Approximation of the inverse scattering Steklov eigenvalues and the inverse spectral problem

TL;DR

This paper develops a Galerkin method using Neumann eigenfunctions to approximate Steklov eigenvalues in inverse acoustic scattering, providing error estimates and exploring their use in non-destructive testing of inhomogeneous media.

Contribution

It introduces a novel Galerkin approach with error analysis for Steklov eigenvalues and investigates their application in recovering refractive indices from spectral data.

Findings

Galerkin method converges with proven error estimates.

Eigenvalues are monotone with respect to refractive index.

Numerical examples validate the inverse spectral approach.

Abstract

In this paper, we consider the numerical approximation of the Steklov eigenvalue problem that arises in inverse acoustic scattering. The underlying scattering problem is for an inhomogeneous isotropic medium. These eigenvalues have been proposed to be used as a target signature since they can be recovered from the scattering data. A Galerkin method is studied where the basis functions are the Neumann eigenfunctions of the Laplacian. Error estimates for the eigenvalues and eigenfunctions are proven by appealing to Weyl's Law. We will test this method against separation of variables in order to validate the theoretical convergence. We also consider the inverse spectral problem of estimating/recovering the refractive index from the knowledge of the Steklov eigenvalues. Since the eigenvalues are monotone with respect to a real-valued refractive index implies that they can be used for non-destructive testing. Some numerical examples are provided for the inverse spectral problem.