Recovering the sources in the stochastic wave equations from multi-frequency far-field patterns

TL;DR

This paper introduces a non-iterative, Fourier-based method for recovering the statistical properties of random sources in acoustic and elastic wave equations from multi-frequency far-field data, demonstrating efficiency and robustness.

Contribution

It proposes a novel Fourier series approach to directly reconstruct the mean and variance of stochastic sources from sparse far-field measurements, avoiding iterative procedures.

Findings

Method accurately recovers source statistics from limited data.

Numerical results confirm robustness against noise.

Approach is computationally efficient and easy to implement.

Abstract

This paper concerns the inverse source scattering problems of recovering random sources for acoustic and elastic waves. The underlying sources are assumed to be random functions driven by an additive white noise. The inversion process aims to find the essential statistical characteristics of the mean and variance from the radiated random wave field at multiple frequencies. To this end, we propose a non-iterative algorithm by approximating the mean and variance via the truncated Fourier series. Then, the Fourier coefficients can be explicitly evaluated by sparse far-field measurements, resulting in an easy-to-implement and efficient approach for the reconstruction. Demonstrations with extensive numerical results are presented to corroborate the feasibility and robustness of the proposed method.