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Quantifying uncertainty in geoacoustic inversion. I. A fast Gibbs sampler approach.

Stan E Dosso1

  • 1School of Earth and Ocean Sciences, University of Victoria, British Columbia, Canada.

The Journal of the Acoustical Society of America
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Summary

This study introduces a fast Gibbs sampler (FGS) for Bayesian geoacoustic inversion, significantly reducing computation time. The FGS efficiently estimates seabed properties and uncertainties, improving upon traditional methods.

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Area of Science:

  • Geophysics
  • Oceanography
  • Acoustics

Background:

  • Seabed geoacoustic property estimation is crucial for underwater acoustics.
  • Bayesian inversion offers a robust framework for parameter estimation and uncertainty quantification.
  • Traditional methods for calculating posterior probability densities (PPD) can be computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient Bayesian approach for estimating seabed geoacoustic properties.
  • To improve the speed and reliability of uncertainty quantification in geoacoustic inversion.
  • To introduce and validate a novel fast Gibbs sampler (FGS) algorithm.

Main Methods:

  • Formulation of matched-field inversion within a Bayesian framework.
  • Development of a fast Gibbs sampler (FGS) algorithm, an optimization of the Gibbs sampler (GS).
  • Implementation of an automated convergence criterion using parallel FGS samples.
  • Comparison with standard Gibbs sampler and Monte Carlo integration.

Main Results:

  • The fast Gibbs sampler (FGS) provides rigorous estimates of PPD moments.
  • FGS requires orders of magnitude less computation time compared to traditional GS and Monte Carlo methods.
  • The automated convergence criterion effectively verifies sample convergence.
  • Successful application to noisy synthetic benchmark test cases.

Conclusions:

  • The FGS algorithm represents a significant advancement in computational efficiency for Bayesian geoacoustic inversion.
  • This method enables more rapid and reliable estimation of seabed geoacoustic properties and their uncertainties.
  • The FGS approach is well-suited for complex acoustic modeling and inversion problems.