An extended sampling-ensemble Kalman filter approach for partial data inverse elastic problems

TL;DR

This paper introduces a two-step method combining extended sampling and ensemble Kalman filter techniques to reconstruct elastic obstacles from partial scattering data, improving accuracy in inverse elastic problems.

Contribution

It presents a novel integrated approach that first locates and then shapes elastic obstacles using partial data, enhancing reconstruction performance.

Findings

Effective obstacle localization with extended sampling.

Accurate shape reconstruction via ensemble Kalman filter.

Method demonstrates robustness with partial data.

Abstract

Inverse problems are more challenging when only partial data are available in general. In this paper, we propose a two-step approach combining the extended sampling method and the ensemble Kalman filter to reconstruct an elastic rigid obstacle using partial data. In the first step, the approximate location of the unknown obstacle is obtained by the extended sampling method. In the second step, the ensemble Kalman filter is employed to reconstruct the shape. The location obtained in the first step guides the construction of the initial particles of the ensemble Kalman filter, which is critical to the performance of the second step. Both steps are based on the same physical model and use the same scattering data. Numerical examples are shown to illustrate the effectiveness of the proposed method.