End-to-End Geoacoustic Inversion With Neural Networks in Shallow Water Using a Single Hydrophone

Vardi, Ariel; Bonnel, Julien

doi:10.1109/joe.2023.3331423

IEEE J. Oceanic Eng.

2024

DOI: 10.1109/joe.2023.3331423

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End-to-End Geoacoustic Inversion With Neural Networks in Shallow Water Using a Single Hydrophone

Ariel Vardi,

Julien Bonnel

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2024

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

Vardi,

Dahl,

Dall'Osto

et al. 2024

The Journal of the Acoustical Society of America

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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. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

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

Vardi,

Dahl,

Dall'Osto

et al. 2024

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

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End-to-End Geoacoustic Inversion With Neural Networks in Shallow Water Using a Single Hydrophone

Cited by 1 publication

References 30 publications

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

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

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