Regular Article

An efficient two-stage Markov chain Monte Carlo method for dynamic data integration

  1. Y. Efendiev1,
  2. A. Datta-Gupta2,
  3. V. Ginting1,
  4. X. Ma2,
  5. B. Mallick3

Article first published online: 20 DEC 2005

DOI: 10.1029/2004WR003764

Issue

Water Resources Research

Water Resources Research

Additional Information

How to Cite

Efendiev, Y., A. Datta-Gupta, V. Ginting, X. Ma, and B. Mallick (2005), An efficient two-stage Markov chain Monte Carlo method for dynamic data integration, Water Resour. Res., 41, W12423, doi:10.1029/2004WR003764.

Author Information

  1. 1

    Department of Mathematics and Institute for Scientific Computation, Texas A&M University, College Station, Texas, USA

  2. 2

    Department of Petroleum Engineering, Texas A&M University, College Station, Texas, USA

  3. 3

    Department of Statistics, Texas A&M University, College Station, Texas, USA

Publication History

  1. Issue published online: 20 DEC 2005
  2. Article first published online: 20 DEC 2005
  3. Manuscript Accepted: 7 SEP 2005
  4. Manuscript Revised: 10 JUN 2005
  5. Manuscript Received: 26 OCT 2004

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Keywords:

  • MCMC;
  • upscaling;
  • acceptance rate

[1] In this paper, we use a two-stage Markov chain Monte Carlo (MCMC) method for subsurface characterization that employs coarse-scale models. The purpose of the proposed method is to increase the acceptance rate of MCMC by using inexpensive coarse-scale runs based on single-phase upscaling. Numerical results demonstrate that our approach leads to a severalfold increase in the acceptance rate and provides a practical approach to uncertainty quantification during subsurface characterization.

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