Infinite-dimensional Bayesian approach for inverse scattering problems of a fractional Helmholtz equation

TL;DR

This paper develops an infinite-dimensional Bayesian framework for inverse scattering problems involving a fractional Helmholtz equation, addressing both loss- and dispersion-dominated models with theoretical well-posedness and convergence results.

Contribution

It introduces a novel Bayesian inverse approach for fractional Helmholtz equations, including models with noise dependence and model reduction analysis.

Findings

Well-posedness established for loss-dominated model

Convergence of estimated function to true function proven

Application to loss-dominated model with absorbing boundary condition

Abstract

In this paper, we focus on a new wave equation described wave propagation in the attenuation medium. In the first part of this paper, based on the time-domain space fractional wave equation, we formulate the frequency-domain equation named as fractional Helmholtz equation. According to the physical interpretations, this new model could be divided into two separate models: loss-dominated model and dispersion-dominated model. For the loss-dominated model (it is an integer- and fractional-order mixed elliptic equation), a well-posedness theory has been established and the Lipschitz continuity of the scattering field with respect to the scatterer has also been established.Because the complexity of the dispersion-dominated model (it is an integer- and fractional-order mixed elliptic system), we only provide a well-posedness result for sufficiently small wavenumber. In the second part of this paper, we generalize the Bayesian inverse theory in infinite-dimension to allow a part of the noise depends on the target function (the function needs to be estimated). Then, we prove that the estimated function tends to be the true function if both the model reduction error and the white noise vanish. At last, our theory has been applied to the loss-dominated model with absorbing boundary condition.