Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learninga).

This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones.

Semiautomated generation of species-specific training data from large, unlabeled acoustic datasets for deep supervised birdsong isolation.

Background: Bioacoustic monitoring is an effective and minimally invasive method to study wildlife ecology. However, even the state-of-the-art techniques for analyzing birdsongs decrease in accuracy in the presence of extraneous signals such as anthropogenic noise and vocalizations of non-target species. Deep supervised source separation (DSSS) algorithms have been shown to effectively separate mixtures of animal vocalizations.

Acoustic ducting by shelf water streamers at the New England shelfbreak.

Greater sound speed variability has been observed at the New England shelfbreak due to a greater influence from the Gulf Stream with increased meander amplitudes and frequency of Warm Core Ring (WCR) generation. Consequently, underwater sound propagation in the area also becomes more variable. This paper presents field observations of an acoustic near-surface ducting condition induced by shelf water streamers that are related to WCRs.

Seabed classification from merchant ship-radiated noise using a physics-based ensemble of deep learning algorithms.

Merchant ship-radiated noise, recorded on a single receiver in the 360-1100 Hz frequency band over 20 min, is employed for seabed classification using an ensemble of deep learning (DL) algorithms. Five different convolutional neural network architectures and one residual neural network are trained on synthetic data generated using 34 seabed types, which span from soft-muddy to hard-sandy environments. The accuracy of all of the networks using fivefold cross-validation was above 97%.

Validating deep learning seabed classification via acoustic similarity.

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.

Seabed type and source parameters predictions using ship spectrograms in convolutional neural networks.

Broadband spectrograms from surface ships are employed in convolutional neural networks (CNNs) to predict the seabed type, ship speed, and closest point of approach (CPA) range. Three CNN architectures of differing size and depth are trained on different representations of the spectrograms. Multitask learning is employed; the seabed type prediction comes from classification, and the ship speed and CPA range are estimated via regression.

Seabed and range estimation of impulsive time series using a convolutional neural network.

In ocean acoustics, many types of optimizations have been employed to locate acoustic sources and estimate the properties of the seabed. How these tasks can take advantage of recent advances in deep learning remains as open questions, especially due to the lack of labeled field data. In this work, a Convolutional Neural Network (CNN) is used to find seabed type and source range simultaneously from 1 s pressure time series from impulsive sounds.

The Airy phase of explosive sounds in shallow water.

The Airy phase is identified in the received signals from explosive charges deployed in a shallow water acoustic experiment conducted in the New England Mudpatch region during the spring of 2017. Measured and modeled time-frequency dispersion curves are compared and a geoacoustic sensitivity study utilizing marginal probability distributions for the sound speed in five sediment layers is performed. The analysis suggests that inclusion of the Airy phase frequency and arrival time in a geoacoustic-inversion method could lower the uncertainty of sound speed parameter estimation in a multi-layer sediment as compared to methods that do not include the Airy phase structure.

Geoacoustic inversion using low frequency broadband acoustic measurements from L-shaped arrays in the Shallow Water 2006 Experiment.

When using geoacoustic inversion methods, one objective function may not result in a unique solution of the inversion problem because of the ambiguity among the unknown parameters. This paper utilizes acoustic normal mode dispersion curves, mode shapes, and modal-based longitudinal horizontal coherence to define a three-objective optimization problem for geoacoustic parameter estimation. This inversion scheme is applied to long-range combustive sound source data obtained from L-shaped arrays deployed on the New Jersey continental shelf in the summer of 2006.

Evidence of three-dimensional acoustic propagation in the Catoche Tongue.

This paper presents observations of two classes of acoustic arrivals recorded on a sparsely populated vertical line array (VLA) moored in the center of the Catoche Tongue, a major reentrant in the Campeche Bank in the southeastern Gulf of Mexico. The acoustic signals were generated by signals underwater sound (SUS) located 50-80 km from the VLA. The first class of arrivals was identified as resulting from a direct (non-horizontally refracted) path.

An impulsive source with variable output and stable bandwidth for underwater acoustic experiments.

The Combustive Sound Source (CSS) is being developed as an environmentally friendly source to be used in ocean acoustics research and surveys. It has the ability to maintain the same wide bandwidth signal over a 20 dB drop in source level. The CSS consists of a submersible combustion chamber filled with a fuel/oxidizer mixture.

Statistical inference of seabed sound-speed structure in the Gulf of Oman Basin.

Addressed is the statistical inference of the sound-speed depth profile of a thick soft seabed from broadband sound propagation data recorded in the Gulf of Oman Basin in 1977. The acoustic data are in the form of time series signals recorded on a sparse vertical line array and generated by explosive sources deployed along a 280 km track. The acoustic data offer a unique opportunity to study a deep-water bottom-limited thickly sedimented environment because of the large number of time series measurements, very low seabed attenuation, and auxiliary measurements.

Scattering in a Pekeris waveguide from a rough bottom using a two-way coupled mode approach.

Scattering from a rough ocean bottom is described numerically with a two-way coupled-mode formalism that contains scattering effects to all orders and provides an exact solution to the wave equation. Both scattered field and direct blast components are computed within the formalism framework. A comparison of the scattered component solution from the coupled mode with the Born approximation (BA) solution for scattering from a rough bottom Pekeris waveguide shows that the BA predicts correctly the scattered field levels but not detailed structure.

Multichannel array diagnosis using noise cross-correlation.

A practical application of noise cross-correlation for the diagnosis of a multichannel ocean hydrophone array is derived. Acoustic data were recorded on a horizontal line array on the New Jersey Shelf while Tropical Storm Ernesto passed through. Results obtained from active source measurements reveal that signals from several hydrophones, which were recorded on certain channels before the storm, are recorded on different channels after the storm.

Geoacoustic inversion with ships as sources.

Estimation of geoacoustic parameters using acoustic data from a surface ship was performed for a shallow water region in the Gulf of Mexico. The data were recorded from hydrophones in a bottom mounted, horizontal line array (HLA). The techniques developed to produce the geoacoustic inversion are described, and an efficient method for geoacoustic inversion with broadband beam cross-spectral data is demonstrated.