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Estimating seabed scattering mechanisms via Bayesian model selection.

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This study introduces a Bayesian inversion method to identify seabed scattering mechanisms. The approach accurately classifies dominant scattering types, distinguishing between surface roughness and volume scattering from sediment layers.

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Area of Science:

  • Geophysics
  • Ocean Acoustics
  • Seabed Characterization

Background:

  • Seabed acoustic scattering is crucial for understanding seafloor properties.
  • Distinguishing between surface roughness and volume scattering is challenging.
  • Existing methods may not comprehensively address complex scattering scenarios.

Purpose of the Study:

  • To develop and apply a quantitative inversion procedure for determining dominant seabed scattering mechanisms.
  • To classify scattering as interface, volume, or mixed using Bayesian inversion.
  • To validate the method with simulated and real-world acoustic data.

Main Methods:

  • Quantitative inversion procedure utilizing trans-dimensional Bayesian inference.
  • Deviance Information Criterion (DIC) for model selection.
  • First-order perturbation theory to model scattering mechanisms (interface, volume, mixed).

Main Results:

  • Successfully identified the dominant scattering mechanism in 5 out of 6 simulated test cases.
  • Applied to measured data, the method determined volume scattering as dominant.
  • Inversion results correlated well with core data, providing estimates of heterogeneity size and scatterer depth.

Conclusions:

  • The developed Bayesian inversion method effectively classifies seabed scattering mechanisms.
  • The approach provides reliable estimates of sediment heterogeneity and sub-bottom scatterer locations.
  • This quantitative method enhances seabed characterization using acoustic scattering data.