment. However, thistransform can degrade mode separation performance in the case where the observed mode pattern is completely
different from the ideal dispersion curve or where the source is unknown. The range mismatch also affects theaccuracy of mode separation. In this proceedings, a dispersion curve line detector based on deep learning is
proposed to evade these problems. The proposed network is based on a U-Net and the data of spectrograms
and corresponding dispersion curve are made by KRAKEN. We show the deep learning is useful in detecting adispersion curve.

Keywords: Normal Mode, Time-warping transform, Deep learning, U-Net, Segmentation

1

INTRODUCTION

The time-warping transform is the method for estimating the dispersion curve of an impulse signal with asingle hydrophone (1), that enables inversion of ocean environmental factors and source localization (2, 3, 4).
By the warping transform, the modal phase in the received field is transformed into the constant for time,
which makes the mode-picking easier. The warping method is known to be robust to environmental mismatch,but there may be a concern of performance degradation in environments such as strong reflection or refracting
environments. This is due to the warping operator based on the ideal waveguide (5). Also as another drawback,the time-warping transform needs some manual works through trial and error in order to separate the modes intime-frequency domain.
In this work, we propose a dispersion curve segmentation network that directly detects the dispersion curveof an impulsive source in a properly modal-differentiated spectrogram image. This network is based on so-called
model-guided deep learning (6), considering the difficulty of data collection and the lack of good data in theunderwater environments. U-Net (7) is chosen as basic network for deep-learning. The physics-informed data(or modeled data) guides the U-Net to estimate the dispersion curve. Considering the incorrect knowledges ofocean environmental factors, the datasets are simulated with KRAKEN in various ocean environments chosenrandomly. Such a procedure makes the deep-learning network robust to the environmental mismatch and can
improve the performance of generalization.
The outline of this proceedings is as follows; Section 2 describes U-Net network, datasets, and preprocessing. Section 3 shows the results of curve detection in the ocean environments with iso-speed profile andnegative gradient sound speed profile (8). Also, we shows the application to the real data. Section 4 is theconclusions.

(a) Sound speed profile in Pekeris environ- (b) Sound speed profile in themocline envi- (c) Source-receiver configuration and bottommentronment
properties

Figure 1. A sample space of the ocean environment. Ocean environmental properties are generated randomly.

2DEEP LEARNING NETWORK AND TRAINING STRATEGIES

2.1 Dataset
The datasets are generated with the normal mode program KRAKEN (9). We simulate them in the frequency
range of 50 to 200 Hz. The SNR is set to be 3 dB. Noisy data is found to be useful to strengthen therobustness of deep learning network. Two range-independent ocean environments are used for learning as shownin Figure 1. For each environment, we randomly select the ocean environmental factors like sound speed profile,bottom depth, geoacoustic property, source depth, receiver depth, source-receiver range. A total of 750 ocean
environments are prepared for the simulation in all cases. Note that this sample size is very small considering
the complexity of ocean environment.
2.1.1 Dataset: Data
The datasets are respectively generated from two different ocean environments, called Pekeris environment andthermocline environment in this proceedings. These environments are displayed in Figure 1. With the ocean
environmental factors selected randomly, KRAKEN performs Monte-Carlo simulation for two kinds of ocean
environments. When generating datasets, we remove the modes of small energy, which may be drowned out
in the noise in the long-range propagation. The time-frequency spectrograms are plotted with each mode, notonly all effective modes. For example, if there are three mode groups in an ocean environment, total four
spectrograms are generated.
2.1.2 Dataset: Preprocessing
A spectrogram is generated via a short-time Fourier transform (STFT) with 1024 frequency bins, 30 overlap
size, and Hamming window of 31 length for 0.6 second time snippet. The spectrogram is normalized by its
absolute highest peak. Using a spline interpolation, the image size of spectrogram is mapped into 128 by 128.

Table 1. Dataset configuration. Mean F1 score for multi-modal is evaluated with all effective mode curves andthe term of ‘single-modal’ means that of each mode curve.
Pekeris environment

Train set

Validation set

Test set

The pressure fieldA subset of modes

A subset of modes

A complete set of modes

The number of data

4156

461

1188

Mean F1 score

0.92

0.89

0.89

Mean F1 score for multi-modal

0.89

0.89

0.81

Mean F1 score for single-modal

0.93

0.89

0.90

Thermocline environment

Train set

Validation set

Test set

The pressure fieldA subset of modes

A subset of modes

A complete set of modes

The number of data

4298

477

1230

Mean F1 score

0.87

0.77

0.86

Mean F1 score for multi-modal

0.77

0.78

0.74

Mean F1 score for single-modal

0.88

0.77

0.88

2.1.3 Dataset: Label
The labeling (true frequency-time curve) can be directly calculated from the KRAKEN. We plot the frequencytime curves in the 128 by 128 image, where one pixel corresponds to one point in the curve.
2.2 Deep learning network: U-Net
U-Net with RGB channels is used as deep learning network for curve detection. The size of input and output
image in the network is the same as 128 by 128. Basically, the U-Net learns datasets proceeding in the directionof minimizing dice loss (10). The final layer of U-Net performs a pixel-level class classification via softmax.
To improve the learning ability, the image augmentation technique is applied with random translation of thespectrogram for the time and frequency axis. Learning rate, batch size, and number of epoch are 1e-5, 32, and500, respectively.
The datasets are split into 64% training sets, 16% validation sets, and 20% test sets, based on environmental
data. Table 1 shows the dataset configuration and the F1 score for each dataset. Note that the number of datasets
is different from the number of ocean environments generated since the spectrograms for each modes are usedas dataset as previously mentioned. From Table 1, it can be shown that the U-Net can be used as a good mode
curve detector in the frequency-time spectrogram.

3

RESULTSIn this section, we show the predicted results using test sets in the same environment, the generalized results
using the unseen environment, and a real data in unseen environment. All simulated test sets in this section are
assumed to have the horizontal distance of 10 km between the source and receiver. The bottom sound speed,
bottom density, and bottom attenuation are 1640 m/s, 1.62 g/cm3 , and 0.05 dB/λ , respectively (8). The water
depth is commonly 80 m and each sound speed profile is displayed in Figures 2, 4, and 6. In addition, theSNRs of those data is varying from 0, 3, and 6 dB to evaluate the performance of trained network in noisy
environments.

(a) Sound speed profile(b) Phase speed of modes(c) Group slowness of modes

Figure 2. Pekeris environment.

(a) Prediction results of PU-Net(b) Prediction results of TU-Net

Figure 3. Prediction in the Pekeris environment (red pixel: prediction, green pixel: true value). The black pixels
indicate completely matched pixels. In the figure, ‘dB’ means the SNR.
3.1 Test dataset in Pekeris environment
Figure 2 shows the sound speed profile and the mode characteristics of test dataset. The source and receiver
depth is assumed to be 70m and 10m. Five propagating modes excites without a refraction mode. Figure 3
shows the prediction results using an U-Net trained with Pekeris-based datasets (PU-Net) and an U-Net trainedwith thermocline-based datasets (TU-Net). The red line represents the predicted curve and the green line does
the true curve. The black line indicates pixels perfectly matching of prediction and true value. The PU-Net
shows better performance than the TU-Net for all cases. However, TU-Net predicts the dispersion curve well,especially for higher SNR, although it has not seen the Pekeris environment.

(a) Sound speed profile(b) Phase speed of modes(c) Group slowness of modes

Figure 4. Thermocline environment. Here, the red dotted line represents the maximum sound speed. In Figure4(b) and (c), each mode curve is plotted with solid line (blue: refraction mode, black: reflection mode).

(a) Prediction results of PU-Net(b) Prediction results of TU-Net

Figure 5. Prediction in the thermocline environment (red pixel: prediction, green pixel: true value). The black
pixels indicate completely matched pixels. In the figure, ‘dB’ means the SNR.
3.2 Test dataset in thermocline environment
In the thermocline environment, the sound speed profile and the mode characteristics of test dataset are shownin Figure 4. The modes under the red dotted line are refractive (Modes 1 and 2). This environment causes more
complex time-frequency pattern of modes (8). The source and receiver depth are assumed to be 10m and 75m.
Figure 5 shows the prediction results for two U-Nets (PU-Net and TU-Net) and SNRs. Different from previous
example, TU-Net has better performance than PU-Net. It is not perfect, but the TU-Net correctly estimates therefractive modes for higher SNRs. The PU-Net finds the mode-pattern but the F1-score is not as high as thatof TU-Net.

(a) Sound speed profile(b) Phase speed of modes(c) Group slowness of modes

Figure 6. Ocean environment with a negative gradient sound speed profile (8). Here, the red dotted line
represents the maximum sound speed. In Figure 6(b) and (c), each mode curve is plotted with solid line (blue:
refraction mode, black: reflection mode).

(a) Prediction results of PU-Net(b) Prediction results of TU-Net

Figure 7. Prediction in the ocean environment with a negative-gradient sound speed (red pixel: prediction, green
pixel: true value). The black pixels indicate completely matched pixels. In the figure, ‘dB’ means the SNR.
3.3 Test dataset in the ocean environment with a negative gradient sound speed profile
This section is to test the generalization performance of PU-Net and TU-Net. A negative gradient sound speed
profile is used to generate the test dataset as shown in Figure 6(a). The source and receiver depth are 40m and
70m. In Figure 6(b), 3 out of 8 modes are refractive modes represented by blue solid line. Figure 7 shows theprediction results. The PU-Net tends to be false-negative in a noisy environment as previous examples. On theother hand, the TU-Net is more pronounced in false-positive than the PU-Net. Also, there is a tendency not toclassify the segments of modes 1 and 2 for the high-frequency range. And, the F1-score of TU-Net is generally
higher.

(a) Spectrogram(b) Time-warping transform(c) PU-Net(d) TU-Net

Figure 8. Prediction results for right whale gunshot. The red solid line represents the prediction of each method.
3.4 Real data: Right whale gunshot
Finally, we evaluate that the trained U-Nets works well with real data. The used data is a right whale gunshot
data acquired by D. Wright in the southeastern Bering Sea in 2013 (4, 11). This original data has largest
energy independent of water propagating modes below 20Hz. It is found that this largest energy affects thenormalization of image and degenerates the performance of U-Net. So, a high pass filter with 25Hz cutoff
frequency is applied into the original data to remove the unnecessary part. Figure 8 shows the prediction resultsby time-warping transform, PU-Net and TU-Net. The deep-learning networks show better results than timewarping transform. Especially, the TU-Net finds all 4 modes as time-warping transform. The results of U-Nets
are more like an ideal dispersion curve and seem to provide rich information than the time-warping transform.

4

CONCLUSIONSIn the proceedings, we propose the U-Net extracting the dispersive curve of impulsive source from the spectrogram. Deep knowledges for estimating the dispersion curve are guided by underwater acoustic normal mode
propagation model, KRAKEN. This procedure of model-guided deep-learning will be more appropriate in theunderwater environments, where it is difficult to collect clean and complete data due to the environmental mismatch and data complexity. For three simulated data and one real data, the trained deep-learning network shows
good performance in the learned and unlearned environments. We expect that the proposed deep-learning procedure can complement the time-warping method for mode separation.

ACKNOWLEDGEMENTS
This work has been funded by the Ministry of Oceans and Fisheries of Korea under Contract No. 20210067.

REFERENCES[1] Bonnel J, Thode A, Wright D, Chapman R. Nonlinear time-warping made simple: A step-by-step tutorial
on underwater acoustic modal separation with a single hydrophone. The journal of the acoustical societyof America. 2020;147(3):1897-926.
[2] Ioana C, Jarrot A, Gervaise C, Stéphan Y, Quinquis A. Localization in underwater dispersive channels using the time-frequency-phase continuity of signals. IEEE Transactions on Signal Processing.
2010;58(8):4093-107.
[3] Michalopoulou ZH, Aunsri N. Environmental inversion using dispersion tracking in a shallow water environment. The Journal of the Acoustical Society of America. 2018;143(3):EL188-93.
[4] Thode A, Bonnel J, Thieury M, Fagan A, Verlinden C, Wright D, et al. Using nonlinear time warping toestimate North Pacific right whale calling depths in the Bering Sea. The Journal of the Acoustical Societyof America. 2017;141(5):3059-69.
[5] Brown MG. Time-warping in underwater acoustic waveguides. The Journal of the Acoustical Society ofAmerica. 2020;147(2):898-910.
[6] Willard J, Jia X, Xu S, Steinbach M, Kumar V. Integrating scientific knowledge with machine learning forengineering and environmental systems. ACM Computing Surveys (CSUR). 2021.
[7] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In:
International Conference on Medical image computing and computer-assisted intervention. Springer; 2015.
p. 234-41.
[8] Niu H, Gerstoft P, Zhang R, Li Z, Gong Z, Wang H. Mode separation with one hydrophone in shallow
water: A sparse Bayesian learning approach based on phase speed. The Journal of the Acoustical Societyof America. 2021;149(6):4366-76.
[9] Porter MB. The KRAKEN normal mode program. Naval Research Lab Washington DC; 1992.
[10] Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical
image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE; 2016. p. 565-71.
[11] Wright D. Simultaneous Identification of Four Mysticete Species in the Bering Sea Using Passive Acoustic
Monitoring Increases Confidence in Acoustic Identification of the Critically Endangered North Pacific Right
Whale (Eubalaena japonica) Final Report to the International Fund for Animal Welfare. Silver Spring, MD:
NOAA. 2015;63.