High resolution inverse scattering in two dimensions using recursive linearization

TL;DR

This paper presents a fast, stable recursive linearization algorithm for high-resolution inverse acoustic scattering in two dimensions, capable of reconstructing sound speed profiles with thousands of unknowns from full aperture data.

Contribution

It introduces a fully nonlinear, efficient method using recursive linearization and spectral solvers to achieve high-resolution reconstructions in inverse scattering problems.

Findings

Reconstructed sound speed profiles with thousands of unknowns.

Achieved high-resolution imaging at resolutions of thousands of wavelengths.

Demonstrated computational feasibility for large-scale problems with nearly two days of computation.

Abstract

We describe a fast, stable algorithm for the solution of the inverse acoustic scattering problem in two dimensions. Given full aperture far field measurements of the scattered field for multiple angles of incidence, we use Chen's method of recursive linearization to reconstruct an unknown sound speed at resolutions of thousands of square wavelengths in a fully nonlinear regime. Despite the fact that the underlying optimization problem is formally ill-posed and non-convex, recursive linearization requires only the solution of a sequence of linear least squares problems at successively higher frequencies. By seeking a suitably band-limited approximation of the sound speed profile, each least squares calculation is well-conditioned and involves the solution of a large number of forward scattering problems, for which we employ a recently developed, spectrally accurate, fast direct solver. For the largest problems considered, involving 19,600 unknowns, approximately one million partial differential equations were solved, requiring approximately two days to compute using a parallel MATLAB implementation on a multi-core workstation.