Inverse acoustic scattering problem in half-space with anisotropic random impedance

TL;DR

This paper addresses the inverse problem of determining the anisotropic covariance of a Gaussian random impedance boundary in a half-space from backscattered acoustic data, establishing unique reconstruction without approximations.

Contribution

It introduces a novel analysis of an anisotropic spherical Radon transform and proves the unique determination of the covariance's principal symbol from frequency-averaged backscattering data.

Findings

Unique determination of the covariance's principal symbol.

Development of an invertible anisotropic spherical Radon transform.

Solution of the full non-linear inverse problem without approximations.

Abstract

We study an inverse acoustic scattering problem in half-space with a probabilistic impedance boundary value condition. The Robin coefficient (surface impedance) is assumed to be a Gaussian random function $\lambda = \lambda(x)$ with a pseudodifferential operator describing the covariance. We measure the amplitude of the backscattered field averaged over the frequency band and assume that the data is generated by a single realization of $\lambda$. Our main result is to show that under certain conditions the principal symbol of the covariance operator of $\lambda$ is uniquely determined. Most importantly, no approximations are needed and we can solve the full non-linear inverse problem. We concentrate on anisotropic models for the principal symbol, which leads to the analysis of a novel anisotropic spherical Radon transform and its invertibility.