Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion

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Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion. / Laloy, Eric; Rogiers, Bart; Vrugt, Jasper A.; Mallants, Dirk; Jacques, Diederik.

In: Water Resources Research, Vol. 49, No. 5, 05.2013, p. 2664-2682.

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@article{08b8c4b3640d4f97b9974e5755785368,
title = "Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion",
abstract = "This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.",
keywords = "stochastic inversion, high parameter dimensionality, groundwater modeling, two-stage Markov chain Monte Carlo, polynomial chaos expansion, dimensionality reduction",
author = "Eric Laloy and Bart Rogiers and Vrugt, {Jasper A.} and Dirk Mallants and Diederik Jacques",
note = "Score = 10",
year = "2013",
month = may,
doi = "10.1002/wrcr.20226",
language = "English",
volume = "49",
pages = "2664--2682",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "AGU - American Geophysical Union",
number = "5",

}

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TY - JOUR

T1 - Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion

AU - Laloy, Eric

AU - Rogiers, Bart

AU - Vrugt, Jasper A.

AU - Mallants, Dirk

AU - Jacques, Diederik

N1 - Score = 10

PY - 2013/5

Y1 - 2013/5

N2 - This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.

AB - This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.

KW - stochastic inversion

KW - high parameter dimensionality

KW - groundwater modeling

KW - two-stage Markov chain Monte Carlo

KW - polynomial chaos expansion

KW - dimensionality reduction

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_130512

UR - http://knowledgecentre.sckcen.be/so2/bibref/10467

U2 - 10.1002/wrcr.20226

DO - 10.1002/wrcr.20226

M3 - Article

VL - 49

SP - 2664

EP - 2682

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 5

ER -

ID: 305249