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Abstract
An approach to the inversion of electromagnetic data in the three-dimensional media based on the Bayesian statistics is discussed. The information available is incorporated in the inversion procedure via the probability density function (PDF), specified for the prior conductivities palette in the region of search, while the parameters to be found are the posterior conductivity values. A stochastic algorithm named a Gibbs sampler is used to estimate the posterior PDF. The model of this process is a Markov chain, the transition law of which converges to the marginal PDF of the parameters. Effects of the quantity and quality of the input data as well as of the prior information on the results of inversion are estimated. The applicability of the Bayesian inversion of audiomagnetotelluric data to mapping of salinity zones of groundwater reservoirs is examined.
Published Version
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