Ocean Waveguide Research Articles

The Depth Distribution Law of the Polarization of the Vector Acoustic Field in the Ocean Waveguide

The polarization of the acoustic field in the ocean waveguide environment is a unique property that can provide new ideas for locating and detecting the underwater target, so it is interesting to study the polarization. This paper extends the Stokes parameters to a broadband form, and uses the non-stationary phase approximation method to simplify the expressions, reducing the complexity of theoretical derivation. A physical phenomenon is observed where polarization exhibits significant variations concerning the sea surface, seafloor, source depth, and the source symmetrical depth. Simulation results demonstrate that the simplified equations using the non-stationary phase approximation are effective. Additionally, by normalizing the broadband Stokes parameters, the effects of horizontal range on the depth distribution law of polarization can be eliminated. Subsequently, using the normalized broadband Stokes parameters, the influence of environmental and source parameters on the depth distribution law of polarization is analyzed. The effectiveness of the non-stationary phase approximation and the range-independence property of the normalized broadband Stokes parameters are verified by processing RHUM-RUM experimental data. Based on the conclusions of this paper, it is expected that the polarization can be used for target depth estimation.

Deblurring of Beamformed Images in the Ocean Acoustic Waveguide Using Deep Learning-Based Deconvolution

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS).

Asymmetric Impacts of ENSO on Boreal Winter Southern Subtropical Cell in the Indian Ocean

AbstractThe interannual relationships between the southern subtropical cell (SSTC) in the Indian Ocean (IO) and the El Niño‐Southern Oscillation (ENSO) are examined. The results demonstrate that the impacts of ENSO on the boreal winter IO SSTC display a notable negative skewness, with the amplification of the SSTC during La Niña being far stronger than its reduction during El Niño. This difference is mainly attributed to an asymmetry in anomalous meridional geostrophic transport, characterized by more robust southward (weaker northward) geostrophic transport anomalies during La Niña (El Niño) events. Further analyses reveal that the differing longitudinal positions of the ENSO‐related sea surface temperature anomaly (SSTA) are the primary drivers of the asymmetry in anomalous meridional geostrophic transport via the oceanic and atmospheric teleconnections. Compared to the warm SSTA in El Niño events, the La Niña‐related cool SSTA in the central‐eastern equatorial Pacific displays a greater westward extension, which induces stronger surface easterlies over the western equatorial Pacific, and then initiates a more substantial sea surface height anomaly (SSHA) in the southeast Indian Ocean (SEIO) through oceanic waveguides. This leads to a heightened zonal SSHA gradient over the southern IO, resulting in a larger anomalous meridional geostrophic transport. On the other hand, the westward extension of negative SSTA during La Niña winters triggers anomalous cyclonic northerlies along the western coast of Australia by shifting the Walker circulation westward. These coastal northerlies amplify the positive SSHA signals in the SEIO and enhance the anomalous meridional geostrophic transport, ultimately contributing to a more robust IO SSTC response.

Predicting ocean pressure field with a physics-informed neural network.

Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases, even at frequencies below 100 Hz, posing a challenge for neural networks to converge to accurate solutions. To address this, we utilize the envelope function from the parabolic-equation technique, fundamental in ocean sound propagation modeling. The envelope function shows slower variations across ranges, enabling PINNs to predict sound pressure in an ocean waveguide more effectively. Additional PDE information allows PINNs to capture PDE solutions even with a limited amount of training data, distinguishing them from purely data-driven machine learning approaches that require extensive datasets. Our approach is validated through simulations and using data from the SWellEx-96 experiment.

Directional soundscapes in the Norwegian Seas observed with a coherent hydrophone array

Directional soundscaping is an efficient approach for examining marine ecosystems since it allows the study of living organisms, their behavior, and temporo-spatial interactions with other natural and man-made objects underwater, useful for ecosystem monitoring during offshore energy activities, maritime surveillance, and defense. A large aperture coherent hydrophone array was employed for remote sensing in the Norwegian and Barents Seas in Spring 2014. Extensive analysis have been conducted in post-processing of recorded hydrophone array data for automatic detection, bearing estimation, and classification of signals by different sound producers, such as whale calls, ship radiated noise, and fish sounds. Directional soundscaping through passive ocean acoustic waveguide remote sensing (POAWRS) provides bearing-time trajectories of signal detections that can be applied for geographical mapping. The received marine mammal sounds include vocalizations from baleen whales such as humpback, fin, and minke, and toothed whales, such as beluga, pilot, and sperm. We provide an insight to the vocalizations and behaviorial patterns of these whales in the Lofoten and Northern Finmark regions. The relative contributions of distinct sound sources, including whales, fish, and ships, to the directional soundscapes are quantified. We also examine their temporo-spatial dependences via geographic mapping of acoustic signals from distinct sound producers.

Acoustic diversity, bearing estimation, and localization of sound sources in shallow water and deep ocean off the U.S. Northeast coast using a coherent hydrophone array

An in-house developed 160-element large-aperture hydrophone array from Northeastern University was utilized for ocean monitoring in both the shallow waters of the Great South Channel (GSC) and the deep-water area off the continental slope south of Rhode Island in September 2021. Utilizing passive ocean acoustic waveguide remote sensing (POAWRS) technology, the system enabled real-time, broad-area surveillance of the oceanic environment. A novel multistage clustering was introduced for annotating the vast volume of underwater acoustic data, drawing inspiration from the human cognitive process of categorizing sounds. Initially, sounds are separated into broad groups, akin to a first human listening pass. Subsequent stages of clustering refine these categories, utilizing different sets of features to achieve a granularity of annotation that closely mirrors expert human annotation processes. It was found that the acoustic landscape of the GSC was primarily composed of diverse marine mammal sounds, such as fin whale 20 Hz pulses, humpback whale songs, minky whale buzz sequences, sperm whale and dolphin echolocation clicks, and unidentified whale calls. Conversely, the deep-water area off the continental slope featured predominantly fish sounds, chorus-like sounds, and dolphin vocalizations. Detailed analyses of the most prevalent vocalizations are presented, including their bearing-time trajectories and localizations.

Performance of an approximate Bayes factor based detector for environmentally informed active sonar

Developed here are a space of approximate Bayes Factor (BF) inference processors for high frequency broadband active sonar with short vertical arrays operating in shallow water ocean waveguides that exploit relatively depth invariant modes of propagation. The relevant information regarding the refractive media, rough surface and volume reverberation are incorporated to build the marginal likelihoods under each of the composite hypotheses of null and alternative. Acoustic scattering from a mobile object of interest under depth uncertainty characterizes the compound alternative hypothesis. Approximations are presented and inferences regarding the presence of the mobile body of interest are determined against a composite null hypothesis of reverberation and ambient acoustic noise. The approximate BF processor is shown to be a time-varying quadratic form of array observations over the beam-delay space. We demonstrate the sub-space processing of depth invariant modes at range and illustrate the BF inferential approach on a few representative waveguides. Performance in classic terms of probability of detection as a function of false alarm rate are presented. [This work supported by the Office of Naval Research.]

Polynomial chaos expansions for modelling the statistics of acoustic propagation in random waveguides

The use of Polynomial Chaos expansions to model the transfer of the statistical properties of the sound speed in ocean waveguides to those of acoustic waves passing through them is described. A perturbational framework approximating the interaction of acoustic normal modes with fluctuations around a background sound speed is used, with the fluctuations being represented vertically by empirical orthogonal functions and horizontally by correlation functions. The Polynomial Chaos expansions are derived to second order in the uncorrelated random variable representing the sound speed fluctuations for the generalized phase of the complex modal amplitudes, and to third order for the modal amplitudes themselves. The ability of the second order polynomial chaos expansion for the generalized model phase to accurately model acoustic propagation statistics is closely coupled to the accuracy of the adiabatic approximation in light scattering regimes. The weights of the Polynomial Chaos expansions are obtained using both direct (theoretical) and indirect (least-squares fit) methods. Divergence between these two sets of weights increases with combinations of increasing strength of sound speed fluctuations, increasing frequency, or increasing range, indicating where a higher order expansion may be necessary. Modelling results for the first and second moments of the acoustic intensity and the Scintillation Index in random waveguides are presented and compared to Monte Carlo results.

SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance.

Depth distribution law of polarization characteristics of vector acoustic field in shallow sea (Retracted)

The polarization of the acoustic field in the ocean waveguide environment is a unique property that can be measured by using a particle velocity sensor in the water column. It can provide new ideas for locating and detecting the underwater target, so it is interesting to study the polarization. The polarization of a monochromatic signal has been described by the Stokes parameters, a set of four real-valued quantities in previous work. In this work, the Stokes parameters are extended to the broadband form, and the expression is simplified by using the nonstationary phase approximation, which reduces the complexity of the theoretical derivation and reveals the physical mechanism behind the significant variations in polarization with source depth and symmetrical depth. Theoretical analysis shows that the polarization characteristics in the ideal waveguide vary significantly in the sea surface, the sea bottom, the depth of the sound source and symmetrical depth. In this work the numerical simulation is used to verify the theoretical analysis and study the relationship between range and integral bandwidth when nonstationary phase approximation method is effective. The numerical results demonstrate that the simplified expression using the nonstationary phase approximation is effective and can better characterize the depth distribution characteristics of the polarization. Additionally, by normalizing the broadband Stokes parameters, the effect of range on the depth distribution characteristics of polarization can be removed. It means that the normalized broadband Stokes parameters are in theory free of the range and depend on the environment, the receiver depth and the source depth, which have the potential to be used for source depth estimation. Subsequently, focusing on normalized broadband Stokes parameters, we analyzes the effects of parameters such as source frequency, source depth, sound speed profile and water depth on the depth distribution characteristics of polarization. The analysis results show that environmental factors have great influence on the depth distribution characteristics of polarization. In the end, the validity of the nonstationary phase approximation and the range-independent property of the normalized broadband Stokes parameters are verified by the results of the RHUM-RUM experimental data processing. The findings provide a theoretical basis for passive target depth estimation based on polarization.

Direct imaging of inhomogeneities in a 3D shallow ocean waveguide with an elastic seabed

Direct imaging of inhomogeneities in a 3D shallow ocean waveguide with an elastic seabed

Direct imaging of inhomogeneities in a 3D shallow ocean waveguide with an icecap

We are concerned with identifying scattering inhomogeneities in a three-dimensional shallow ocean waveguide with an icecap, which is motivated by applications in oceanic acoustics. A direct imaging method (DIM) is proposed to identify the marine point sources and medium obstacles from the near-field data. The key ingredient of the DIM is a certain imaging functional whose indicating behaviors is quantitatively characterized. Moreover, as corroborated by extensive numerical experiments, the DIM is computationally efficient and highly robust against noises, and the DIM can identify multiple sources and the scatterers of different shapes and scales from a few incidences and receivers.

Three-Dimensional Modeling of Sound Field Holograms of a Moving Source in the Presence of Internal Waves Causing Horizontal Refraction

In this paper, we study the variations of holograms of a moving source in an inhomogeneous ocean waveguide. It is assumed that intense internal waves (internal solitons) are the reason for the inhomogeneities of the shallow water waveguide. The results of 3D modeling of the sound field considering horizontal refraction by internal waves are presented. In the context of 3D modeling, the interferogram (sound intensity distributions in frequency–time coordinates) and hologram (2D Fourier transform of the interferogram) of moving sources are analyzed. The hologram consists of two disjoint regions corresponding to the unperturbed field and the field perturbed by internal waves. This structure of the hologram allows for the reconstruction of the interferogram of the unperturbed field in a waveguide in the absence of intense internal waves. The error in the reconstruction of the unperturbed interferogram is estimated.

Striation-based beamforming using a horizontal array

Conventional beamforming (CBF) based on plane waves is widely used for direction finding with a horizontal array deployed in ocean waveguides. However, CBF suffers from loss of spatial coherence due to the inhomogeneous field produced by constructive and destructive interference between multipath arrivals. Moreover, the grazing angle propagation leads to a bias in the estimated bearing. In this work, we revisit the striation-based beamforming (SBF) that exploits the waveguide invariant representing the complex wave propagation [Zurk and Rouseff, J. Acoust. Soc. Am. 132, EL264 (2012)]. Our approach involves a preliminary processing to restore the spatial coherence across the horizontal array, followed by CBF. Experimental results illustrate the performance improvement of SBF over CBF in terms of array gain, resolution, and bias.

Physics-informed neural network for predicting unmeasured ocean acoustic pressure field

This study employs a physics-informed neural network in an ocean waveguide to predict the unmeasured acoustic pressure field, leveraging partially measured data. The method addresses a scenario where an acoustic source transmits signals across different ranges and is measured by multiple receivers. The acoustic pressure field in ocean waveguides exhibits rapid spatial variations over kilometer-range scales. The fully connected neural networks encounter challenges when approximating high-frequency functions, known as spectral bias. To mitigate this problem, the measured pressure field is transformed into a low-frequency function for training the neural network. We propose two methods sharing the same neural network architecture but utilizing different information. The first method uses a complex value of the pressure field (i.e., both magnitude and phase), while the second method uses only magnitude. We validate the proposed methods using simulations and experimental data from the SWellEx-96 environment. Results demonstrate that the first method exhibits superior performance with sparse data, while the second method works better in real-world scenarios.

The Influence of Bottom Sediment on the Propagation of Caustic Beams in Oceanic Waveguides

The Influence of Bottom Sediment on the Propagation of Caustic Beams in Oceanic Waveguides

Improving mode extraction with physics-informed neural network

This study aims to enhance conventional mode extraction methods in ocean waveguides using a physics-informed neural network (PINN). Mode extraction involves estimating mode wavenumbers and corresponding mode depth functions. The approach considers a scenario with a single frequency source towed at a constant depth and measured from a vertical line array (VLA). Conventional mode extraction methods applied to experimental data face two problems. First, mode shape estimation is limited because the receivers only cover a partial waveguide. Second, the wavenumber spectrum is affected by issues such as Doppler shift and range errors. To address these challenges, we train the PINN with measured data, generating a densely sampled complex pressure field, including the unmeasured region above the VLA. We then apply the same mode extraction methods to both the raw data and the PINN-generated data for comparison. The proposed method is validated using data from the SWellEx-96, demonstrating improved mode extraction performance compared to using raw experimental data directly.

Passive ocean acoustic waveguide remote sensing of vocalization behavior and spatial distribution of diverse marine mammal species in the Norwegian and Barents Sea

Leveraging data acquired using a 160-element coherent hydrophone array deployed in the Norwegian and Barents Seas during spring 2014, and the passive ocean acoustic waveguide remote sensing (POAWRS) technique is employed to enable instantaneous wide-area monitoring of marine mammal vocalizations over expanses exceeding 100 km in diameter. The vocalization behavior of diverse marine mammal species including Fin, Humpback, Minke, Sperm, and Beluga whales are analyzed, quantifying time-frequency characteristics and call patterns from their vocalization signals present in high-resolution beamformed power spectrograms. Previously developed automatic detection and machine learning algorithms are employed for clustering and classification of underwater acoustic events, facilitating the subsequent manual audio-visual inspection of whale calls. Bearing-time trajectories of repetitive species-specific vocalizations signals are utilized to estimate locations of the whale calls and hence their temporo-spatial distributions. Through comparative studies, we assess the presence and abundance of marine mammal species-specific vocalization signals in three distinct coastal regions off Norway, namely, Alesund, Lofoten, and Northern Finmark. In addition, we investigate correspondences of marine mammal distributions with concurrently imaged instantaneous wide-area fish distributions from distinct fish species to elucidate potential predator-prey interactions and habitat preferences.

On coherently processing multiple arrays in underwater environments with spatial coherence losses from random medium effects and sensor position uncertainties

Under ideal circumstances coherently beamforming multiple distributed arrays should provide improvements over the standard approach of processing the arrays individually and combining them incoherently. These include improved directivity and higher gain. However, two major challenges when coherently processing spatially separated arrays in a real ocean are dealing with coherence losses or wave front distortions caused by random medium effects like internal waves and uncertainties in sensor locations. Some of the consequences are degraded beampatterns and poorer array directivity. In this paper, we perform a simulation-based study utilizing numerical propagation models along with theory examining the effects of wave front distortions induced by random medium effects like internal waves and sensor position uncertainties on coherently beamforming distributed arrays in representative ocean waveguides. Realistic realizations of wave front distortions are generated from actual internal wave measurements and models. The sensitivities of coherent processing to these effects are characterized by resultant beampatterns and losses in array directivity along with a quantification of the tolerances.

The Influence of Bottom Sediment on the Propagation of Caustic Beams in Oceanic Waveguides

Numerical modeling with the use of mode theory was used to investigate the regularities of the spatial (depth and horizontal distance) distribution of the acoustic field intensity formed by multiple interaction of a caustic beam with a layered bottom in a shallow oceanic waveguide with an underwater sound channel open to the bottom. It has been established that at the values of the sound velocity at the upper boundary in the sedimentary layer, less than the sound velocity at the bottom in the water layer, the formation of a multi-beam structure of the acoustic field is possible. It was found that, starting from certain distances, newly formed beams can play the main role in the spatial distribution of the acoustic field intensity.