Groundwater contaminant source identification based on iterative local update ensemble smoother

YANG Ai-lin; JIANG Si-min; LIU Jin-bing; JIANG Qian-yun; ZHOU Ting; ZHANG Wen

doi:10.19637/j.cnki.2305-7068.2020.01.001

Volume 8 Issue 1

Mar. 2020

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YANG Ai-lin, JIANG Si-min, LIU Jin-bing, et al. 2020: Groundwater contaminant source identification based on iterative local update ensemble smoother. Journal of Groundwater Science and Engineering, 8(1): 1-9. doi: 10.19637/j.cnki.2305-7068.2020.01.001

Citation:

YANG Ai-lin, JIANG Si-min, LIU Jin-bing, et al. 2020: Groundwater contaminant source identification based on iterative local update ensemble smoother. Journal of Groundwater Science and Engineering, 8(1): 1-9. doi: 10.19637/j.cnki.2305-7068.2020.01.001

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Groundwater contaminant source identification based on iterative local update ensemble smoother

doi: 10.19637/j.cnki.2305-7068.2020.01.001

1Department of Hydraulic Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China.2Beijing Haidian

Funds:

JIANG Si-min

Publish Date: 2020-03-28

Abstract

Abstract

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation, and is called groundwater contaminant source identifcation. This is a kind of typical groundwater inverse problem, and the solution is usually ill-posed. Especially considering the spatial variability of hydraulic conductivity field, the identification process is more challenging. In this paper, the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model (MT3DMS) and a data assimilation method (Iterative local update ensemble smoother, ILUES). In addition, Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction. In practical problems, the geostatistical method is usually used to characterize the hydraulic conductivity feld, and only the contaminant source information is inversely calculated in the identifcation process. In this study, the identification of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field. The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site, and ILUES has good performance in solving high-dimensional parameter inversion problems.
- Groundwater contamination,
- Groundwater inverse problem,
- Source identification,
- Ensemble smoother,
- Data assimilation

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References(21)

References

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