• ISSN 2305-7068
  • Indexed by ESCI CABI CAS
  • DOAJ EBSCO Scopus GeoRef AJ CNKI
Advanced Search
Volume 9 Issue 1
Mar.  2021
Turn off MathJax
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
More Information
  • 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.
  • 加载中
  • CAO Zhen-dan, LI Liang-ping, CHEN Kang. 2018. Bridging iterative ensemble smoother and multiple-point geostatistics for better flow and transport modeling. Journal of Hydrology, 565: 411-421. doi:  10.1016/j.jhydrol.2018.08.023
    Chen Y, Oliver DS. 2012. Ensemble randomized maximum likelihood method as an itera-tive ensemble smoother. Mathematical Geo-sciences, 44(1): 1-26. http://d.wanfangdata.com.cn/periodical/c207e8f6aeddbce8976da66371cdd132
    Emerick AA, Reynolds AC. 2013. Ensemble smoother with multiple data assimilation. Computers & Geosciences, 55: 3-15. http://dl.acm.org/citation.cfm?id=2464411
    Evensen G, Van Leeuwen PJ. 2000. An ensemble kalman smoother for nonlinear dynamics. Monthly Weather Review, 128(6): 1852-1867. doi:  10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;2
    Gravey M, Mariethoz G. 2020. QuickSampling v1.0: A robust and simplified pixel-based multiple-point simulation approach. Geosci-entific Model Development, 13(6): 2611-2630.
    Guneshwor L, Eldho TI, Vinod kumar A. 2018. Identification of groundwater contamination sources using meshfree RPCM simulation and particle swarm optimization. Water Resources Management, 32(4): 1517-1538. doi:  10.1007/s11269-017-1885-1
    Jha M, Datta B. 2013. Three-dimensional ground-water contamination source identi-fication using adaptive simulated annealing. Journal of Hydrologic Engineering, 18(3): 307-317. doi:  10.1061/(ASCE)HE.1943-5584.0000624
    JIANG Si-min, ZAHNG Ya-li, WANG Pei, et al. 2013. An almost-parameter-free harmony search algorithm for groundwater pollution source identification. Water Science and Technology, 68(11): 2359-2366. doi:  10.2166/wst.2013.499
    JU Lei, ZHANG Jiang-jiang, MENG Long, et al. 2018. An adaptive Gaussian process-based iterative ensemble smoother for data assimilation. Advances in Water Resources, 115: 125-135. doi:  10.1016/j.advwatres.2018.03.010
    LI Li, WANG Yong-gang. 2006. Review of app-lications of geostatistics. Progress in Expora-tion Geophysics, 29(3): 163-169. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-KTDQ200603003.htm
    LI Liang-ping, Stetler L, CAO Zhen-dan, et al. 2018a. An iterative normal-score ensemble smoother for dealing with non-Gaussianity in data assimilation. Journal of Hydrology, 567: 759-766. doi:  10.1016/j.jhydrol.2018.01.038
    LI Liang-ping, Puzel R, Davis A, et al. 2018b. Data assimilation in groundwater modelling: Ensemble Kalman filter versus ensemble smoothers. Hydrological Processes, 32(13): 2020-2029. doi:  10.1002/hyp.13127
    LIU Ling-ling, WU Jian-feng, WU Ji-chun, et al. 2009. A comparative study of four geostatistical methods for identifying the hydraulic conductivity fields based on test data. Hydrogeology & Engineering Geology, 36(5): 66-71. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTotal-SWDG200905017.htm
    LIU Wen-ting, CHAO LUN Ba-gen, LIU Yan-wei, et al. 2010. Applying geostatistic for the study on spatial variability of coefficient of permeability. Water Conservancy Science and Technology and Economy, 16(4): 364-366. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-SLKY201004004.htm
    LUO Hong-mei, YANG Pei-jie, WANG Chang-jiang, et al. 2015. Lithofacies simulation based on multi-point geostatistics multiple data joint constraints. Oil Geophysical Pros-pecting, 50(1): 162-169. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTotal-SYDQ201501031.htm
    Mariethoz G, Renard P, Straubhaar J, et al. 2010. The Direct sampling method to perform multiple‐point geostatistical simulations. Water Resources Research, 46(11): 1-14. http://www.cabdirect.org/abstracts/20113080508.html;jsessionid=0548407FA58EB53F80C232A33FD8C6A7;jsessionid=B8DC5AD104CC11C440FBB052C79885CE
    MO Shao-xing, Zabaras N, SHI Xiao-qing, et al. 2019. Deep autoregressive neural net-works for high‐dimensional inverse pro-blems in groundwater contaminant source identification. Water Resources Research, 55(5): 3856-3881. doi:  10.1029/2018WR024638
    MO Shao-xing, Zabaras N, SHI Xiao-qing, et al. 2020. Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non-Gaussian hydraulic conductivities. Water Resources Research, 56(2): 1-24. http://arxiv.org/abs/1906.11828?context=stat.ML
    Ramarao BS, Lavenue AM, DE Marsily G, et al. 1995. Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields: 1. Theory and computational experiments. Water Resources Research, 31(3): 475-493. doi:  10.1029/94WR02258
    Rezaee H, Mariethoz G, Koneshloo M, et al. 2013. Multiple-point geostatistical simulation using the bunch-pasting direct sampling method. Computers & Geosciences, 54: 293-308. doi:  10.1016/j.cageo.2013.01.020
    Straubhaar J, Renard P, Mariethoz G, et al. 2011. An improved parallel multiple-point algorithm using a list approach. Mathematical Geosciences, 43(3): 305-328. doi:  10.1007/s11004-011-9328-7
    Strebelle S. 2002. Conditional simulation of complex geological structures using multiple-point statistics. Mathematical Geology, 34(1): 1-21. doi:  10.1023/A:1014009426274
    XIA Xue-min, JIANG Si-min, ZHOU Nian-qing, et al. 2019. Genetic algorithm hyper-parameter optimization using taguchi design for ground-water pollution source identification. Water Supply, 19(1): 137-146. doi:  10.2166/ws.2018.059
    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
    YANG Pei-jie. 2014. Geostatistics inversion- from two-point to multiple-point. Progress in Geophysics, 29(5): 2293-2300. (in Chinese) http://en.cnki.com.cn/Article_en/CJFDTOTAL-DQWJ201405045.htm
    ZHANG Jiang-jiang, LIN Guang, LI Wei-xuan, et al. 2018. An iterative local updating en-semble smoother for estimation and un-certainty assessment of hydrologic model parameters with multimodal distributions. Water Resources Research, 54(3): 1716-1733. doi:  10.1002/2017WR020906
    ZONG Cheng-yuan, KANG Xue-yuan, SHI Xiao-qing, et al. 2020. Characterization of non-Gaussian hydraulic conductivity fields using multiple-point geostatistics and ensemble smoother with multiple data assimilation method. Hydrogeology & Engineering Geology, 47(02): 1-8. (in Chinese)
  • Relative Articles

    [1] Meng-lei Ji, Shuai-chao Wei, Wei Zhang, Feng Liu, Yu-zhong Liao, Ruo-xi Yuan, Xiao-xue Yan, Long Li, 2024: Characterization of rock thermophysical properties and factors affecting thermal conductivity−A case study of Datong Basin, China, Journal of Groundwater Science and Engineering, 12, 4-15.  doi: 10.26599/JGSE.2024.9280002
    [2] Hui-Meng Su, Fa-Wang Zhang, Jing-Yu Hu, Jin-Feng Lei, Wei Zuo, Bo Yang, Yu-Hua Liu, 2024: Identified the hydrochemical and the sulfur cycle process in subsidence area of Pingyu mining area using multi-isotopes combined with hydrochemistry methods, Journal of Groundwater Science and Engineering, 12, 62-77.  doi: 10.26599/JGSE.2024.9280006
    [3] ILUNGA Nyembwe, AMADI Akobundu Nwanosike, Gilbert NDATIMANA, Nelson OKOT, Raphaël TSHIMANGA Muamba, 2024: Evaluation of aquifer hydraulic properties from resistivity and pumping test data in parts of Gwagwalada, Northcentral Nigeria, Journal of Groundwater Science and Engineering, 12, 309-320.  doi: 10.26599/JGSE.2024.9280023
    [4] Peng-yu Shi, Jian-jun Liu, Yi-jie Zong, Kai-qing Teng, Yu-ming Huang, Liang Xiao, 2023: Analytical solution for Non-Darcian effect on transient confined-unconfined flow in a confined aquifer, Journal of Groundwater Science and Engineering, 11, 365-378.  doi: 10.26599/JGSE.2023.9280029
    [5] Yi-hang Gao, Jun-hui Shen, Lin Chen, Xiao Li, Shuang Jin, Zhen Ma, Qing-hua Meng, 2023: Influence of underground space development mode on the groundwater flow field in Xiong’an new area, Journal of Groundwater Science and Engineering, 11, 68-80.  doi: 10.26599/JGSE.2023.9280007
    [6] Xiu-yan Wang, Lin Sun, Shuai-wei Wang, Ming-yu Wang, Jin-qiu Li, Wei-chao Sun, Jing-jing Wang, Xi Zhu, He Di, 2023: Development and application of multi-field coupled high-pressure triaxial apparatus for soil, Journal of Groundwater Science and Engineering, 11, 308-316.  doi: 10.26599/JGSE.2023.9280025
    [7] Yi-jie Zong, Li-hua Chen, Jian-jun Liu, Yue-hui Liu, Yong-xin Xu, Fu-wan Gan, Liang Xiao, 2022: Analytical solutions for constant-rate test in bounded confined aquifers with non-Darcian effect, Journal of Groundwater Science and Engineering, 10, 311-321.  doi: 10.19637/j.cnki.2305-7068.2022.04.001
    [8] Wondesen Fikade Niway, Dagnachew Daniel Molla, Tarun Kumar Lohani, 2022: Holistic approach of GIS based Multi-Criteria Decision Analysis (MCDA) and WetSpass models to evaluate groundwater potential in Gelana watershed of Ethiopia, Journal of Groundwater Science and Engineering, 10, 138-152.  doi: 10.19637/j.cnki.2305-7068.2022.02.004
    [9] Hong-bo HAO, Jie LV, Yan-mei CHEN, Chuan-zi WANG, Xiao-rui HUANG, 2021: Research advances in non-Darcy flow in low permeability media, Journal of Groundwater Science and Engineering, 9, 83-92.  doi: 10.19637/j.cnki.2305-7068.2021.01.008
    [10] Zhao-xian Zheng, Xiao-shun Cui, Pu-cheng Zhu, Si-jia Guo, 2021: Sensitivity assessment of strontium isotope as indicator of polluted groundwater for hydraulic fracturing flowback fluids produced in the Dameigou Shale of Qaidam Basin, Journal of Groundwater Science and Engineering, 9, 93-101.  doi: 10.19637/j.cnki.2305-7068.2021.02.001
    [11] Chun-lei GUI, Zhen-xing WANG, Rong MA, Xue-feng ZUO, 2021: Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN, Journal of Groundwater Science and Engineering, 9, 1-11.  doi: 10.19637/j.cnki.2305-7068.2021.01.001
    [12] Van Hoang Nguyen, 2021: Determination of groundwater solute transport parameters in finite element modelling using tracer injection and withdrawal testing data, Journal of Groundwater Science and Engineering, 9, 292-303.  doi: 10.19637/j.cnki.2305-7068.2021.04.003
    [13] Afraz Mehdi, Eftekhari Mobin, Akbari Mohammad, Ali Haji Elyasi, Noghani Zahra, 2021: Application assessment of GRACE and CHIRPS data in the Google Earth Engine to investigate their relation with groundwater resource changes (Northwestern region of Iran), Journal of Groundwater Science and Engineering, 9, 102-113.  doi: 10.19637/j.cnki.2305-7068.2021.02.002
    [14] YANG Ai-lin, JIANG Si-min, LIU Jin-bing, JIANG Qian-yun, ZHOU Ting, ZHANG Wen, 2020: Groundwater contaminant source identification based on iterative local update ensemble smoother, Journal of Groundwater Science and Engineering, 8, 1-9.  doi: 10.19637/j.cnki.2305-7068.2020.01.001
    [15] NAN Tian, GUO Si-jia, 2019: Influence of borehole quantity and distribution on lithology field simulation, Journal of Groundwater Science and Engineering, 7, 295-308.  doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.001
    [16] SOSI Benjamin, BARONGO Justus, GETABU Albert, MAOBE Samson, 2019: Electrical-hydraulic conductivity model for a weathered-fractured aquifer system of Olbanita, Lower Baringo Basin, Kenya Rift, Journal of Groundwater Science and Engineering, 7, 360-372.  doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.007
    [17] LI Xiao-yuan, YUE Gao-fan, SU Ran, YU Juan, 2016: Research on Pisha-sandstone’s anti-erodibility based on grey multi-level comprehensive evaluation method, Journal of Groundwater Science and Engineering, 4, 103-109.
    [18] Dana Mawlood, Jwan Mustafa, 2016: Comparison between Neuman (1975) and Jacob (1946) application for analysing pumping test data of unconfined aquifer, Journal of Groundwater Science and Engineering, 4, 165-173.
    [19] LIU Chun-lei, YANG Hui-feng, WANG Gui-ling, 2014: Back calculation of soil hydraulic parameters based on HYDRUS-1D, Journal of Groundwater Science and Engineering, 2, 46-53.
    [20] Patsakron Assiri, 2013: Artesian Flowing Wells Field of Phu Tok Aquifer, Journal of Groundwater Science and Engineering, 1, 95-98.
  • 加载中

Catalog

    Figures(4)  / Tables(5)

    Article Metrics

    Article views (2780) PDF downloads(66) Cited by()
    Proportional views
    Related

    JGSE-ScholarOne Manuscript Launched on June 1, 2024.

    Online Submission

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return