Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves

TL;DR

This paper presents a Bayesian approach for acoustic seabed tomography that simultaneously estimates seabed features and roughness, addressing the ill-posed nature of the problem with a novel statistical isotropy-based method.

Contribution

It introduces a new infinite-dimensional Bayesian framework and a numerical algorithm for joint seabed and roughness estimation using wave scattering data.

Findings

Numerical experiments demonstrate the method's effectiveness.

The approach accurately estimates seabed properties and quantifies uncertainties.

The framework is promising for large-scale seabed exploration.

Abstract

This paper introduces an infinite-dimensional Bayesian framework for acoustic seabed tomography, leveraging wave scattering to simultaneously estimate the seabed and its roughness. Tomography is considered an ill-posed problem where multiple seabed configurations can result in similar measurement patterns. We propose a novel approach focusing on the statistical isotropy of the seabed. Utilizing fractional differentiability to identify seabed roughness, the paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties. Extensive numerical experiments validate the effectiveness of this method, offering a promising avenue for large-scale seabed exploration.