Application of the hybrid stochastic-deterministic minimization method to a surface data inverse scattering problem

TL;DR

This paper introduces a hybrid stochastic-deterministic minimization technique for identifying small inhomogeneities in surface data within an inverse scattering framework, especially effective when traditional approximations like Born fail.

Contribution

The paper presents a novel hybrid minimization method combining stochastic and deterministic approaches for inverse scattering problems involving small scatterers.

Findings

Method successfully identifies scatterer positions.

Effective when Born approximation is invalid.

Includes an algorithm to estimate the number of scatterers.

Abstract

A method for the identification of small inhomogeneities from a surface data is presented in the framework of an inverse scattering problem for the Helmholtz equation. Using the assumptions of smallness of the scatterers one reduces this inverse problem to an identification of the positions of the small scatterers. These positions are found by a global minimization search. Such a search is implemented by a novel Hybrid Stochastic-Deterministic Minimization method. The method combines random tries and a deterministic minimization. The effectiveness of this approach is illustrated by numerical experiments. In the modeling part our method is valid when the Born approximation fails. In the numerical part, an algorithm for the estimate of the number of the small scatterers is proposed.