Bayesian Approach to Inverse Time-harmonic Acoustic Scattering with Phaseless Far-field Data

Abstract

This paper is concerned with inverse acoustic scattering problem of inferring the position and shape of a sound-soft obstacle from phaseless far-field data. We propose the Bayesian approach to recover sound-soft disks, line cracks and kite-shaped obstacles through properly chosen incoming waves in two dimensions. Given the Gaussian prior measure, the well-posedness of the posterior measure in the Bayesian approach is discussed. The Markov Chain Monte Carlo (MCMC) method is adopted in the numerical approximation and the preconditioned Crank-Nicolson (pCN) algorithm with random proposal variance is utilized to improve the convergence rate. Numerical examples are provided to illustrate effectiveness of the proposed method.

References in corpus (2)

• MCMC using Hamiltonian dynamics
• Well-Posed Bayesian Geometric Inverse Problems Arising in Subsurface Flow

Cited by in corpus (3)

• A deterministic-statistical approach to reconstruct moving sources using sparse partial data
• Bayesian Approach to Inverse Time-harmonic Acoustic Scattering from Sound-soft Obstacles with Phaseless Data
• A direct imaging method for inverse scattering problem of biharmonic wave with phased and phaseless data