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Inverse Problems and Imaging (IPI)
 

The Bayesian formulation of EIT: Analysis and algorithms

Pages: 1007 - 1036, Volume 10, Issue 4, November 2016      doi:10.3934/ipi.2016030

 
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Matthew M. Dunlop - Computing & Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, United States (email)
Andrew M. Stuart - Computing & Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, United States (email)

Abstract: We provide a rigorous Bayesian formulation of the EIT problem in an infinite dimensional setting, leading to well-posedness in the Hellinger metric with respect to the data. We focus particularly on the reconstruction of binary fields where the interface between different media is the primary unknown. We consider three different prior models -- log-Gaussian, star-shaped and level set. Numerical simulations based on the implementation of MCMC are performed, illustrating the advantages and disadvantages of each type of prior in the reconstruction, in the case where the true conductivity is a binary field, and exhibiting the properties of the resulting posterior distribution.

Keywords:  Inverse problems, ill-posed problems, electrical impedance tomography, Bayesian regularisation, geometric priors, level set methodology.
Mathematics Subject Classification:  Primary: 62G05, 65N21; Secondary: 92C55.

Received: August 2015;      Revised: July 2016;      Available Online: October 2016.

 References