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A statistical geoacoustic inversion scheme based on a modified radial basis functions neural network.

George Tzagkarakis¹, Michael I Taroudakis, Panagiotis Tsakalides

¹Department of Computer Science, University of Crete and Institute of Computer Science - FO.R.T.H. FORTH-ICS, P.O. Box 1385, 711 10 Heraklion, Crete, Greece. gtzag@ics.forth.gr

The Journal of the Acoustical Society of America

|October 2, 2007

Summary

This study uses a novel neural network approach to recover shallow-water geoacoustic parameters from acoustic signals. The method efficiently extracts signal features for accurate environmental sound speed and density inversion.

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Area of Science:

Ocean Acoustics
Geophysics
Machine Learning

Background:

Accurate geoacoustic parameter estimation is crucial for understanding shallow-water environments.
Traditional inversion methods can be computationally intensive and sensitive to environmental complexities.

Purpose of the Study:

To develop and evaluate a novel neural network-based inversion technique for recovering geoacoustic parameters.
To introduce an efficient signal 'observable' derived from non-Gaussian statistics for improved inversion accuracy.

Main Methods:

A one-dimensional wavelet decomposition transforms acoustic signals into subband coefficients.
Alpha-stable distribution parameters are estimated from subband coefficients to create a novel 'observable'.
A radial basis functions neural network is trained to map observables to geoacoustic parameters (sound speed, density).

Main Results:

The proposed method demonstrates effective recovery of sound speed and density in simulated shallow-water environments.
The novel observable, based on alpha-stable distributions, enhances the efficiency and accuracy of the inversion process.
Performance was validated using synthetic acoustic data generated with a normal-mode propagation model.

Conclusions:

The neural network inversion approach, utilizing a statistically derived observable, offers a promising tool for geoacoustic parameter estimation.
This method provides an efficient and accurate alternative for characterizing shallow-water environments.
Further research can explore its application in real-world acoustic data and more complex scenarios.