Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves
For geoscientists and engineers involved in seabed exploration, this method addresses the ill-posed inverse problem of acoustic tomography by providing robust estimates and uncertainty quantification.
This paper introduces an infinite-dimensional Bayesian framework that simultaneously estimates the seabed and its roughness using longitudinal wave scattering, validated through extensive numerical experiments.
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.