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An inverse random source problem for the Helmholtz equation. (English) Zbl 1278.65005

Summary: This paper is concerned with an inverse random source problem for the one-dimensional stochastic Helmholtz equation, which is to reconstruct the statistical properties of the random source function from boundary measurements of the radiating random electric field. Although the emphasis of the paper is on the inverse problem, we adapt a computationally more efficient approach to study the solution of the direct problem in the context of the scattering model. Specifically, the direct model problem is equivalently formulated into a two-point spatially stochastic boundary value problem, for which the existence and uniqueness of the pathwise solution is proved. In particular, an explicit formula is deduced for the solution from an integral representation by solving the two-point boundary value problem. Based on this formula, a novel and efficient strategy, which is entirely done by using the fast Fourier transform, is proposed to reconstruct the mean and the variance of the random source function from measurements at one boundary point, where the measurements are assumed to be available for many realizations of the source term. Numerical examples are presented to demonstrate the validity and effectiveness of the proposed method.

MSC:

65C30 Numerical solutions to stochastic differential and integral equations
65L09 Numerical solution of inverse problems involving ordinary differential equations
65L10 Numerical solution of boundary value problems involving ordinary differential equations
34A55 Inverse problems involving ordinary differential equations
34B05 Linear boundary value problems for ordinary differential equations
78A46 Inverse problems (including inverse scattering) in optics and electromagnetic theory
60H10 Stochastic ordinary differential equations (aspects of stochastic analysis)
34F05 Ordinary differential equations and systems with randomness
60H35 Computational methods for stochastic equations (aspects of stochastic analysis)
Full Text: DOI

References:

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