Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves

2026-02-01

AI Summary

This study addresses the ill-posed nature of joint inversion for seabed layering structure and roughness, a challenge arising from the non-uniqueness of acoustic responses across distinct subsurface configurations. The authors propose a novel infinite-dimensional Bayesian inversion framework grounded in acoustic scattering theory, which—by integrating statistical isotropy assumptions with fractional-order differentiability for the first time—simultaneously characterizes seabed roughness and estimates geoacoustic parameters. The approach mitigates ill-posedness through fractional-order differential regularization, high-fidelity wave scattering simulations, and rigorous uncertainty quantification. Extensive numerical experiments demonstrate that the method achieves high-fidelity reconstruction of both seabed topography and roughness while delivering reliable uncertainty estimates, thereby establishing a new paradigm for large-scale seabed acoustic inversion.

Technology Category

Search and Optimization: Mixed Discrete/Continuous SearchReasoning under Uncertainty: Stochastic OptimizationMachine Learning: Bayesian Learning

Application Category

Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsWeb Mining and Content Analysis: Robustness and generalizability of Web mining methodsSystems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• seabed tomography
• roughness estimation
• ill-posed problem
• acoustic scattering
• Bayesian inference

Innovation

• Bayesian inference
• seabed tomography
• surface roughness
• fractional differentiability
• wave scattering