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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Published on: March 1, 2022

A Bayesian approach to modal decomposition in ocean acoustics.

Zoi-Heleni Michalopoulou

    The Journal of the Acoustical Society of America
    |November 10, 2009
    PubMed
    Summary

    This study introduces a Bayesian method for analyzing underwater acoustic signals. It accurately estimates modal frequencies and amplitudes, providing crucial uncertainty information for geoacoustic inversion.

    Area of Science:

    • Oceanography
    • Acoustics
    • Signal Processing
    • Geophysics

    Background:

    • Underwater acoustic signals contain vital information about the marine environment.
    • Modal decomposition is essential for analyzing these complex signals.
    • Traditional methods often lack uncertainty quantification for modal characteristics.

    Purpose of the Study:

    • To develop a Bayesian approach for modal decomposition of broadband acoustic signals.
    • To accurately estimate modal frequencies and amplitudes with associated uncertainty.
    • To enable improved geoacoustic inversion using detailed modal information.

    Main Methods:

    • Utilized a Bayesian framework for modal decomposition.
    • Employed time-frequency representations of acoustic signals.

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    Published on: October 24, 2012

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  • Optimized the approach using Gibbs sampling for posterior distribution estimation.

    • Main Results:

    • Achieved accurate estimation of modal frequencies and amplitudes.
    • Provided posterior probability distributions for modal characteristics.
    • Demonstrated the capability to quantify uncertainty in modal analysis.

    Conclusions:

    • The Bayesian approach offers a robust method for modal decomposition in underwater acoustics.
    • The derived uncertainty information enhances the reliability of geoacoustic inversion.
    • This method advances the analysis of acoustic propagation in marine environments.