Stochastic inversion of electrical resistivity changes using a Markov Chain Monte Carlo approach

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Read Full Article (file size: 1152533 bytes) Cited by JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, B02101, doi:10.1029/2004JB003449, 2005 Stochastic inversion of electrical resistivity changes using a Markov Chain Monte Carlo approach A. L. Ramirez Lawrence Livermore National Laboratory, Livermore, California, USA J. J. Nitao Lawrence Livermore National Laboratory, Livermore, California, USA W. G. Hanley Lawrence Livermore National Laboratory, Livermore, California, USA R. Aines Lawrence Livermore National Laboratory, Livermore, California, USA R. E. Glaser Lawrence Livermore National Laboratory, Livermore, California, USA S. K. Sengupta Lawrence Livermore National Laboratory, Livermore, California, USA K. M. Dyer Lawrence Livermore National Laboratory, Livermore, California, USA T. L. Hickling Lawrence Livermore National Laboratory, Livermore, California, USA W. D. Daily Lawrence Livermore National Laboratory, Livermore, California, USA Abstract We describe a stochastic inversion method for mapping subsurface regions where the electrical resistivity is changing. The technique combines prior information, electrical resistance data, and forward models to produce subsurface resistivity models that are most consistent with all available data. Bayesian inference and a Metropolis simulation algorithm form the basis for this approach. Attractive features include its ability (1) to provide quantitative measures of the uncertainty of a generated estimate and (2) to allow alternative model estimates to be identified, compared, and ranked. Methods that monitor convergence and summarize important trends of the posterior distribution are introduced. Results from a physical model test and a field experiment were used to assess performance. The presented stochastic inversions provide useful estimates of the most probable location, shape, and volume of the changing region and the most likely resistivity change. The proposed method is computationally expensive, requiring the use of extensive computational resources to make its application practical. Received 23 September 2004; accepted 20 December 2004; published 23 February 2005. Keywords: electrical resistivity; stochastic inversion. Index Terms: 0910 Exploration Geophysics: Data processing; 0925 Exploration Geophysics: Magnetic and electrical methods (5109); 0915 Exploration Geophysics: Downhole methods; 3265 Mathematical Geophysics: Stochastic processes (3235, 4468, 4475, 7857). Read Full Article (file size: 1152533 bytes) Cited by Citation: Ramirez, A. L., J. J. Nitao, W. G. Hanley, R. Aines, R. E. Glaser, S. K. Sengupta, K. M. Dyer, T. L. Hickling, and W. D. Daily (2005), Stochastic inversion of electrical resistivity changes using a Markov Chain Monte Carlo approach, J. Geophys. Res., 110, B02101, doi:10.1029/2004JB003449. This paper is not subject to U.S. copyright. Published in 2005 by the American Geophysical Union.

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