%0 Journal Article
%T Thin-Sheet Inversion Modeling of Geomagnetic Deep Sounding Data Using MCMC Algorithm
%A Hendra Grandis
%A Michel Menvielle
%A Michel Roussignol
%J International Journal of Geophysics
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/531473
%X 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. 1. Introduction In geomagnetic deep sounding (GDS), we measure the natural Earth¡¯s magnetic transient variations to infer large-scale subsurface conductivity distribution. Recent advances in magnetotelluric (MT) technique tend to put the attention to move towards local scale investigation of conductivity anomalies with more economic interests as in exploration for mineral, geothermal, or hydrocarbon. However, GDS is still considered as the most appropriate natural source electromagnetic (EM) method capable of imaging the Earth¡¯s interior especially for tectonic study at the regional and continental scales (e.g., [1, 2]). 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 MCMC inversion algorithm has been applied for 1D modeling of MT [3, 4], vertical electrical sounding (VES) [5] and also controlled-source audio-frequency MT (CSAMT) [6] with satisfactory results. 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]. We will first briefly review the concept and the formulation of the thin-sheet modeling and then describe the MCMC inversion algorithm. The result of inversion of real GDS data from Bohemian Massif-West Carpathians and also its interpretation will be discussed. 2. Thin-Sheet Electromagnetic Modeling In electromagnetic (EM) geophysics, the thin-sheet modeling refers to an approximation of 3D conductivity variation by a thin layer with variable conductance, that is, integrated conductivity over the thickness of the thin layer.
%U http://www.hindawi.com/journals/ijge/2013/531473/