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

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. Copyright 2005 by the American Geophysical Union.

authors

• Dattagupta, Akhil
• Efendiev, Yalchin
• Mallick, Bani

published proceedings

• WATER RESOURCES RESEARCH

author list (cited authors)

• Efendiev, Y., Datta-Gupta, A., Ginting, V., Ma, X., & Mallick, B.

citation count

• 97

complete list of authors

• Efendiev, Y||Datta-Gupta, A||Ginting, V||Ma, X||Mallick, B

publication date

• December 2005

publisher

• American Geophysical Union (AGU)  Publisher

published in

• Water Resources Research  Journal

keywords

• 37 Earth Sciences
• 3707 Hydrology

Digital Object Identifier (DOI)

• 10.1029/2004WR003764

start page

• 1

end page

• 6

volume

• 41

issue

• 12

URL

• http://dx.doi.org/10.1029/2004wr003764