Thin-Sheet Inversion Modeling of Geomagnetic Deep Sounding Data Using MCMC Algorithm

摘要

The geomagnetic deep sounding (GDS) method is one of electromagnetic (EM) methods in geophysics that allows the estimation of the subsurface electrical conductivity distribution.This paper presents the inversion modeling of GDS data employing Markov ChainMonte Carlo (MCMC) algorithm to evaluate themarginal posterior probability of the model parameters.We used thin-sheet model to represent quasi-3D conductivity variations in the heterogeneous subsurface.The algorithmwas applied to invert field GDS data from the zone covering an area that spans from eastern margin of the Bohemian Massif to the West Carpathians in Europe. Conductivity anomalies obtained from this study confirm the well-known large-scale tectonic setting of the area.