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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Bayesian evidence computation for model selection in non-linear geoacoustic inference problems.

Jan Dettmer1, Stan E Dosso, John C Osler

  • 1School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada. jand@uvic.ca

The Journal of the Acoustical Society of America
|January 12, 2011
PubMed
Summary
This summary is machine-generated.

This study uses Bayesian inference for geoacoustic inversion of interface-wave data, providing robust sediment shear-wave velocity profiles. The method ensures accurate model selection and uncertainty analysis for geophysical exploration.

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Area of Science:

  • Geophysics
  • Oceanography
  • Bayesian Inference

Background:

  • Geoacoustic inversion is crucial for understanding seabed properties.
  • Interface-wave dispersion data offer insights into sediment layers.
  • Traditional methods may lack rigorous model selection and uncertainty quantification.

Purpose of the Study:

  • To apply a general Bayesian inference approach for geoacoustic inversion.
  • To perform quantitative model selection using Bayesian evidence computation.
  • To determine the sediment shear-wave velocity profile using interface-wave dispersion data.

Main Methods:

  • Bayesian evidence computation for model selection.
  • Annealed importance sampling for evidence calculation.
  • Metropolis-Hastings sampling for posterior probability density estimation.

Main Results:

  • Successfully inverted interface-wave dispersion data from the Scotian Shelf.
  • Obtained a sediment shear-wave velocity profile consistent with prior studies.
  • Validated results against core samples and seismic reflection data.

Conclusions:

  • The Bayesian inference approach provides a rigorous framework for geoacoustic inversion.
  • This method enhances model selection and uncertainty analysis in geophysical studies.
  • The findings contribute to a more accurate understanding of marine sediment properties.