Abstract
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 Chain Monte Carlo (MCMC) algorithm to evaluate the marginal posterior probability of the model parameters. We used thin-sheet model to represent quasi-3D conductivity variations in the heterogeneous subsurface. The algorithm was 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.
Highlights
In geomagnetic deep sounding (GDS), we measure the natural Earth’s magnetic transient variations to infer largescale subsurface conductivity distribution
This paper describes the inversion modeling technique for GDS data in terms of conductivity distribution by using the Markov Chain Monte Carlo (MCMC) algorithm
The thin-sheet modeling employing MCMC inversion algorithm has been applied to GDS data from the area covering the Bohemian Massif and the West Carpathians with satisfactory result
Summary
In geomagnetic deep sounding (GDS), we measure the natural Earth’s magnetic transient variations to infer largescale subsurface conductivity distribution. This paper describes the inversion modeling technique for GDS data in terms of conductivity distribution by using the Markov Chain Monte Carlo (MCMC) algorithm. In the so-called thin-sheet modeling, the conductivity variations are assumed to be confined in a thin layer such that the model parameters are integrated conductivity over the thickness of the thin layer. This simplification suits well for modeling GDS data that are mostly sensitive to lateral variation of conductivity [7, 8]. The result of inversion of real GDS data from Bohemian Massif-West Carpathians and its interpretation will be discussed
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