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The well-known thin-sheet modeling has become a very useful interpretation tool in electromagnetic (EM) methods. The thin-sheet model approximates fairly well 3-D heterogeneities having a limited vertical dimension. This type of approximation leads to amenable computation of EM response of a relatively complex conductivity distribution. This paper describes the integration of thin-sheet forward modeling into an inversion method based on a stochastic Monte Carlo Markov Chain (MCMC) algorithm. Effective exploration of the model space is performed using a biased sampler capable to avoid entrapment to local minima frequently encountered in a such highly non- linear problem. Results from inversion of synthetic EM data show that the algorithm can reasonably resolve the true structure. Effectiveness and limitations of the proposed inversion method is discussed with reference to the synthetic data inversions.

Thin-sheet electromagnetic inversion modeling using Monte Carlo Markov Chain (MCMC) algorithm