Efficient Bayesian experimental design for contaminant source identification
Efficient Bayesian experimental design for contaminant source identification
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用于污染物源识别的高效贝叶斯实验设计
DOI：
10.1002/2014wr015740
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发表时间：
2015
影响因子：
5.4
通讯作者：
Laosheng Wu
中科院分区：
文献类型：
--
作者：
Jiangjiang Zhang;Lingzao Zeng;Cheng Chen;Dingjiang Chen;Laosheng Wu
本文提出一种有效的全贝叶斯方法，用于地下水污染物的最优采样井位置设计和源参数识别。一种信息度量，即，在识别未知参数时，采用相对熵来量化来自浓度测量的信息增益。在这种方法中，给出最大期望相对熵的采样位置被选择为最优设计。在确定采样位置后，使用基于马尔可夫链蒙特卡罗（MCMC）的贝叶斯方法来估计未知参数。在设计和估算中，污染物输运方程需要多次求解以评估可能性。为了减少计算量，采用基于自适应稀疏网格的插值方法来构造污染物输运方程的替代体。近似的似然可以直接从代理评估，这大大加快了设计和估计过程。我们的方法的准确性和效率证明通过数值案例研究。结果表明，该方法可用于地下水污染源识别的单点定位和监测网设计。
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.