Efficient Bayesian experimental design for contaminant source identification

Jiangjiang Zhang, Lingzao Zeng, Cheng Chen, Dingjiang Chen, Laosheng Wu

Research output: Contribution to journalArticlepeer-review

111 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)576-598
Number of pages23
JournalWater Resources Research
Volume51
Issue number1
DOIs
StatePublished - Jan 2015

Keywords

  • Bayesian experimental design
  • MCMC
  • adaptive sparse grid
  • contaminant transport
  • source identification

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