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

abstract

• In this paper, we use a twostage Markov chain Monte Carlo (MCMC) method for subsurface characterization that employs coarsescale models. The purpose of the proposed method is to increase the acceptance rate of MCMC by using inexpensive coarsescale runs based on singlephase 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.

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

• 104

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