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Probabilistic two-dimensional water-column and seabed inversion with self-adapting parameterizations.

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This study introduces a probabilistic 2D inversion technique for geoacoustic environments. It accurately maps seabed and water column properties in complex, range-dependent ocean regions using advanced Bayesian methods.

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Area of Science:

  • Oceanography
  • Acoustics
  • Geophysics

Background:

  • Range-dependent environments are crucial in acoustical oceanography.
  • Efficient probabilistic inversion methods are needed for complex environments.

Purpose of the Study:

  • Develop a probabilistic 2D inversion for geoacoustic seabed and water-column parameters.
  • Address challenges in strongly range-dependent environments.

Main Methods:

  • Utilizes nonlinear inverse theory and advanced sampling techniques.
  • Employs self-adapting irregular grids within a trans-dimensional hierarchical Bayesian model.
  • Applies Metropolis-Hastings-Green algorithm and population Monte Carlo for efficient sampling.

Main Results:

  • Demonstrates accurate 2D inversion of geoacoustic parameters in simulated range-dependent scenarios.
  • Provides 2D ensemble parameter estimates and credibility intervals.
  • Highlights the effectiveness of self-adapting grids for parsimonious solutions.

Conclusions:

  • The developed probabilistic 2D inversion is effective for characterizing complex geoacoustic environments.
  • Advanced Bayesian methods and adaptive grids enable efficient and accurate parameter estimation.
  • This approach advances acoustical oceanography for shelf and shelf-break regions.