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Broadband modal phase speed estimation and geoacoustic inversion with sparse Bayesian learning using multi-range

Shanru Lin^1,2, Haiqiang Niu^1,2, Zhenglin Li^3,4

¹State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.

The Journal of the Acoustical Society of America

|September 24, 2025

Summary

This summary is machine-generated.

A novel multi-range sparse Bayesian learning (SBL) method enhances geoacoustic inversion in shallow waters. This technique accurately estimates broadband modal phase speed, improving underwater acoustic localization.

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Area of Science:

Oceanography
Acoustics
Signal Processing

Background:

Geoacoustic inversion in shallow waters is crucial for underwater acoustics.
Traditional methods face limitations like narrowband constraints and parameter interference.

Purpose of the Study:

To propose a broadband modal phase speed estimation method using multi-range sparse Bayesian learning (multi-range SBL).
To improve geoacoustic inversion accuracy and localization in shallow water environments.

Main Methods:

Utilizing multi-range signals from a vertical line array for local modal depth functions and horizontal wavenumbers.
Employing sparse Bayesian learning (SBL) to overcome narrowband limitations and decouple seabed attenuation.
Applying Bayesian optimization for efficient multi-dimensional parameter estimation.

Main Results:

Accurate estimation of horizontal wavenumbers over a broad bandwidth.
Successful geoacoustic inversion by fitting broadband phase speed curves.
Decoupled estimation of seabed attenuation coefficient, reducing mutual interference.
Improved range and depth estimation compared to conventional methods.

Conclusions:

The multi-range SBL method provides a robust and accurate approach for shallow water geoacoustic inversion.
The method's ability to handle local modes and reduce parameter interference enhances inversion reliability.
Validated through simulations and experimental data, demonstrating superior performance in localization.