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AGU: Journal of Geophysical Research, Solid Earth

 

Keywords

  • electrical resistivity
  • stochastic inversion

Index Terms

  • Exploration Geophysics: Data processing
  • Exploration Geophysics: Magnetic and electrical methods
  • Exploration Geophysics: Downhole methods
  • Mathematical Geophysics: Stochastic processes
Abstract
Cited By (6)
 

Abstract

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

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.

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.

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