Geostatistical approach to bayesian inversion of geophysical data: Markov chain Monte Carlo method

Details Geostatistical approach to bayesian inversion of geophysical data: Markov chain Monte Carlo method Geostatistical approach to bayesian inversion of geophysical data: Markov chain Monte Carlo method

Aug 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001ep%26s...53..777o&link_type=abstract

Earth, Planets and Space, Volume 53, p. 777-791.

Mathematics

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1

Scientific paper

This paper presents a practical and objective procedure for a Bayesian inversion of geophysical data. We have applied geostatistical techniques such as kriging and simulation algorithms to acquire a prior model information. Then the Markov chain Monte Carlo (MCMC) method is adopted to infer the characteristics of the marginal distributions of model parameters. Geostatistics which is based upon a variogram model provides a means to analyze and interpret the spatially distributed data. For Bayesian inversion of dipole-dipole resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger and well logging data for obtaining a prior information by cokriging and simulations from covariogram models. Indicator approaches make it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the Markov chain Monte Carlo approach, based on Gibbs sampling, to examine the characteristics of a posterior probability density function and marginal distributions of each parameter. The MCMC technique provides a robust result from which information given by the indicator method, that is fundamentally non-parametric, is fully extracted. We have used the a prior information proposed by the geostatistical method as the full conditional distribution for Gibbs sampling. And to implement Gibbs sampler, we have applied the modified Simulated Annealing (SA) algorithm which effectively searched for global model space. This scheme provides a more effective and robust global sampling algorithm as compared to the previous study.

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