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

Y. Efendiev; A. Datta-Gupta; V. Ginting; X. Ma; B. Mallick

doi:10.1029/2004WR003764

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

Y. Efendiev^*, A. Datta-Gupta, V. Ginting, X. Ma, B. Mallick

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

101 Scopus citations

Abstract

[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.

Original language	English (US)
Article number	W12423
Pages (from-to)	1-6
Number of pages	6
Journal	Water Resources Research
Volume	41
Issue number	12
DOIs	https://doi.org/10.1029/2004WR003764
State	Published - Dec 2005
Externally published	Yes

ASJC Scopus subject areas

Water Science and Technology

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10.1029/2004WR003764

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title = "An efficient two-stage Markov chain Monte Carlo method for dynamic data integration",

abstract = "[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.",

author = "Y. Efendiev and A. Datta-Gupta and V. Ginting and X. Ma and B. Mallick",

year = "2005",

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language = "English (US)",

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AU - Datta-Gupta, A.

AU - Ginting, V.

AU - Ma, X.

AU - Mallick, B.

PY - 2005/12

Y1 - 2005/12

N2 - [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.

AB - [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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DO - 10.1029/2004WR003764

M3 - Article

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

Abstract

ASJC Scopus subject areas

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