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Read Full Article (file size: 450982 bytes) Cited by
WATER RESOURCES RESEARCH,
VOL. 41,
W12423,
doi:10.1029/2004WR003764,
2005
An efficient two-stage Markov chain Monte Carlo method for dynamic data integration
Y. Efendiev
Department of Mathematics and Institute for Scientific Computation, Texas A&M University, College Station, Texas, USA
A. Datta-Gupta
Department of Petroleum Engineering, Texas A&M University, College Station, Texas, USA
V. Ginting
Department of Mathematics and Institute for Scientific Computation, Texas A&M University, College Station, Texas, USA
X. Ma
Department of Petroleum Engineering, Texas A&M University, College Station, Texas, USA
B. Mallick
Department of Statistics, Texas A&M University, College Station, Texas, USA
Abstract
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.
Keywords: MCMC;
upscaling;
acceptance rate.
Index Terms: 1873 Hydrology: Uncertainty assessment (3275); 3275 Mathematical Geophysics: Uncertainty quantification (1873); 3260 Mathematical Geophysics: Inverse theory.
Read Full Article (file size: 450982 bytes) Cited by
Citation: 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.
Copyright 2005 by the American Geophysical Union.
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