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Parallel tempering for strongly nonlinear geoacoustic inversion.

Stan E Dosso1, Charles W Holland, Malcolm Sambridge

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

The Journal of the Acoustical Society of America
|November 14, 2012
PubMed
Summary
This summary is machine-generated.

Parallel tempering improves Bayesian geoacoustic inversion for complex seabed models. This advanced Markov chain Monte Carlo method efficiently samples challenging parameter spaces, overcoming limitations of standard techniques.

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Area of Science:

  • Geophysics
  • Ocean Acoustics
  • Computational Seismology

Background:

  • Bayesian geoacoustic inversion estimates seabed parameters using ocean acoustic data and prior information.
  • Markov-chain Monte Carlo (MCMC) methods, like Metropolis-Hastings, are standard but inefficient for strongly nonlinear problems with multi-modal posterior probability densities (PPDs).
  • Inefficiencies can lead to missed parameter space regions and underestimated uncertainties.

Purpose of the Study:

  • To apply parallel tempering within a Bayesian framework for strongly nonlinear geoacoustic inverse problems.
  • To demonstrate the effectiveness of parallel tempering in overcoming sampling challenges posed by multi-modal PPDs.
  • To enhance the efficiency and accuracy of seabed parameter estimation.

Main Methods:

  • Implementation of parallel tempering, a MCMC technique, for Bayesian geoacoustic inversion.
  • Running parallel Markov chains at varying temperatures with probabilistic interchanges.
  • Application to the inversion of simulated acoustic reverberation data exhibiting a highly multi-modal PPD.

Main Results:

  • Parallel tempering achieved efficient and convergent sampling of the PPD, unlike standard Metropolis-Hastings.
  • The method successfully navigated and sampled multiple modes of the challenging PPD.
  • Demonstrated superior performance in exploring the parameter space for nonlinear geoacoustic problems.

Conclusions:

  • Parallel tempering is a highly effective method for tackling strongly nonlinear geoacoustic inverse problems.
  • It significantly improves upon standard MCMC sampling, providing more reliable seabed parameter estimates.
  • Further methods to enhance parallel tempering efficiency are explored for broader applicability.