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A Bayesian Approach to Geophysical Inverse Problems  

Oh Seokhoon (Marine Meteorology & Earthquake Res. Lab/METRI)
Chung Seung-Hwan (Geophysical Exploration and Mining Division, Korea Institute of Geoscience and Mineral Resources)
Kwon Byung-Doo (Dept. of Earth Sciences Education, Seoul National University)
Lee Heuisoon (Dept. Science Education, Inchon Nat'l Univ. of Education)
Jung Ho Jun (Heesong Geotek, Co. Ltd.)
Lee Duk Kee (Marine Meteorology & Earthquake Res. Lab/METRI)
Publication Information
Geophysics and Geophysical Exploration / v.5, no.4, 2002 , pp. 262-271 More about this Journal
Abstract
This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.
Keywords
Bayesian inversion; MCMC; Gibbs sampling; posterior PDF;
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