Abstract
Original language | English |
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Pages (from-to) | 8383-8399 |
Number of pages | 17 |
Journal | Water Resources Research |
Volume | 49 |
Issue number | 12 |
Early online date | 7 Nov 2013 |
DOIs | |
Publication status | Published - Dec 2013 |
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Nested sampling algorithm for subsurface flow model selection, uncertainty quantification and nonlinear calibration. / ELsheikh, Ahmed H. ; Wheeler, Mary F; Hoteit, Ibrahim.
In: Water Resources Research, Vol. 49, No. 12, 12.2013, p. 8383-8399.Research output: Contribution to journal › Article
TY - JOUR
T1 - Nested sampling algorithm for subsurface flow model selection, uncertainty quantification and nonlinear calibration
AU - ELsheikh, Ahmed H.
AU - Wheeler, Mary F
AU - Hoteit, Ibrahim
PY - 2013/12
Y1 - 2013/12
N2 - Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface ow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data-mismatch of the calibrated model.
AB - Calibration of subsurface flow models is an essential step for managing ground water aquifers, designing of contaminant remediation plans and maximizing recovery from hydrocarbon reservoirs. We investigate an efficient sampling algorithm known as nested sampling (NS), which can simultaneously sample the posterior distribution for uncertainty quantification, and estimate the Bayesian evidence for model selection. Model selection statistics, such as the Bayesian evidence, are needed to choose or assign different weights to different models of different levels of complexities. In this work, we report the first successful application of nested sampling for calibration of several nonlinear subsurface flow problems. The estimated Bayesian evidence by the NS algorithm is used to weight different parameterizations of the subsurface ow models (prior model selection). The results of the numerical evaluation implicitly enforced Occam's razor where simpler models with fewer number of parameters are favored over complex models. The proper level of model complexity was automatically determined based on the information content of the calibration data and the data-mismatch of the calibrated model.
U2 - 10.1002/2012WR013406
DO - 10.1002/2012WR013406
M3 - Article
VL - 49
SP - 8383
EP - 8399
JO - Water Resources Research
JF - Water Resources Research
SN - 0043-1397
IS - 12
ER -