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An efficient two-stage Markov chain Monte Carlo method for dynamic data integration
Department of Mathematics and Institute for Scientific Computation, Texas A&M University, College Station, Texas, USA
Department of Petroleum Engineering, Texas A&M University, College Station, Texas, USA
Department of Mathematics and Institute for Scientific Computation, Texas A&M University, College Station, Texas, USA
Department of Petroleum Engineering, Texas A&M University, College Station, Texas, USA
Department of Statistics, Texas A&M University, College Station, Texas, USA
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.
Received 26 October 2004; accepted 7 September 2005; published 20 December 2005.
Citation: (2005), An efficient two-stage Markov chain Monte Carlo method for dynamic data integration, Water Resour. Res., 41, W12423, doi:10.1029/2004WR003764.
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