Seafloor identification in sonar imagery via simulations of Helmholtz equations and discrete optimization

TL;DR

This paper introduces a multiscale inverse problem approach for seafloor feature identification in sonar imagery, combining Helmholtz equations, geometrical optics, and a precomputed response library to improve accuracy and efficiency.

Contribution

It develops a novel multiscale framework integrating Helmholtz equations and microlocal analysis with a response library for efficient seafloor parameter recovery.

Findings

Accurate seafloor feature identification demonstrated in simulated data

Reduced computational cost via precomputed response library

Effective multiscale modeling across diverse seafloor geometries

Abstract

We present a multiscale approach for identifying features in ocean beds by solving inverse problems in high frequency seafloor acoustics. The setting is based on Sound Navigation And Ranging (SONAR) imaging used in scientific, commercial, and military applications. The forward model incorporates multiscale simulations, by coupling Helmholtz equations and geometrical optics for a wide range of spatial scales in the seafloor geometry. This allows for detailed recovery of seafloor parameters including material type. Simulated backscattered data is generated using numerical microlocal analysis techniques. In order to lower the computational cost of the large-scale simulations in the inversion process, we take advantage of a pre-computed library of representative acoustic responses from various seafloor parameterizations.