Accelerated Markov Chain Monte Carlo Sampling in Electrical Capacitance Tomography
نویسندگان
چکیده
Electrical Capacitance Tomography is an ill-posed inverse problem that aims at recovering the spatial permittivity distribution of an inhomogeneous medium from capacitance measurements at the boundary. We consider the problem of fast robust estimation of inclusion shape and position in binary mixtures. The boundary of the inclusion is represented implicitly using a radial basis function representation. The inverse problem is formulated as Bayesian inference, with Markov chain Monte Carlo sampling used to explore the posterior distribution. An affine approximation to the forward map built over the state space significantly reduces reconstruction time, while introducing minimal extra error. Numerical examples are presented for synthetic data sets, avoiding all inverse crimes.
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