Ocean Acoustics Research Articles

High-resolution observations of shallow-water acoustic propagation with distributed acoustic sensing.

Distributed acoustic sensing (DAS), converting fiber-optic cables into dense acoustic sensors, is a promising technology that offers a cost-effective and scalable solution for long-term, high-resolution studies in ocean acoustics. In this paper, the telecommunication cable of Martha's Vineyard Coastal Observatory (MVCO) is used to explore the feasibility of cable localization and shallow-water sound propagation with a mobile acoustic source. The MVCO DAS array records coherent, high-quality acoustic signals in the frequency band of 105-160 Hz, and a two-step inversion method is used to improve the location accuracy of DAS channels, reducing the location uncertainty to ∼2 m. The DAS array with refined channel positions enables the high-resolution observation of acoustic modal interference. Numerical simulations that reproduce the observed interference pattern suggest a compressional speed of 1750 m/s in the sediment, which is consistent with previous in situ geoacoustic measurements. These findings demonstrate the long-term potential of DAS for high-resolution ocean acoustic studies.

Regional soundscape modeling of the Atlantic Outer Continental Shelfa).

The ocean soundscape is a complex superposition of sound from natural and anthropogenic sources. Recent advances in acoustic remote sensing and marine bioacoustics have highlighted how animals use their soundscape and how the background sound levels are influenced by human activities. In this paper, developments in computational ocean acoustics, remote sensing, and oceanographic modeling are combined to generate modelled sound fields at multiple scales in time and space. Source mechanisms include surface shipping, surface wind, and wave fields. A basin scale model is presented and applied to the United States Atlantic Outer Continental Shelf (OCS). For model-data comparison at a single hydrophone location, the model is run for a single receiver position. Environmental and source model uncertainty is included in the site-specific modeling of the soundscape. An inversion of the local sediment type is made for a set of sites in the OCS. After performing this inversion, the qualitative comparison of the modelled sound pressure level (SPL) time series and observed SPL is excellent. The quantitative differences in the mean root mean square error between the model and data is less than 3 dB for most sites and frequencies above 90 Hz.

Eckart's contribution to modern treatments of scattering in ocean acoustics.

Eckart's contribution to modern treatments of scattering in ocean acoustics.

Inference of source signatures of merchant ships in shallow ocean environmentsa).

An ocean acoustics experiment in 2017 near a shipping lane on the New England continental shelf in about 75 m of water provided an opportunity to evaluate a methodology to extract source signatures of merchant ships in a bottom-limited environment. The data of interest are the received acoustic levels during approximately 20 min time intervals centered at the closest position of approach (CPA) time for each channel on two 16-element vertical line arrays. At the CPA ranges, the received levels exhibit a frequency-dependent peak and null structure, which possesses information about the geophysical properties of the seabed, such as the porosity and sediment thickness, and the characterization of the source, such as an effective source depth. The modeled seabed is represented by two sediment layers, parameterized with the viscous grain shearing (VGS) model, which satisfies causality, over a fixed deep layered structure. Inferred estimates of the implicit source levels require averaging an error function over the full 20 min time intervals. Within the 200-700 Hz band, the Wales-Heitmeyer model captures the inferred frequency dependence of the source levels.

A Time-Domain Wavenumber Integration Model for Underwater Acoustics Based on the High-Order Finite Difference Method

Simulating the acoustic field excited by pulse sound sources holds significant practical value in computational ocean acoustics. Two primary methods exist for modeling underwater acoustic propagation in the time domain: the Fourier synthesis technique based on frequency decomposition and the time-domain underwater acoustic propagation model (TD-UAPM). TD-UAPMs solve the wave equation in the time domain without requiring frequency decomposition, providing a more intuitive explanation of the physical process of sound energy propagation over time. However, time-stepping numerical methods can accumulate numerical errors, making it crucial to improve the algorithm’s accuracy for TD-UAPMs. Herein, the time-domain wavenumber integration model SPARC was improved by replacing the second-order finite element method (FEM) with the high-order accuracy finite difference method (FDM). Furthermore, the matched interface and boundary (MIB) method was used to process the seabed more accurately. The improved model was validated using three classic underwater acoustic benchmarks. By comparing the acoustic solutions obtained using the FDM and the FEM, it is evident that the improved model requires fewer grid points while maintaining the same level of accuracy, leading to lower computational costs and faster processing compared to the original model.

Maximum Likelihood Deconvolution of Beamforming Images with Signal-Dependent Speckle Fluctuations

Ocean Acoustic Waveguide Remote Sensing (OAWRS) typically utilizes large-aperture linear arrays combined with coherent beamforming to estimate the spatial distribution of acoustic scattering echoes. The conventional maximum likelihood deconvolution (DCV) method uses a likelihood model that is inaccurate in the presence of multiple adjacent targets with significant intensity differences. In this study, we propose a deconvolution algorithm based on a modified likelihood model of beamformed intensities (M-DCV) for estimation of the spatial intensity distribution. The simulated annealing iterative scheme is used to obtain the maximum likelihood estimation. An approximate expression based on the generalized negative binomial (GNB) distribution is introduced to calculate the conditional probability distribution of the beamformed intensity. The deconvolution algorithm is further simplified with an approximate likelihood model (AM-DCV) that can reduce the computational complexity for each iteration. We employ a direct deconvolution method based on the Fourier transform to enhance the initial solution, thereby reducing the number of iterations required for convergence. The M-DCV and AM-DCV algorithms are validated using synthetic and experimental data, demonstrating a maximum improvement of 73% in angular resolution and a sidelobe suppression of 15 dB. Experimental examples demonstrate that the imaging performance of the deconvolution algorithm based on a linear small-aperture array consisting of 16 array elements is comparable to that obtained through conventional beamforming using a linear large-aperture array consisting of 96 array elements. The proposed algorithm is applicable for Ocean Acoustic Waveguide Remote Sensing (OAWRS) and other sensing applications using linear arrays.

Asymptotic Ray Method for the Double Square Root Equation

The parabolic wave equation describes wave propagation in a preferable direction, which is usually horizontal in underwater acoustics and vertical in seismic applications. For dense receiver arrays (receiver spacing is less than signal wavelength), this equation can be used for propagating the recorded wavefield back into the medium for imaging sources and scattering objects. Similarly, for multiple source and receiver array acquisition, typical for seismic exploration and potentially beneficial for ocean acoustics, one can model data in one run using an extension of the parabolic equation—the pseudo-differential Double Square Root (DSR) equation. This extended equation allows for the modeling and imaging of multi-source data but operates in higher-dimensional space (source, receiver coordinates, and time), which makes its numerical computation time-consuming. In this paper, we apply a faster ray method for solving the DSR equation. We develop algorithms for both kinematic and dynamic ray tracing applicable to either data modeling or true-amplitude recovery. Our results can be used per se or as a basis for the future development of more elaborated asymptotic techniques that provide accurate and computationally feasible results.

An overview of physics-based data science techniques in ocean acoustics

Scientific applications in ocean acoustics include inversion, signal detection, seabed classification, source localization, and environmental assessment. Traditionally, addressing these problems commonly rely on modeling, deterministic setups, or optimization methods. However, these solutions encounter challenges when faced with non-ideal data or experimental conditions, leading to ill-posed problems or assumptions that deviate from reality. Through a new approach in recent years the integration of data science and machine learning techniques has emerged as a promising solution across various research fields. Only recently have these methodologies been introduced to the field of ocean acoustics, offering efficient and alternative for addressing problems through data-driven approaches. This study presents a collection of techniques developed by the University of Delaware, focusing on acoustic and environmental assessment. The methodologies employed include statistical approaches like maximum entropy and data-driven methods, ranging from simpler strategies such as dictionary learning and image segmentation, to more sophisticated structures like convolutional neural networks and graph neural networks, the latter can exploit spatial information in the data. The results obtained from these diverse methods offer valuable insight, paving the way for innovative alternatives to physics-based problem-solving in ocean acoustics.

The Graduate Program in Acoustics at Penn State

The Graduate Program in Acoustics at Penn State offers graduate degrees (M.Eng., M.S., Ph.D.) in Acoustics, with courses and research opportunities in a wide variety of subfields. Our 820 alumni are employed around the world in military and government labs, academic institutions, consulting firms, and consumer audio and related industries. Our 40 + faculty from several disciplines conduct research and teach courses in structural acoustics, nonlinear acoustics, architectural acoustics, signal processing, aeroacoustics, biomedical ultrasound, transducers, computational acoustics, noise and vibration control, acoustic metamaterials, psychoacoustics, and underwater acoustics. Course offerings include fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, experimental techniques, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, biomedical ultrasound, flow induced noise, spatial sound and three-dimensional audio, and the acoustics of musical instruments. Distance education students pursuing the M.Eng. degree join resident students in a hybrid classroom environment. This poster highlights faculty research areas, laboratory facilities, student demographics, successful graduates, and recent enrollment and employment trends.

Evaluation of data-driven neural operators in ocean acoustic propagation modeling

Various types of neural networks have been employed to tackle a wide range of complex partial differential equations (PDEs) and ordinary differential equations (ODEs). Notably, neural operators such as DeepONet and FNO show promise in handling these problems, offering potential real-time prediction capabilities. In contrast to physics-informed neural network (PINN) methods, neural operators primarily derive insight from extensive, well-prepared datasets. In the context of ocean acoustic propagation modeling, where the challenge involves solving the wave or Helmholtz equation given specific boundary conditions, this study focuses on assessing the performance of data-driven neural operators in predicting sound pressure. Unlike conventional approaches that map between finite-dimensional Euclidean spaces, neural operators excel in learning mappings between infinite-dimensional function spaces—a particularly advantageous feature in sound propagation modeling tasks. This research specifically delves into evaluating the generalization capabilities of neural operators when applied to sound propagation modeling in a range-independent shallow water environment. By exploring the neural operators' effectiveness in this domain, the study aims to contribute valuable insights into their potential applications for real-world ocean acoustics simulations.

Solutions to muddy (geoacoustic inversion) problems

Waveguide propagation is a ubiquitous topic in ocean acoustics. This presentation focuses on low-frequency (f < 500 Hz) sound propagation in coastal oceans (water depth shallower than 500 m). These environments are highly dynamical and complex, and act as dispersive waveguides, bounded by the sea-surface and the seafloor. The underwater acoustic field can thus be described by a set of modes that propagate with frequency-dependent speeds. Propagation of transient acoustic signals to a distant receiver (range > 1 km) accrues multi-modal dispersion information about the propagation medium. To extract relevant information about the oceanic environment from the acoustic recording, physics-based processing methods must be used. In this presentation, I will briefly review modal propagation and time-frequency analysis. I will then show how these approaches can be combined into a non-linear signal processing method dedicated to extracting modal information from a single receiver: this information is the foundation of a transdimensional inversion method used to characterize the oceanic environment. This method will be used to perform geoacoustic inversion on the New England Mud Patch. Using several datasets collected under different oceanographic conditions, I will notably demonstrate a successful estimation of the seafloor properties, consistent with geophysical core measurements and other inversion studies, even when the water column is dynamical and mostly unknown.

Inference of geoacoustic model parameters from acoustic field data: Perspectives on Geoacoustic Inversion

Estimation of parameters of geoacoustic models from acoustic field data has been a central research theme in acoustical oceanography and ocean acoustics. During the past several decades, highly efficient numerical inversion techniques have been developed that provide model parameter estimates and their uncertainties based on statistical inference methods. However, the methods are model-based and the inversions are prone to errors related to model mismatch. In any event, the inversions can generate only effective models of the true structure of the ocean bottom, which is generally highly variable over relatively small spatial scales in range and depth. There are also questions about the theory for modelling sound propagation in porous sediment media that raise doubt about the validity of inversion results. In most inversions, a visco-elastic theory is used, but is this the most appropriate propagation model? Another question is about the impact of neglecting shear waves in geoacoustic models. Most inversions assume a fluid model of the ocean bottom. This paper revisits issues that have raised questions about limitations of geoacoustic inversion methods, and discusses the impact of various mitigation measures that have been applied. The paper concludes with musings about new inversion techniques based on machine learning.

Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data

High-resolution ocean sound speed profile (HROSSP) data is essential for ocean acoustic modeling and sonar performance evaluation. However, the large volume and storage requirements of this data severely restrict its practical application in ocean acoustics. In this paper, we propose a compression autoencoder specifically designed for managing HROSSP data (CAE-HROSSP) and investigate the optimal network structure. Experimental results demonstrate that by using the min-max normalization method for input data and the corresponding inverse normalization for output data, along with employing the LeakyReLU function as the final activation layer, the accuracy of decompressed data reconstruction can be significantly improved. To tackle the challenges of fitting the distribution of surface sound speed data caused by significant variations and noise, we propose two loss functions: slice mean square error and elemental mean square error. These loss functions are combined with mean squared error through weighted summation to enhance CAE-HROSSP’s ability to fit the distribution of surface sound speed values and minimize the reconstruction errors of compressed data. Performance evaluation experiments reveal that CAE-HROSSP outperforms two existing methods in compressing HROSSP data, achieving superior performance with smaller data reconstruction errors at higher compression ratios. Furthermore, transfer learning is utilized to enhance the training of CAE-HROSSP, employing HROSSP data from the area where the mesoscale eddy is situated, as well as at the convergence of cold and warm ocean currents. The compression performance of both the training set and the validation set is comparable in the sea, where the structure of the sound speed profile varies greatly. This indicates that CAE-HROSSP can compress highly variable sound speed profile data in more sea areas using transfer learning, and has the potential to be extended globally. The findings and insights obtained from this study provide guidance for future endeavors in utilizing autoencoders to compress HROSSP data.

Testing transfer learning for source ranging in a laboratory tank

One difficulty in applying deep learning techniques to ocean acoustics is the spatially and temporally varying environmental properties. Another challenge is the lack of labeled data for training large networks. The overall goal of this work is to develop deep learning approaches that can be adaptable to different conditions. For example, a trained neural network will fail to generalize when the appropriate environmental variability is not included in the training data. To improve network performance, transfer learning can modify a pre-trained network to make predictions on data that was recorded under different conditions than the original dataset. In this work, a convolutional neural network was trained on acoustic data measured in a water tank, while the water was at room temperature, to predict source-receiver range. Transfer learning was used to update the pre-trained model with a smaller set of data measured at different water temperatures. The resulting model better generalizes to measurements at different temperatures. This approach illustrates how transfer learning can be used in ocean acoustics to improve generalizability in a specific area with less labeled data and lower computational cost. [Work supported by the Office of Naval Research, Grant N00014-22-12402.]

The ONR three octave research array at Penn State

A new, large diameter towed research array has been built to support the experimental efforts of the Office of Naval Research (ONR) Ocean Acoustics Program. Array development was a collaborative effort between the Centre for Maritime Research and Experimentation (CMRE) in La Spezia, Italy, and the Penn State Applied Research Laboratory (PSU). PSU chose the array specifications based on discussions within the underwater acoustics community, prior measurement experience, cost efficiency, and flexibility in scientific measurements, while CMRE designed and assembled the array. The design includes three, forward-nested acoustic apertures cut for 1, 2, and 4 kHz, and hence, has been dubbed the Three Octave Research Array (THORA) in deference to its predecessor, the Five Octave Research Array (FORA). However, this system has a modular design to allow addition of acoustic apertures if scientific interest and sponsor support warrant. The array currently consists of a 50 m acoustic module and a 25 m vibration isolation module to help isolate the acoustic module from cable strum. This ship towed system has nearly 1 km of tow cable and a maximum measurement depth of 500 m. The array’s design, its use in the ONR NESMA23 Pilot experiment, and data quality will be discussed.

Simultaneous integration of acoustic and environmental data in a graph-based framework for underwater waveguide analysis

Accurate assessment of underwater acoustic propagation relies heavily on understanding variability in the waveguide. Typically, prevailing methods in the field use environmental data exclusively to evaluate water column fluctuations. This study breaks new ground by integrating a graph-based approach, merging insight from acoustics and environmental data to comprehensively analyze ocean fluctuations. The proposed framework leverages data obtained during the Shallow Water ‘06 Experiment (SW06) from a densely measured region in the experimental area. The data used in this work include acoustic broadband signals below 1 kHz from a 48-element L- shape array and environmental measurements from 59 thermistors across 16 mooring arrays, affected by the passing of internal waves. The spatial relationship in the data, facilitated by sensor locations, is harnessed through a graph built upon underlying features from the datasets, providing a nuanced understanding of environmental variability in the water column. The proposed framework is not confined to theoretical constructs; it is substantiated through rigorous model evaluation on the measured data. This validation process robustly demonstrates the generalization power of the proposed architecture, affirming its effectiveness in a highly fluctuating water column and showcasing its potential for advancing underwater acoustic research. [Work supported by ONR Ocean Acoustics program.]

Further development of high-amplitude underwater sound sources at The University of Texas Applied Research Laboratories

Following the early research on the Combustive Sound Source (CSS), described in the previous talk, additional development occurred in the 2000’s and beyond, which included work on both the CSS and a new source, the Rupture Induced Underwater Sound Source (RIUSS). Development of the CSS in this era included maximizing the output of the source, investigating the use of CSS in arrays, and towing the source. The CSS was also deployed in surveys conducted during Shallow Water ’06 and the Seabed Characterization Experiment. In part due to lessons learned during those surveys, the RIUSS was conceived and developed. The RIUSS functions by placing a rupture disk over an evacuated chamber and mechanically breaking the disk (either by striking on demand or via hydrostatic pressure) at a specified depth to initiate a volume collapse that produces an impulsive acoustic waveform. The development and use of each sound source will be provided along with high-speed underwater video, examples of signatures, and examples of the efficacy of the sources in ocean acoustics research. [Work Supported by ONR and NAVO].

Measurements of the frequency dependence of geo-alpha in clayey silt at low frequencies

Transmission loss (TL) measurements in the Santa Barbara Channel, together with co-located chirp measurements, show that geo-alpha (dB/l) in clayey silt increases approximately linearly with frequency (f) at f < 3 kHz. TL was measured between a fixed source and fixed vertical array at 3.7 km at 0.3 < f < 5 kHz. Geo-alpha was inverted from the TL data at 0.3, 1.1 and 1.9 kHz. Inversion calculations at 1.1 and 1.9 kHz considered both geo-alpha and bio-alpha due to layer of anchovies at 12 m. Inversion calculations at 0.3 kHz considered only the effect of geo-alpha; the effect bio-alpha on TL was small. Geo-alpha was also inferred from chirp sonar measurements at 3.2 and 6.2 kHz at the ends and middle of the TL track. Chirp sonar showed that the bottom consisted of a layer of unconsolidated sediments overlying a layer of sand. Co-located cores revealed that the unconsolidated layer consisted of clayey silt. Bio-alpha data were compared to Biot and VGS theories and the theory of attenuation in suspended sediments (Pierce et al., 2017). The measured frequency dependence is consistent with Pierce et al.’s theory. This research was supported by The Office of Naval Research Ocean Acoustics Program.

Characterization of Fiber Metal Laminates for the Development of Subsea Housing

Fiber Metal Laminates (FML) are hybrid composites comprising metals and Fiber Reinforced Plastics (FRP). FMLs are the most widely used in aerospace, defence and automotive sectors due to their superior qualities like light weight, tensile, compression, flexural, excellent fatigue and impact resistance. The properties like strength-to-weight ratio, susceptibility to corrosion and good heat conduction of FML make it suitable for subsea applications. Commonly, FML with a combination of aluminium (Al), titanium (Ti), stainless steel (SS) alloys and FRP are widely used for ocean applications. Compared to other FML, the SS alloy-based FML is typically used in subsea applications as it has more creep and excellent corrosion resistance. In India, under the Ocean Acoustics programme of the National Institute of Ocean Technology (NIOT), an autonomous underwater Ambient Noise Measurement System (ANMS) has been developed and deployed in the shallow waters of Indian seas for the past 12 years to study the background noise prevailing in the sea. To accommodate electronics and power packs for the measurement of ambient noise at an ocean depth of 100 m, subsea housing with stainless steel 316L (SS316L) material for a pressure rating of 1 MPa has been developed. The objective of this study is to develop the FML with SS316L and FRP for reducing the weight of the housing. Based on the literature studies and Classical Laminate Theory (CLT), the FML has been fabricated as a 0.45 m (450 mm) panel with a sequence of SS316L as outer layers and E-glass fibre and carbon as the inner layers. The total thickness of the laminates is 0.006 m (6 mm). The developed FMLs are processed with water jet cutting machines to carry out various testing such as tensile, compression and flexural, which are relevant to the characterization of FML and the experimental results are described in the paper.

Effectiveness of ocean gliders in monitoring ocean acoustics and anthropogenic noise from ships: A systematic review

Underwater noise emissions from marine vehicles contribute to increasing ocean ambient noise levels, which is a threat to marine ecosystems. Passive acoustic monitoring, normally performed using moored-anchored systems, is commonly used to assess ocean soundscapes, vessel noise, and the presence of marine species. A systematic literature review was conducted to explore the use of autonomous underwater gliders (AUG) as an alternative to traditional moored systems for monitoring and assessing ship noise levels. We conducted thorough searches in Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We considered 13 articles on ocean glider applications for passive surveys and 8 articles on challenges in assessing underwater ship noise levels. Our findings were supplemented with international standards and guidelines from classification societies. Gliders are effective platforms for ship noise signature assessment due to their low noise signature and extended autonomy. In addition, AUGs can perform a 3D scan of the water column salinity, temperature, and density, which are needed to estimate sound propagation loss accurately. Further research is required to assess the potential of AUGs for accurately estimating ship noise levels by integrating oceanographic and acoustic data.