Open AccessA Bayesian level set method for an inverse medium scattering problem in acoustics
Author(s) -
Jiangfeng Huang,
Zhong Liang Deng,
Li Xu
Publication year - 2021
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.755
H-Index - 40
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2021029
Subject(s) - posterior probability , markov chain monte carlo , piecewise , inverse problem , bayesian probability , inverse , inverse scattering problem , prior probability , computer science , mathematics , algorithm , monte carlo method , mathematical optimization , mathematical analysis , geometry , artificial intelligence , statistics
In this work, we are interested in the determination of the shape of the scatterer for the two dimensional time harmonic inverse medium scattering problems in acoustics. The scatterer is assumed to be a piecewise constant function with a known value inside inhomogeneities and its shape is represented by the level set functions for which we investigate the information using the Bayesian method. In the Bayesian framework, the solution of the geometric inverse problem is defined as a posterior probability distribution. The well-posedness of the posterior distribution is discussed and the Markov chain Monte Carlo (MCMC) method is applied to generate samples from the posterior distribution. Numerical experiments are presented to demonstrate the effectiveness of the proposed method.