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The presence of internal waves (IWs) in the ocean alters the isotropic properties of sound speed profiles (SSPs) in the water column. Changes in the SSPs affect underwater acoustics since most of the energy is dissipated into the seabed due to the downward refraction of sound waves. In consequence, variations in the SSP must be considered when modeling acoustic propagation in the ocean. Empirical orthogonal functions (EOFs) are regularly employed to model and represent SSPs using a linear combination of basis functions that capture the sound speed variability. A different approach is to use dictionary learning to obtain a learned dictionary (LD) that generates a nonorthogonal set of basis functions (atoms) that generate a better sparse representation. In this paper, the performance of EOFs and LDs are evaluated for sparse representation of SSPs affected by the passing of IWs. In addition, an LD-based supervised framework is presented for SSP classification and is compared with classical learning models. The algorithms presented in this work are trained and tested on data collected from the Shallow Water Experiment 2006. Results show that LDs yield lower reconstruction error than EOFs when using the same number of bases. In addition, overcomplete LDs are demonstrated to be a robust method to classify SSPs during low, medium, and high IW activity, reporting accuracy that is comparable to and sometimes higher than that of standard supervised classification methods.

Efforts to mentor undergraduates in research require time, energy, and resources but can yield significant benefits. A review of education research on undergraduate research is presented followed by a case study of how technical and educational principles from the literature have been implemented and practiced in a new underwater acoustics lab. This case study involves the students who have joined this lab over its first three years. The results highlight the importance of several key factors that mentors can implement to improve their students' research experience. In order to meet the steep learning curve that new students face, faculty mentors should establish a culture of research (i.e., well established norms, expectations, and practices associated with being a part of a research group) through clear expectations, open communication, and student-led peer mentorship supported by carefully selected or designed resources (i.e., scaffolding). This paper seeks to share these education research resources with the acoustics community while providing a case study for review as a specific application of these key principles into an acoustics laboratory setting.

Sounds to Astound is an acoustics demonstration show, produced for the community twice yearly by the Brigham Young University Student Chapter of the Acoustical Society of America. The free, interactive demonstration show explores the science of sound for a target audience of fifth- to eighth-grade students. Introductory acoustics concepts, such as longitudinal wave motion, wave properties, propagation effects, and standing waves, are taught with live demonstrations, animations, and videos. The goal of this paper is to inspire and encourage readers in their outreach efforts by describing the purposes of Sounds to Astound and technical details of several entertaining and educational demonstrations. Lessons learned from a decade of these student-produced shows serve as an aid for future efforts and highlight the benefits of outreach efforts, particularly for the students involved.

The Seabed Characterization Experiment 2017 yielded a rich set of environmental and acoustical data, and subsequent geoacoustic inversions have estimated seabed properties. Seventeen of these seabed parameterizations are now used to define seabed classes, and a convolutional neural network (CNN) is trained to select a seabed class using explosive sounds. The CNN is trained on synthetic 1-s pressure time series and then applied to measured data samples from a single pressure sensor. While the environmental variability in the training data impacts the seabed classification, physical insights are gained by considering the classification results as a function of the sound speed ratio across the sediment–water interface and the interval velocity of the top sediment layer. These results indicate that the selected seabed classes for the data samples with longer propagation distances consistently have similar sediment–water sound speed ratios that are less than unity, while the data samples with the shortest propagation distances consistently have higher interval velocities. These classification results indicate that the CNN has learned physical features associated with acoustic sound propagation and points to future work that needs to be considered if the seabed classes have acoustically distinct signatures in the data.

Sensitivity analysis is a powerful tool for analyzing multi-parameter models. For example, the Fisher information matrix (FIM) and the Cramér–Rao bound (CRB) involve derivatives of a forward model with respect to parameters. However, these derivatives are difficult to estimate in ocean acoustic models. This work presents a frequency-agnostic methodology for accurately estimating numerical derivatives using physics-based parameter preconditioning and Richardson extrapolation. The methodology is validated on a case study of transmission loss in the 50–400[Formula: see text]Hz band from a range-independent normal mode model for parameters of the sediment. Results demonstrate the utility of this methodology for obtaining Cramér–Rao bound (CRB) related to both model sensitivities and parameter uncertainties, which reveal parameter correlation in the model. This methodology is a general tool that can inform model selection and experimental design for inverse problems in different applications.

While seabed characterization methods have often focused on estimating individual sediment parameters, deep learning suggests a class-based approach focusing on the overall acoustic effect. A deep learning classifier—trained on 1D synthetic waveforms from underwater explosive sources—can distinguish 13 seabed classes. These classes are distinct according to a proposed metric of acoustic similarity. When tested on seabeds not used in training, the classifier obtains 96% accuracy for matching such a seabed to one of the top-3 most acoustically similar classes from the 13 training seabeds. This approach quantifies the performance of a seabed classifier in the face of real seabed variability.

Environmental inversions in ocean acoustics require the simultaneous estimation of many unknown parameters. Broad studies of multi-parameter models from diverse fields have shown that such inference problems are universally sloppy. Sloppiness is a phenomenon in which the predictions of a model are insensitive to all but a few key combinations of parameters. Sloppy model analysis is based on information geometry, an application of differential geometry to statistics. In this talk, we give an overview of information geometry for underwater acoustics models for environmental inversion, focusing on transmission loss (TL) in range-independent normal mode models. We demonstrate that these models are sloppy and how the geometry directly relates the information content of acoustic data to the relevance of environmental parameters. In particular, the model manifold quantifies what environmental information is encoded in ocean sounds and how unidentifiable parameters can be removed from the model to give a simplified, identifiable ocean acoustics model of comparable accuracy. We summarize physical insights revealed by this information geometry analysis for ocean inversion. [Work supported by Office of Naval Research]

During Seabed Characterization Experiment 2017 in the New England Mudpatch under near isospeed conditions of the water column, observations were made of well-defined intensity striations in time-frequency spectrograms in the 1.5–4.0 kHz band. These observations provided the basis for the hypothesis that the compressional sound speed profile for a homogenous fine-grained sediment possessed a gradient in the upper portions of the seabed. This hypothesis was tested with a statistical inference method that extracts probability density functions for parameter values representing an empirical-based nonlinear profile whose origins go back to Hamilton. Inferred was a surface sound speed that is less than the sound speed of the water at the seafloor and a surface sound speed gradient of about 6 1/s. In 2022 the experiment was repeated during non-isospeed conditions of the water column. The results for 2017 and 2022 are compared with the intent of understanding the response of the geoacoustic properties of the upper portions of a fine-grained sediment to changes in the bottom water temperature and salinity of the water column. [Work supported by Office of Naval Research.]

The variation of temperature in the ocean causes changes in sound propagation. To simulate the naturally occurring variability in an ocean environment, this project quantified the sound speed variation achievable in a laboratory water tank. The rectangular tank (1.2 m × 3.6 m with 0.5 m depth) has anechoic paneling that minimizes lateral reflections. In this experiment, temperature was measured from four different temperature sensors. The temperature was used to calculate the sound speed in the tank as a function of time while the water was cooled and heated. Sound speed values were compared from four different empirical equations. We found that while the temperature varies in time, the temperature is relatively uniform at different locations in the tank. In experiments involving three hours of heating and adding 100 pounds of ice, there was a 10-degree Celsius difference in temperature, corresponding to a 20 m/s difference in water sound speed. By adding an additional degree of variability to the tank measurements, a portion of the variability seen in the ocean can be replicated. This sound speed variability can then be used to test the robustness of machine learning algorithms.

Remote sensing using passive sonar in the ocean is a challenging problem due to variations in the geoacoustic structure of the seabed and unknown source location and strength. One way to improve remote sensing is to perform an optimization for geoacoustic and source parameters. We use a Bayesian maximum entropy (BME) approach with a viscous-grain shearing parameterization for two sediment layers. The statistical optimization provides probability distributions for porosity and thickness of the sediment layers as well as ship speed, closest point of approach, and the source strength for the Wales-Heitmeyer empirical source level spectrum. We use this approach on spectrograms of transiting ships collected on a vertical line array during the 2017 seabed characterization experiment. We compare the resulting parameter distributions from distinct ships as well as previous estimates of geoacoustic values and source properties. This research shows that the BME approach obtains estimates for porosity and source strength that have narrow posterior probability distributions. [Work supported by the Office of Naval Research.]

The connection between ocean environmental parameters and transmission loss (TL) can be modeled using sound propagation models. We can analyze and improve these models using information theoretic tools such as the Fisher information matrix (FIM) and methods for model reduction, where we identify parameters that can be removed from a model without sacrificing accuracy. Critical to these methods for model reduction is the ability to evaluate derivatives of the model predictions of TL with respect to model parameters. Calculating derivatives of TL models using finite difference methods with sufficient accuracy is challenging due to the complexity of sound fields in an ocean environment; instead, we train a surrogate machine learning model to which we can apply automatic differentiation (AD). We propose a general method for model reduction in which a ML model is used as a surrogate model for physical sound propagation ocean models, which is then used to find potential simplifications of the original model. We demonstrate this method using the Pekeris waveguide model of the ocean, which calculates underwater acoustic TL in an ocean environment due to seafloor characteristics. [Work supported by Office of Naval Research]

Dimensional reduction methods based on feature extraction are critical for statistical inference application in variable ocean environments. Presented are analyses for selected acoustic measurements made during the Seabed Characterization Experiment in 2021-2022. Broadband sources were deployed on the New England continental slope between the 200 and 400 m depth contours. The method of analysis includes first analyzing short range (higher grazing angles) acoustic measurements provided by both a broadband towed source and explosive sources. Features utilized for short-ranged data include those associated with seabed interferometry. Then, longer range data (lower grazing angles) are utilized to provide improved estimates of such quantities as the attenuations and sound speed gradients. Features utilized for long-ranged data include those associated with the dispersion of signals generated by explosive sources. As a general guide the mid-frequency band is utilized to infer characteristics about the upper portions of the sediment whereas the lower frequencies are utilized to infer the deeper portions of the seabed. [Work supported by Office of Naval Research.]

Deep learning has great potential to be useful for ocean acoustics problems. However, uncertainty in an ocean environment must be taken into account, because it affects the ability of trained machine learning models to generalize. Because ocean measurements are costly, we are using a laboratory water tank to help develop our deep learning neural networks. In addition, using a water tank in a lab increases some level of control over the amount of uncertainty in data used to train neural networks. A residual convolutional neural network architecture is trained to identify source-receiver range in an underwater tank environment at room temperature. The model is then tested on data taken with variable water temperature. For comparison, two different source signals are used (sine waves and linear chirps) to analyze the usefulness of broadband sounds. These tests explore how environmental uncertainty impacts source localization and will lead to insights on how to develop robust machine learning for ocean acoustics problems. [Work supported by the Office of Naval Research.]

Because ocean measurements are costly, a laboratory water tank provides opportunities to take scaled measurements and develop approaches to machine learning that are transferable to ocean acoustic problems. These applications are better achieved if reflections off the side walls are significantly reduced. To reduce these reflections, the side walls of the tank are lined with Aptile SF5048 echo reducing tiles made by Precision Acoustics. This work studies the extent to which two ocean sound propagation models (namely ORCA and BELLHOP) can predict relative transmission loss as a function of source-receiver range and frequency. The modeling parameters of sound speed and density that determine the reflections at the bottom of the tank were refined through a Bayesian optimization algorithm. We found that the optimized parameters improve the model-data agreement and that the models fit the data at certain frequencies corresponding mainly to peaks in the echo reduction for the Aptile panels. This agreement implies that at certain frequencies, sound propagation in our tank acts as it would in a large body of water. Using our tank as a testbed for developing machine learning algorithms is a reasonable approach.

Since the ocean is not a convenient place to conduct research, data are often collected from offshore buoys. However, the data collected from the buoys usually do not have labels (e.g., source of the sound) attached to them. Manually labeling the data is time-consuming, but labeled data are necessary to use powerful supervised machine learning methods. We propose that unsupervised learning can be used to assign preliminary labels to data samples. To show the utility of this approach, we took an echosounder to Utah Lake and collected sonar images, some of which showed evidence of fish. We used Python to crop the images, similar to how regions of interest are identified in image processing. These cropped images are used in an unsupervised k-means clustering algorithm. The images are clustered based on visual features. An analysis of the resulting clusters and the feasibility of using unsupervised learning to provide preliminary labels to data is discussed.

Deep learning can assist in characterizing seabeds using sources of opportunity such as shipping noise. While previous work focused on seabed classification, this study uses a residual convolutional neural network to find individual seabed properties. The training data were labeled with sound speed, density, attenuation, and thickness of the layer values of the top sediment layer. A comparison was made between predictive capabilities of ResNet-18 networks when trained to learn a single parameter and those trained to simultaneously learn multiple parameters. For stiff parameters—those with high information content in the data—learning an individual parameter performed better. These single parameter predictions are fundamentally different from a geoacoustic inversion for one parameter. In geoacoustic inversion, all other parameters are held at a fixed value. In deep learning, variability in all other parameters is contained in the training data, but the network focuses on features in the data related to a single property. The trained networks are applied to ship noise measured during the 2017 Seabed Characterization Experiment. [Work supported by the Office of Naval Research and the National Science Foundation’s REU program.]

Because the seabed impacts sound propagation in the ocean, machine learning is being used for both seabed classification and to obtain estimates of individual seabed properties. This paper proposes a method to simultaneously estimate these properties and an associated uncertainty label. A residual neural network is trained and validated using synthetic ship noise spectrograms generated with a range-independent normal mode sound propagation model and a ship noise source spectrum. The data set includes 140 seabeds: In each, the top sediment layer has a random thickness and properties randomly chosen from five sets of bounds, which roughly correspond to clay, mud, sand, silt, and gravel. For each of the 140 sediments, a random selection of 405 combinations of ship speed, closest-point-of-approach range, and source depth are used resulting in 22k data samples. Each data sample is labeled with the true values of the sediment parameters as well as a label for the uncertainty level of each. The uncertainty levels are obtained from the Fisher information and qualify of the information content in the ship spectrogram about each parameter. Examples of how the residual neural network learns to perform regression for the parameter value and the uncertainty level will be presented.

Graduate studies in acoustics at Brigham Young University prepare students for industry, research, and academia by complementing in-depth coursework with publishable research. Coursework provides a solid foundation in core acoustical principles and practices and measurement skills, including a strong foundation in experimental techniques and technical writing. Labs across the curriculum cover calibration, directivity, scattering, absorption, laser Doppler vibrometry, experimental methods for dynamic structures, lumped-element mechanical systems, equivalent circuit modeling, arrays, filters, room acoustics, active noise control, and near-field acoustical holography. Recent thesis and dissertation topics include active noise control, directivity, room acoustics, energy-based acoustics, time reversal, nondestructive evaluation, vibration and acoustics of aerospace vehicles, biomedical applications, flow-based acoustics, voice production, aeroacoustics, sound propagation modeling, nonlinear propagation, high-amplitude noise analyses, machine and deep learning applied to ambient noise level prediction, crowd noise interpretation, and underwater acoustic source localization, and ocean environment classification. Graduate students are expected to present research at professional meetings and publish in peer-reviewed acoustics journals. Additionally, graduate students often serve as peer mentors to undergraduate students on related projects and may participate in field experiments to gain additional experience.@BYUAcoustics

In this study the characteristics of the modal dispersion of acoustic waves in littoral ocean environments are utilized to infer both source properties (range, depth, speed, and source level) and geoacoustic properties of the seabed. An analysis is made of broadband acoustic data (10–4500 Hz) taken during 2021–2022 on the New England continental slope over water depths from 200 to 500 m. The acoustic data are generated by both moving broadband sources and explosive sources over range scales of 1–30 km. Such range scales can reveal the nature of the modal dispersion. Conductivity, Temperature, and Depth (CTD) and Expendable Bathythermographs (XBTs) measurements provide a sparse sampling of the temporal and spatial variability of the ocean during the acoustic measurements. Along with bathymetry measurements the sound speed profiles derived from the CTDs and XBTs provide prior information for an inverse problem from which insight can be obtained on the effects of the seabed on the dispersion of broadband propagation.

The impact of individual seabed properties on sound propagation in the ocean depends on many factors including source-receiver range and frequency band of interest. In this talk, estimates of the sound speed ratio across the water-sediment interface are obtained using a maximum entropy approach and ResNet18, a supervised machine learning model. The input data are spectrograms of surface ship noise from shipping lanes. Synthetic spectrograms are modeled using a ship noise source spectrum and a range-independent normal mode model, ORCA, with a wide range of environments and ship parameters. Experimental data from the New England Mud Patch are used with both inverse methods. The maximum entropy approach uses data-model mismatch to obtain a posterior probability distribution for the parameters of interest. The ResNet18 is trained on the synthetic spectrograms, augmented with additive noise, and then applied to the experimental data. A comparison of the results from these two methods for a variety of ships using different frequency bands will be presented, along with a discussion of the advantages and limitations of each method.