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Volume 9 Issue 1
Mar.  2021
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Article Contents
Jin-bing LIU, Si-min JIANG, Nian-qing ZHOU, et al. 2021: Groundwater contaminant source identification based on QS-ILUES. Journal of Groundwater Science and Engineering, 9(1): 73-82. doi: 10.19637/j.cnki.2305-7068.2021.01.007
Citation: Jin-bing LIU, Si-min JIANG, Nian-qing ZHOU, et al. 2021: Groundwater contaminant source identification based on QS-ILUES. Journal of Groundwater Science and Engineering, 9(1): 73-82. doi: 10.19637/j.cnki.2305-7068.2021.01.007

Groundwater contaminant source identification based on QS-ILUES

doi: 10.19637/j.cnki.2305-7068.2021.01.007
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  • Corresponding author: JIANG Si-min, E-mail: jiangsimin@tongji.edu.cn
  • Received Date: 2020-09-27
  • Accepted Date: 2020-11-23
  • Publish Date: 2021-03-28
  • When groundwater pollution occurs, to come up with an efficient remediation plan, it is particularly important to collect information of contaminant source (location and source strength) and hydraulic conductivity field of the site accurately and quickly. However, the information can not be obtained by direct observation, and can only be derived from limited measurement data. Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source. As for hydraulic conductivity field, especially for complex non-Gaussian field, it can be directly estimated by geostatistics method based on limited hard data, while the accuracy is often not high. Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem. Therefore, in this study, the multi-point geostatistics method Quick Sampling (QS) is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother (ILUES) to develop a new data assimilation framework QS-ILUES. It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data. While the pilot points are utilized to reduce the dimension of hydraulic conductivity field, the influence of pilot points' layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored.
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