Range estimation of bowhead whale (Balaena mysticetus) calls in the Arctic using a single hydrophone

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part I: Theory and schemes

Robust informative acoustic predictions require precise knowledge of ocean physics, bathymetry, seabed, and acoustic parameters. However, in realistic applications, this information is uncertain due to sparse and heterogeneous measurements and complex ocean physics. Efficient techniques are thus needed to quantify these uncertainties and predict the stochastic acoustic wave fields. In this work, we derive and implement new stochastic differential equations that predict the acoustic pressure fields and their probability distributions. We start from the stochastic acoustic parabolic equation (PE) and employ the instantaneously-optimal Dynamically Orthogonal (DO) equations theory. We derive stochastic DO-PEs that dynamically reduce and march the dominant multi-dimensional uncertainties respecting the nonlinear governing equations and non-Gaussian statistics. We develop the dynamical reduced-order DO-PEs theory for the Narrow-Angle parabolic equation and implement numerical schemes for discretizing and integrating the stochastic acoustic fields.

Sources depth estimation for a tonal source by matching the interference structure in the arrival angle domain

A publication by McCargar and Zurk [J. Acoust. Soc. Am. 133(4), EL320-EL325 (2013)] introduced a passive source depth estimation method for a moving tonal source with a vertical line array (VLA), utilizing the depth-dependent modulation in the arrival angle domain caused by the interference between the direct and surface-reflected acoustic arrivals. Under the isovelocity approximation, this method can estimate the depth of sources at close ranges, but the depth estimation error will increase with the increase in source range, as the impact of the sound speed profile on sound propagation is ignored. This paper presents a theoretical formula for calculating the modeled interference structure in the arrival angle domain with the knowledge of the sound speed profile. By matching the measured interference structure obtained from the beamforming of the acoustic data received by the VLA with the modeled structure under different assumed source depths, the tonal source depth estimation is achieved, even for sources at the remote part of the direct arrival zone. The performance of this method is verified by simulation data, as well as experimental data radiated from a towed source and a non-cooperative passing ship.

Ecological inferences about marine mammals from passive acoustic data

Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.

Sea ice directs changes in bowhead whale phenology through the Bering Strait

BACKGROUND

Climate change is warming the Arctic faster than the rest of the planet. Shifts in whale migration timing have been linked to climate change in temperate and sub-Arctic regions, and evidence suggests Bering-Chukchi-Beaufort (BCB) bowhead whales (Balaena mysticetus) might be overwintering in the Canadian Beaufort Sea.

METHODS

We used an 11-year timeseries (spanning 2009-2021) of BCB bowhead whale presence in the southern Chukchi Sea (inferred from passive acoustic monitoring) to explore relationships between migration timing and sea ice in the Chukchi and Bering Seas.

RESULTS

Fall southward migration into the Bering Strait was delayed in years with less mean October Chukchi Sea ice area and earlier in years with greater sea ice area (p = 0.04, r² = 0.40). Greater mean October-December Bering Sea ice area resulted in longer absences between whales migrating south in the fall and north in the spring (p < 0.01, r² = 0.85). A stepwise shift after 2012-2013 shows some whales are remaining in southern Chukchi Sea rather than moving through the Bering Strait and into the northwestern Bering Sea for the winter. Spring northward migration into the southern Chukchi Sea was earlier in years with less mean January-March Chukchi Sea ice area and delayed in years with greater sea ice area (p < 0.01, r² = 0.82).

CONCLUSIONS

As sea ice continues to decline, northward spring-time migration could shift earlier or more bowhead whales may overwinter at summer feeding grounds. Changes to bowhead whale migration could increase the overlap with ships and impact Indigenous communities that rely on bowhead whales for nutritional and cultural subsistence.

Machine-learning-based simultaneous detection and ranging of impulsive baleen whale vocalizations using a single hydrophone

The low-frequency impulsive gunshot vocalizations of baleen whales exhibit dispersive propagation in shallow-water channels which is well-modeled by normal mode theory. Typically, underwater acoustic source range estimation requires multiple time-synchronized hydrophone arrays which can be difficult and expensive to achieve. However, single-hydrophone modal dispersion has been used to range baleen whale vocalizations and estimate shallow-water geoacoustic properties. Although convenient when compared to sensor arrays, these algorithms require preliminary signal detection and human labor to estimate the modal dispersion. In this paper, we apply a temporal convolutional network (TCN) to spectrograms from single-hydrophone acoustic data for simultaneous gunshot detection and ranging. The TCN learns ranging and detection jointly using gunshots simulated across multiple environments and ranges along with experimental noise. The synthetic data are informed by only the water column depth, sound speed, and density of the experimental environment, while other parameters span empirically observed bounds. The method is experimentally verified on North Pacific right whale gunshot data collected in the Bering Sea. To do so, 50 dispersive gunshots were manually ranged using the state-of-the-art time-warping inversion method. The TCN detected these gunshots among 50 noise-only examples with high precision and estimated ranges which closely matched those of the physics-based approach.

Acoustic properties and shallow water propagation distances of Caribbean spiny lobster sounds (Panulirus argus)

Marine crustaceans produce broadband sounds that are useful for passive acoustic monitoring to support conservation and management efforts. However, the propagation characteristics and detection ranges of their signals are poorly known, limiting our leveraging of these sounds. Here, we used a four-hydrophone linear array to measure source levels (SLs) and sound propagation from Caribbean spiny lobsters (Panulirus argus) of a wide range of sizes within a natural, shallow water habitat (3.3 m depth). Source level in peak-peak (SLpp) varied with body size; larger individuals produced SLpp up to 166 dB re 1 μPa. However, transmission losses (TL) were similar across all sizes, with a global fitted TL of 12.1 dB. Correspondingly, calculated detection ranges varied with body size, ranging between 14 and 364 m for small and large individuals (respectively). This increased up to 1612 m for large spiny lobsters when considering lower ambient noise levels. Despite the potential ease of tank studies, our results highlight the importance of empirical in situ sound propagation studies for marine crustaceans. Given the important ecological and economic role of spiny lobsters, these data are a key step to supporting remote monitoring of this species for fisheries management and efforts to acoustically quantify coral reefs' health.

Acoustic detection range of right whale upcalls identified in near-real time from a moored buoy and a Slocum glider

The goal of this study was to characterize the detection range of a near real-time baleen whale detection system, the digital acoustic monitoring instrument/low-frequency detection and classification system (DMON/LFDCS), equipped on a Slocum glider and a moored buoy. As a reference, a hydrophone array was deployed alongside the glider and buoy at a shallow-water site southwest of Martha's Vineyard (Massachusetts, USA) over a four-week period in spring 2017. A call-by-call comparison between North Atlantic right whale upcalls localized with the array (n = 541) and those detected by the glider or buoy was used to estimate the detection function for each DMON/LFDCS platform. The probability of detection was influenced by range, ambient noise level, platform depth, detection process, review protocol, and calling rate. The conservative analysis of near real-time pitch tracks suggested that, under typical conditions, a 0.33 probability of detection of a single call occurred at 6.2 km for the buoy and 8.6-13.4 km for the glider (depending on glider depth), while a 0.10 probability of detection of a single call occurred at 14.4 m for the buoy and 22.6-27.5 km for the glider. Probability of detection is predicted to increase substantially at all ranges if more than one call is available for detection.

Marine mammal detections on the Chukchi Plateau 2009-2020

The Arctic Ice Monitoring (AIM) observatory has been maintained on the Chukchi Plateau at 75.1° N 168.0° W nearly continuously since 2003. The AIM site consists of a submerged mooring that, since October 2008, has been instrumented with a passive acoustic recorder to sample ambient sound, with a focus on marine mammal detections in the High Arctic. Year-long data sets for 2009, 2012, and 2014-2020 were analyzed for the presence of signals from Arctic species including bowhead and beluga whales, bearded seals, and walrus. Calls from subarctic ribbon seals were commonly detected in autumn months, suggesting they have expanded their distribution much further northward. Killer whale calls were detected in recent years providing evidence that they have moved further north into the Pacific Arctic. No other subarctic cetaceans were heard. Year-round passive acoustic sampling of sounds produced by marine mammals over a decadal timescale has enhanced our understanding of how climate-driven changes in biodiversity are affecting even the very High Arctic.

Passive acoustic monitoring reveals year-round marine mammal community composition off Tasiilaq, Southeast Greenland

Climate-driven changes are affecting sea ice conditions off Tasiilaq, Southeast Greenland, with implications for marine mammal distributions. Knowledge about marine mammal presence, biodiversity, and community composition is key to effective conservation and management but is lacking, especially during winter months. Seasonal patterns of acoustic marine mammal presence were investigated relative to sea ice concentration at two recording sites between 2014 and 2018, with one (65.6°N, 37.4°W) or three years (65.5°N, 38.0°W) of passive acoustic recordings. Seven marine mammal species were recorded. Bearded seals were acoustically dominant during winter and spring, whereas sperm, humpback, and fin whales dominated during the sea ice-free summer and autumn. Narwhals, bowhead, and killer whales were recorded only rarely. Song-fragments of humpback whales and acoustic presence of fin whales in winter suggest mating-associated behavior taking place in the area. Ambient noise levels in 1/3-octave level bands (20, 63, 125, 500, 1000, and 4000 Hz), ranged between 75.6 to 105 dB re 1 μPa. This study provides multi-year insights into the coastal marine mammal community composition off Southeast Greenland and suggests that the Tasiilaq area provides suitable habitat for various marine mammal species year-round.

Classification of dispersive gunshot calls using a convolutional neural network

A convolutional neural network (CNN) was trained to identify multi-modal gunshots (impulse calls) within large acoustic datasets in shallow-water environments. South Atlantic right whale gunshots were used to train the CNN, and North Pacific right whale (NPRW) gunshots, to which the network was naive, were used for testing. The classifier generalizes to new gunshots from the NPRW and is shown to identify calls which can be used to invert for source range and/or environmental parameters. This can save human analysts hours of manually screening large passive acoustic monitoring datasets.

Polarization of ocean acoustic normal modes

In ocean acoustics, shallow water propagation is conveniently described using normal mode propagation. This article proposes a framework to describe the polarization of normal modes, as measured using a particle velocity sensor in the water column. To do so, the article introduces the Stokes parameters, a set of four real-valued quantities widely used to describe polarization properties in wave physics, notably for light. Stokes parameters of acoustic normal modes are theoretically derived, and a signal processing framework to estimate them is introduced. The concept of the polarization spectrogram, which enables the visualization of the Stokes parameters using data from a single vector sensor, is also introduced. The whole framework is illustrated on simulated data as well as on experimental data collected during the 2017 Seabed Characterization Experiment. By introducing the Stokes framework used in many other fields, the article opens the door to a large set of methods developed and used in other contexts but largely ignored in ocean acoustics.

Bowhead and beluga whale acoustic detections in the western Beaufort Sea 2008-2018

The Distributed Biological Observatory (DBO) was established to detect environmental changes in the Pacific Arctic by regular monitoring of biophysical responses in each of 8 DBO regions. Here we examine the occurrence of bowhead and beluga whale vocalizations in the western Beaufort Sea acquired by acoustic instruments deployed from September 2008-July 2014 and September 2016-October 2018 to examine inter-annual variability of these Arctic endemic species in DBO Region 6. Acoustic data were collected on an oceanographic mooring deployed in the Beaufort shelfbreak jet at ~71.4°N, 152.0°W. Spectrograms of acoustic data files were visually examined for the presence or absence of known signals of bowhead and beluga whales. Weekly averages of whale occurrence were compared with outputs of zooplankton, temperature and sea ice from the BIOMAS model to determine if any of these variables influenced whale occurrence. In addition, the dates of acoustic whale passage in the spring and fall were compared to annual sea ice melt-out and freeze-up dates to examine changes in phenology. Neither bowhead nor beluga whale migration times changed significantly in spring, but bowhead whales migrated significantly later in fall from 2008-2018. There were no clear relationships between bowhead whales and the environmental variables, suggesting that the DBO 6 region is a migratory corridor, but not a feeding hotspot, for this species. Surprisingly, beluga whale acoustic presence was related to zooplankton biomass near the mooring, but this is unlikely to be a direct relationship: there are likely interactions of environmental drivers that result in higher occurrence of both modeled zooplankton and belugas in the DBO 6 region. The environmental triggers that drive the migratory phenology of the two Arctic endemic cetacean species likely extend from Bering Sea transport of heat, nutrients and plankton through the Chukchi and into the Beaufort Sea.

Low-frequency ocean ambient noise on the Chukchi Shelf in the changing Arctic

This article presents the study of a passive acoustic dataset recorded on the Chukchi Shelf from October 2016 to July 2017 during the Canada Basin Acoustic Propagation Experiment (CANAPE). The study focuses on the low-frequency (250-350 Hz) ambient noise (after individual transient signals are removed) and its environmental drivers. A specificity of the experimental area is the Beaufort Duct, a persistent warm layer intrusion of variable extent created by climate change, which favors long-range acoustic propagation. The Chukchi Shelf ambient noise shows traditional polar features: it is quieter and wind force influence is reduced when the sea is ice-covered. However, the study reveals two other striking features. First, if the experimental area is covered with ice, the ambient noise drops by up to 10 dB/Hz when the Beaufort Duct disappears. Further, a large part of the noise variability is driven by distant cryogenic events, hundreds of kilometers away from the acoustic receivers. This was quantified using correlations between the CANAPE acoustic data and distant ice-drift magnitude data (National Snow and Ice Data Center).

Deep learning-based high-frequency source depth estimation using a single sensor

The sensitivity of underwater propagation models to acoustic and environmental variability increases with the signal frequency; therefore, realizing accurate acoustic propagation predictions is difficult. Owing to this mismatch between the model and actual scenarios, achieving high-frequency source localization using model-based methods is generally difficult. To address this issue, we propose a deep learning approach trained on real data. In this study, we focused on depth estimation. Several 18-layer residual neural networks were trained on a normalized log-scaled spectrogram that was measured using a single hydrophone. The algorithm was evaluated using measured data transmitted from the linear frequency modulation chirp probe (11-31 kHz) in the shallow-water acoustic variability experiment 2015. The signal was received through two vertical line arrays (VLAs). The proposed method was applied to all 16 sensors of the VLA to determine the estimation performance with respect to the receiver depth. Furthermore, frequency-difference matched field processing was applied to the experimental data for comparison. The results indicate that ResNet can determine complicated features of high-frequency signals and predict depths, regardless of the receiver depth, while exhibiting robust environmental and positional variability.

Spiny lobster sounds can be detectable over kilometres underwater

The detection ranges of broadband sounds produced by marine invertebrates are not known. To address this deficiency, a linear array of hydrophones was built in a shallow water area to experimentally investigate the propagation features of the sounds from various sizes of European spiny lobsters (Palinurus elephas), recorded between 0.5 and 100 m from the animals. The peak-to-peak source levels (SL, measured at one meter from the animals) varied significantly with body size, the largest spiny lobsters producing SL up to 167 dB re 1 µPa². The sound propagation and its attenuation with the distance were quantified using the array. This permitted estimation of the detection ranges of spiny lobster sounds. Under the high ambient noise conditions recorded in this study, the sounds propagated between 5 and 410 m for the smallest and largest spiny lobsters, respectively. Considering lower ambient noise levels and different realistic propagation conditions, spiny lobster sounds can be detectable up to several kilometres away from the animals, with sounds from the largest individuals propagating over 3 km. Our results demonstrate that sounds produced by P. elephas can be utilized in passive acoustic programs to monitor and survey this vulnerable species at kilometre scale in coastal waters.

Nonlinear time-warping made simple: A step-by-step tutorial on underwater acoustic modal separation with a single hydrophone

Classical ocean acoustic experiments involve the use of synchronized arrays of sensors. However, the need to cover large areas and/or the use of small robotic platforms has evoked interest in single-hydrophone processing methods for localizing a source or characterizing the propagation environment. One such processing method is "warping," a non-linear, physics-based signal processing tool dedicated to decomposing multipath features of low-frequency transient signals (frequency f < 500 Hz), after their propagation through shallow water (depth D < 200 m) and their reception on a distant single hydrophone (range r > 1 km). Since its introduction to the underwater acoustics community in 2010, warping has been adopted in the ocean acoustics literature, mostly as a pre-processing method for single receiver geoacoustic inversion. Warping also has potential applications in other specialties, including bioacoustics; however, the technique can be daunting to many potential users unfamiliar with its intricacies. Consequently, this tutorial article covers basic warping theory, presents simulation examples, and provides practical experimental strategies. Accompanying supplementary material provides matlab code and simulated and experimental datasets for easy implementation of warping on both impulsive and frequency-modulated signals from both biotic and man-made sources. This combined material should provide interested readers with user-friendly resources for implementing warping methods into their own research.

Range Localization of a Moving Source Based on Synthetic Aperture Beamforming Using a Single Hydrophone in Shallow Water

To localize a moving source in shallow water with a single hydrophone, a passive range localization method based on synthetic aperture beamforming is proposed. First, the horizontal wavenumber spectrum excited by the source is obtained by synthetic aperture beamforming. Then, according to the theoretical derivation (when the integration time is short, the maximum value of the horizontal wavenumber spectrum is related to the average horizontal wavenumber and the radial velocity of the source), the radial velocity can be obtained after obtaining the average horizontal wavenumber. Finally, in the case where there is a closest point of approach (CPA), the range can be recovered from estimation of the range and time of CPA, and from the constant source speed alone the linear track by fitting the source velocity with the model of radial velocity. The only a priori information required is the sound velocity in water. The processing results using simulated data and SWellEx-96 experimental data show that the proposed method can effectively estimate the range of a moving source in shallow sea.

Estimating the spatial distribution of vocalizing animals from ambient sound spectra using widely spaced recorder arrays and inverse modelling

The sound energy from marine mammal populations vocalizing over extended periods of time adds up to quasi-continuous "choruses," which create characteristic peaks in marine sound spectra. An approach to estimate animal distribution is presented, which uses chorus recordings from very sparse unsynchronized arrays in ocean areas that are too large or remote to survey with traditional methods. To solve this under-determined inverse problem, simulated annealing is used to estimate the distribution of vocalizing animals on a geodesic grid. This includes calculating a transmission loss (TL) matrix, which connects all grid nodes and recorders. Geometrical spreading and the ray trace model BELLHOP [Porter (1987). J. Acoust. Soc. Am. 82(4), 1349-1359] were implemented. The robustness of the proposed method was tested with simulated marine mammal distributions in the Atlantic sector of the Southern Ocean using both drifting acoustic recorders [Argo (2018). SEANOE] and a moored array as acoustic receivers. The results show that inversion accuracy mainly depends on the number and location of the recorders, and can be predicted using the entropy and range of the estimated source distributions. Tests with different TL models indicated that inversion accuracy is affected only slightly by inevitable inaccuracies in TL models. The presented method could also be applied to bird, crustacean, and insect choruses.

Characterizing the seabed in the Straits of Florida by using acoustic noise interferometry and time warping

Interferometry of ambient and shipping noise in the ocean provides a way to estimate physical parameters of the seafloor and the water column in an environmentally friendly manner without employing any controlled sound sources. With noise interferometry, two-point cross-correlation functions of noise serve as the probing signals and replace the Green's function measured in active acoustic remote sensing. The amount of environmental information that can be obtained with passive remote sensing and the robustness of the estimates of the seafloor parameters increase when contributions of individual normal modes are resolved in the noise cross-correlation function. Using the data obtained in the 2012 noise-interferometry experiment in the Straits of Florida, dispersion curves of the first four normal modes are obtained in this paper by application of the time-warping transform to noise cross correlations. The passively measured dispersion curves are inverted for unknown geoacoustic properties of the seabed. Resulting thickness of the sediment layer and sound speed are consistent with the geoacoustic models obtained earlier by other means.

Range-dependent impacts of ocean acoustic propagation on automated classification of transmitted bowhead and humpback whale vocalizations

Significant effort has been made over the last few decades to develop automated passive acoustic monitoring (PAM) systems capable of classifying cetaceans at the species level. The utility of such systems depends on the systems' ability to operate across a wide range of ocean acoustic environments; however, anecdotal evidence suggests that site-specific propagation characteristics impact the performance of PAM systems. Variability in propagation characteristics leads to differences in how each cetacean vocalization is altered as it propagates along the source-receiver path. A propagation experiment was conducted in the Gulf of Mexico to investigate the range-dependent impacts of acoustic propagation on the performance of an automated classifier. Modified bowhead and humpback vocalizations were transmitted over ranges from 1 to 10 km. When the classifier was trained with signals collected near the sound source, it was found that the performance decreased with increasing transmission range-this appeared to be largely explained by decreasing signal-to-noise ratio (SNR). Generation of performance matrices showed that one method to develop a classifier that maintains high performance across many ranges is to include a varied assortment of ranges in the training data; however, if the training set is limited, it is best to train on relatively low SNR vocalizations.

A Passive Source Location Method in a Shallow Water Waveguide with a Single Sensor Based on Bayesian Theory

Bayesian methodology is a good way to infer unknown parameters in a marine environment. A passive source location method in a shallow water waveguide with a single sensor based on Bayesian theory is presented in this paper. The input of a Bayesian inversion algorithm is received different normal mode impulse signals, which are separated and extracted with a warping transformation from received broadband impulse signals. The source range, depth, and other seabed parameters were estimated without prior knowledge of the seabed information. Different normal mode impulse acoustic signals travelling at different group speeds arrived at the sensor at different times because of the dispersion characteristics of the shallow water waveguide. The time delay of different modes can be used for the passive source location. However, normal mode group speeds are greatly affected by the environmental parameters. The performance of the passive location becomes negative when parameters mismatch. In this paper, the source location was transformed to the inversion of the source location and environmental parameters, which can be estimated accurately based on the multi-dimensional posterior probability density (PPD). This method is less limited by environmental factors, and the accuracy of inversion results can be analyzed according to the PPD of inversion parameters, which has higher reliability and a wider application scope. The effectiveness and robustness of the algorithm were quantified in terms of the root mean squared error (RMSE) at a variety of signal-to-noise ratios (SNRs) in 50 simulation sets. The RMSE values decreased with the SNR. The validity and accuracy of the method were proved by the results of simulation and experiment data.

Grid-free compressive mode extraction

A grid-free compressive sensing (CS) based method for extracting the normal modes of acoustic propagation in the ocean waveguide from vertical line array (VLA) data is presented. Extracting the normal modes involves the estimation of mode horizontal wavenumbers and the corresponding mode shapes. Sparse representation of the waveguide propagation using modes at discrete horizontal wavenumbers enables CS to be applied. Grid-free CS, based on group total-variation norm minimization, is adopted to mitigate the issues of the wavenumber search grid discretization in the conventional CS. In addition, the suggested method can process multiple sensor data jointly, which improves performance in estimation over single sensor data processing. The method here uses data on a VLA from a source at several ranges, and processes the multiple sensor data at different depths jointly. The grid-free CS extracts the mode wavenumbers and shapes even with no a priori environmental knowledge, a partial water column spanning array data, and without the mode orthogonality condition. The approach is illustrated by numerical simulations and experimental SWellEx-96 (shallow water evaluation cell experiment 1996) data.

The underwater soundscape in western Fram Strait: Breeding ground of Spitsbergen's endangered bowhead whales

In the Arctic, warming and concomitant reductions in sea ice will affect the underwater soundscape, with the greatest changes likely being linked to anthropogenic activities. In this study, an acoustic recorder deployed on an oceanographic mooring in western Fram Strait documented the soundscape of this area, which is important habitat for the Critically Endangered Spitsbergen bowhead whale population. The soundscape was quasi-pristine much of the year, with low numbers of ships traversing the area. However, during summer/autumn, signals from airgun surveys were detected >12h/day. Mean received peak-to-peak SPLs for loud airgun pulses reached 160.46±0.48dB 1μPa when seismic-survey ships were close (at ~57km). Bowhead whales were present almost daily October-April in all years, with singing occurring in almost every hour November-March. Currently, loud anthropogenic sound sources do not temporally overlap the peak period of bowhead singing. This study provides important baseline data for future monitoring.

A pulsed-air model of blue whale B call vocalizations

Blue whale sound production has been thought to occur by Helmholtz resonance via air flowing from the lungs into the upper respiratory spaces. This implies that the frequency of blue whale vocalizations might be directly proportional to the size of their sound-producing organs. Here we present a sound production mechanism where the fundamental and overtone frequencies of blue whale B calls can be well modeled using a series of short-duration (<1 s) wavelets. We propose that the likely source of these wavelets are pneumatic pulses caused by opening and closing of respiratory valves during air recirculation between the lungs and laryngeal sac. This vocal production model is similar to those proposed for humpback whales, where valve open/closure and vocal fold oscillation is passively driven by airflow between the lungs and upper respiratory spaces, and implies call frequencies could be actively changed by the animal to center fundamental tones at different frequency bands during the call series.

Using nonlinear time warping to estimate North Pacific right whale calling depths in the Bering Sea

Calling depth distributions are estimated for two types of calls produced by critically endangered eastern North Pacific right whales (NPRWs) in the Bering Sea, using passive acoustic data collected with bottom-mounted hydrophone recorders. Nonlinear time resampling of 12 NPRW "upcalls" and 20 "gunshots" recorded in a critical NPRW habitat isolated individual normal mode arrivals from each call. The relative modal arrival times permitted range estimates between 1 and 40 km, while the relative modal amplitudes permitted call depth estimates, provided that environmental inversions were obtained from high signal-to-noise ratio calls. Gunshot sounds were generally only produced at a few meters depth, while upcall depths clustered between 10 and 25 m, consistent with previously published bioacoustic tagging results from North Atlantic right whales. A Wilcoxon rank sum test rejected the null hypothesis that the mean calling depths of the two call types were the same (p = 2.9 × 10^-5); the null hypothesis was still rejected if the sample set was restricted to one call per acoustic encounter (p = 0.02). Propagation modeling demonstrates that deeper depths enhance acoustic propagation and that source depth estimates impact both NPRW upcall source level and detection range estimates.

Waveguide mode amplitude estimation using warping and phase compensation

In shallow water, low-frequency propagation can be described by modal theory. Acoustical oceanographic measurements under this situation have traditionally relied on spatially filtering signals with arrays of synchronized hydrophones. Recent work has demonstrated how a method called warping allows isolation of individual mode arrivals on a single hydrophone, a discovery that subsequently opened the door for practical single-receiver source localization and geoacoustic inversion applications. Warping is a non-linear resampling of the signal based on a simplistic waveguide model. Because warping is robust to environmental mismatch, it provides accurate estimates of the mode phase even when the environment is poorly known. However, the approach has issues with mode amplitude estimation, particularly for the first arriving mode. As warping is not invariant to time shifting, it relies on accurate estimates of the signal's time origin, which in turn heavily impacts the first mode's amplitude estimate. Here, a revised warping operator is proposed that incorporates as much prior environmental information as possible, and is actually equivalent to compensating the relative phase of each mode. Warping and phase compensation are applied to both simulated and experimental data. The proposed methods notably improve the amplitude estimates of the first arriving mode.

Bowhead whale localization using time-difference-of-arrival data from asynchronous recorders

This paper estimates bowhead whale locations and uncertainties using nonlinear Bayesian inversion of the time-difference-of-arrival (TDOA) of low-frequency whale calls recorded on onmi-directional asynchronous recorders in the shallow waters of the northeastern Chukchi Sea, Alaska. A Y-shaped cluster of seven autonomous ocean-bottom hydrophones, separated by 0.5-9.2 km, was deployed for several months over which time their clocks drifted out of synchronization. Hundreds of recorded whale calls are manually associated between recorders. The TDOA between hydrophone pairs are calculated from filtered waveform cross correlations and depend on the whale locations, hydrophone locations, relative recorder clock offsets, and effective waveguide sound speed. A nonlinear Bayesian inversion estimates all of these parameters and their uncertainties as well as data error statistics. The problem is highly nonlinear and a linearized inversion did not produce physically realistic results. Whale location uncertainties from nonlinear inversion can be low enough to allow accurate tracking of migrating whales that vocalize repeatedly over several minutes. Estimates of clock drift rates are obtained from inversions of TDOA data over two weeks and agree with corresponding estimates obtained from long-time averaged ambient noise cross correlations. The inversion is suitable for application to large data sets of manually or automatically detected whale calls.

Source level and calling depth distributions of migrating bowhead whale calls in the shallow Beaufort Sea

Automated and manual acoustic localizations of migrating bowhead whales were used to estimate source level and calling depth distributions of their frequency-modulated-modulated calls over seven years between 2008 and 2014. Whale positions were initially triangulated using directional autonomous seafloor acoustic recorders, deployed between 25 and 55 m water depth near Kaktovik, Alaska, during the fall westward migration. Calling depths were estimated by minimizing the "discrepancy" between source level estimates from at least three recorders detecting the same call. Applying a detailed waveguide propagation model to the data yielded broadband source levels of 161 ± 9 dB re 1 μPa² s at 1 m (SEL) for calls received between 20 and 170 Hz. Applying a simpler 15 log₁₀(R) power-law propagation model yielded SEL source levels of 158 ± 10 dB. The most probable calling depths lay between 22 and 30 m: optimal depths for long-range acoustic signal transmission in this particular environment.

Bowhead whale localization using asynchronous hydrophones in the Chukchi Sea

This paper estimates bowhead whale locations and uncertainties using non-linear Bayesian inversion of their modally-dispersed calls recorded on asynchronous recorders in the Chukchi Sea, Alaska. Bowhead calls were recorded on a cluster of 7 asynchronous ocean-bottom hydrophones that were separated by 0.5-9.2 km. A warping time-frequency analysis is used to extract relative mode arrival times as a function of frequency for nine frequency-modulated whale calls that dispersed in the shallow water environment. Each call was recorded on multiple hydrophones and the mode arrival times are inverted for: the whale location in the horizontal plane, source instantaneous frequency (IF), water sound-speed profile, seabed geoacoustic parameters, relative recorder clock drifts, and residual error standard deviations, all with estimated uncertainties. A simulation study shows that accurate prior environmental knowledge is not required for accurate localization as long as the inversion treats the environment as unknown. Joint inversion of multiple recorded calls is shown to substantially reduce uncertainties in location, source IF, and relative clock drift. Whale location uncertainties are estimated to be 30-160 m and relative clock drift uncertainties are 3-26 ms.

Sequential inversion of modal data for sound attenuation in sediment at the New Jersey Shelf

This paper presents a method for estimating bottom geoacoustic properties especially the sediment attenuation from information contained in normal modes of a broadband signal. Propagating modes are resolved using the time-warping technique applied to signals from light bulb sound sources deployed at ranges of 5 and 7 km in the Shallow Water '06 experiment. A sequential inversion approach is designed that uses specific features of the acoustic data that are highly sensitive to specific geoacoustic model parameters. The first feature is the modal group speed, which is inverted for seabed sound speed, density, and sediment thickness. The second feature is the modal depth function for inverting receiver depths. The third feature is related to the modal coefficient spectra, and this is inverted for source depth and sediment attenuation. In each subsequent stage, estimates from the previous stage(s) are used as known values. The sequential inversion is stable and generates estimates for the geoacoustic model parameters that agree very well with results from other experiments carried out in the same region. Notably, the inversion obtains an estimated attenuation of 0.078 dB/λ in the band 120-180 Hz for the de-watered marine sediment characteristic of the continental shelf at the site.