Direct sampling methods for inverse elastic scattering problems

TL;DR

This paper introduces simple, fast, and stable direct sampling methods for inverse elastic scattering problems, capable of reconstructing the shape and location of scatterers using far field data, even with limited measurements and noisy data.

Contribution

The paper proposes novel direct sampling methods based on far field patterns, with theoretical stability and effectiveness demonstrated through numerical simulations.

Findings

Methods are simple and involve only inner products.

Indicator functionals decay like Bessel functions outside scatterers.

Methods are stable under data errors and effective with limited-aperture data.

Abstract

We consider the inverse elastic scattering of incident plane compressional and shear waves from the knowledge of the far field patterns. Specifically, three direct sampling methods for location and shape reconstruction are proposed using the different component of the far field patterns. Only inner products are involved in the computation, thus the novel sampling methods are very simple and fast to be implemented. With the help of the factorization of the far field operator, we give a lower bound of the proposed indicator functionals for sampling points inside the scatterers. While for the sampling points outside the scatterers, we show that the indicator functionals decay like the Bessel functions as the sampling point goes away from the boundary of the scatterers. We also show that the proposed indicator functionals continuously dependent on the far field patterns, which further implies that the novel sampling methods are extremely stable with respect to data error. For the case when the observation directions are restricted into the limited aperture, we firstly introduce some data retrieval techniques to obtain those data that can not be measured directly and then use the proposed direct sampling methods for location and shape reconstructions. Finally, some numerical simulations in two dimensions are conducted with noisy data, and the results further verify the effectiveness and robustness of the proposed sampling methods, even for multiple multiscale cases and limited-aperture problems.