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Parallel tempering in trans-dimensional Bayesian geoacoustic inversion for high-information-content data and

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Trans-dimensional Bayesian inversion improves seabed geoacoustic modeling by probabilistically sampling layer numbers and parameters. Parallel tempering enhances this method for complex problems with highly informative data.

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Area of Science:

  • Ocean acoustics
  • Geophysical inversion
  • Bayesian inference

Background:

  • Trans-dimensional (trans-D) Bayesian inversion is key for seabed geoacoustic model estimation.
  • It combines model selection and uncertainty quantification.
  • Challenges arise when data is highly informative or models are complex, reducing dimension change acceptance probability.

Purpose of the Study:

  • To investigate the efficacy of parallel tempering in trans-D Bayesian inversion.
  • To address limitations in dimension change acceptance for challenging inversion problems.

Main Methods:

  • Utilizing parallel tempering, a technique involving multiple interacting Markov chains with varying likelihoods.
  • Applying this to trans-D Bayesian inversion for geoacoustic model estimation.

Main Results:

  • Parallel tempering improves the acceptance rate of dimension changes in trans-D inversion.
  • This leads to more efficient and robust estimation of seabed geoacoustic models, even with complex data.

Conclusions:

  • Parallel tempering is an effective strategy to overcome convergence issues in trans-D Bayesian inversion.
  • This advancement facilitates more accurate seabed geoacoustic characterization from ocean-acoustic data.