TY - JOUR
T1 - Efficient Bayesian experimental design for contaminant source identification
AU - Zhang, Jiangjiang
AU - Zeng, Lingzao
AU - Chen, Cheng
AU - Chen, Dingjiang
AU - Wu, Laosheng
N1 - Publisher Copyright:
© 2015. American Geophysical Union. All Rights Reserved.
PY - 2015/1
Y1 - 2015/1
N2 - In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.
AB - In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.
KW - Bayesian experimental design
KW - MCMC
KW - adaptive sparse grid
KW - contaminant transport
KW - source identification
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U2 - 10.1002/2014WR015740
DO - 10.1002/2014WR015740
M3 - Article
AN - SCOPUS:85027948635
SN - 0043-1397
VL - 51
SP - 576
EP - 598
JO - Water Resources Research
JF - Water Resources Research
IS - 1
ER -