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

Hendra Grandis | Zendy; Michel Menvielle | Zendy; Michel Roussignol | Zendy

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

Author(s) -

Hendra Grandis,

Michel Menvielle,

Michel Roussignol

Publication year - 2013

Publication title -

international journal of geophysics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.253

H-Index - 19

eISSN - 1687-8868

pISSN - 1687-885X

DOI - 10.1155/2013/531473

Subject(s) - markov chain monte carlo , geology , inversion (geology) , depth sounding , algorithm , earth's magnetic field , vertical electrical sounding , geophysics , tectonics , monte carlo method , seismology , aquifer , computer science , magnetic field , geotechnical engineering , statistics , mathematics , groundwater , oceanography , physics , quantum mechanics

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

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