Study on detection method of landfill le achate level affected by HDPE membrane
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摘要:
渗滤液水位会影响填埋场堆体稳定并有渗漏污染风险，当渗滤液赋存于HDPE防渗膜上方，堆体和渗滤液堆积电阻率特征的极端分异特性以及边界效应等因素使得最小二乘（LS）等传统物探反演方法无法精确反演实际电阻率分布，从而无法根据电阻率差异特征定位HDPE膜上方渗滤液水位的高度。为准确刻画堆体内部特别是渗滤液-HDPE膜局部电阻率精细分布，对传统高密度电法(ERT)装置进行改进，提出了一种川形探测装置(C-ERT)，并采用BP神经网络的电阻率反演模型算法。通过COMSOL理论模型和江西某生活垃圾填埋场采集的现场数据对该方法进行验证，并与LS法比较。结果表明：基于川形装置的BP神经网络能有效识别出HDPE膜上方的渗滤液区域，识别准确率约为83.2%，而LS法并不能识别出渗滤液区域。
Abstract:The water level of leachate will affect the stability of landfill and have the risk of leakage and pollution. When the leachate is stored on the HDPE impermeable membrane, the extreme differentiation characteristics of the resistivity characteristics of the two and the boundary effect and other factors make the least squares and other traditional geophysical inversion methods unable to accurately invert the actual resistivity distribution, and then according to the resistivity the difference feature locates the height of the leachate water level above the HDPE membrane. In order to accurately describe the fine distribution of the local resistivity of the leachate-HDPE membrane inside the garbage dump, The traditional high density electrical method (ERT) device is improved, and a detection device (C-ERT) is proposed, and the resistivity inversion model algorithm of BP neural network is adopted. The method is verified by COMSOL theoretical model and field data collected from a domestic waste landfill in Jiangxi Province, and compared with the least square algorithm (LS). The results show that the BP algorithm based on C-ERT can effectively identify the leachate area above HDPE membrane, and the recognition accuracy is about 83.2%, while LS inversion algorithm can not identify the leachate area.
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图 1 川形装置数据神经网络反演框架和流程
Figure 1. Framework and process of neural network inversion of data of C-ERT
图 2 川形装置结构示意
注：A1~A32与B1~B32为供电电极，M1~M64为测量电极;a为电极间距，d为测线间距。
Figure 2. Schematic diagram of C-ERT structure
图 6 填埋场1现场测线布置示意
Figure 6. Schematic diagram of on-site survey line layout in landfill 1
图 7 渗滤液模型及BP神经网络和LS法的反演结果
Figure 7. Leachate model 1-2 and inversion results of BP and LS
图 8 填埋场现场及BP神经网络和LS法的反演结果
Figure 8. Landfill site map and inversion results of BP and LS
表 1 川形装置电极采集方式
Table 1. Electrode collection mode of C-ERT
供正电电极 供负电电极 测量电极 A1 B1 M1，M2 … … … A1 B32 M32，M33 A2 B1 M2，M3 … … … A32 B32 M63，M64 
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[1] 徐亚, 能昌信, 刘峰, 等.填埋场长期渗漏的环境风险评价方法与案例研究[J]. 环境科学研究,2015,28(4):605-612.XU Y, NAI C X, LIU F, et al. Method and case study to evaluate long-term environmental risks from landfill leakage[J]. Research of Environmental Sciences,2015,28(4):605-612. [2] 徐亚, 刘玉强, 刘景财, 等.填埋场渗漏风险评估的三级PRA模型及案例研究[J]. 环境科学研究,2014,27(4):447-454.XU Y, LIU Y Q, LIU J C, et al. 3-level of probability risk assessment on environmental risk of landfill leakage and it's case study[J]. Research of Environmental Sciences,2014,27(4):447-454. [3] 詹良通, 管仁秋, 陈云敏, 等.某填埋场垃圾堆体边坡失稳过程监测与反分析[J]. 岩石力学与工程学报,2010,29(8):1697-1705. [4] 孙汉武, 熊彬, 徐志锋, 等.高密度电法在垃圾填埋场勘探中的应用[J]. 矿产与地质,2020,34(6):1143-1148. doi: 10.19856/j.cnki.issn.1001-5663.2020.06.016SUN H W, XIONG B, XU Z F, et al. Application of high density electric method in the exploration of waste landfill site[J]. Mineral Resources and Geology,2020,34(6):1143-1148. doi: 10.19856/j.cnki.issn.1001-5663.2020.06.016 [5] 朱紫祥, 胡俊杰.高密度电法在岩溶地区溶洞勘查中的应用[J]. 工程地球物理学报,2017,14(3):290-293. doi: 10.3969/j.issn.1672-7940.2017.03.006ZHU Z X, HU J J. Application of high-density electrical method to exploration of Karst caves[J]. Chinese Journal of Engineering Geophysics,2017,14(3):290-293. doi: 10.3969/j.issn.1672-7940.2017.03.006 [6] 龚育龄, 叶腾飞, 董路, 等.卫生填埋场黏土衬层密实性试验研究[J]. 环境科学与技术,2011,34(9):9-11. doi: 10.3969/j.issn.1003-6504.2011.09.003GONG Y L, YE T F, DONG L, et al. Experimental study on compactness of clay liners in sanitary landfill[J]. Environmental Science & Technology,2011,34(9):9-11. doi: 10.3969/j.issn.1003-6504.2011.09.003 [7] 付士根, 杜文利, 胡家国.垃圾填埋场渗滤液水位地球物理探测技术初探[J]. 工程地球物理学报,2018,15(6):749-754. doi: 10.3969/j.issn.1672-7940.2018.06.011FU S G, DU W L, HU J G. Preliminary study on geophysics echnology of leachate level in landfill site[J]. Chinese Journal of Engineering Geophysics,2018,15(6):749-754. doi: 10.3969/j.issn.1672-7940.2018.06.011 [8] 张建智.时移电阻率法在垃圾填埋场渗滤液监测中的应用[J]. 中国煤炭地质,2019,31(10):80-85. doi: 10.3969/j.issn.1674-1803.2019.10.16 [9] 王玉玲, 能昌信, 王彦文, 等.2种电阻率法污染探测装置的抗噪声性能比较[J]. 环境科学研究,2013,26(8):879-884.WANG Y L, NAI C X, WANG Y W, et al. Comparison of anti-noise performance using wenner and dipole-dipole arrays in pollution survey[J]. Research of Environmental Sciences,2013,26(8):879-884. [10] EL-QADY G, USHIJIMA K. Inversion of DC resistivity data using neural networks[J]. Geophysical Prospecting,2001,49(4):417-430. doi: 10.1046/j.1365-2478.2001.00267.x [11] CALDERÓN-MACÍAS C, SEN M K, STOFFA P L. Artificial neural networks for parameter estimation in geophysics[J]. Geophysical Prospecting,2000,48(1):21-47. doi: 10.1046/j.1365-2478.2000.00171.x [12] 徐海浪, 吴小平.电阻率二维神经网络反演[J]. 地球物理学报,2006,49(2):584-589. doi: 10.3321/j.issn:0001-5733.2006.02.035XU H L, WU X P. 2-D resistivity inversion using the neural network method[J]. Chinese Journal of Geophysics,2006,49(2):584-589. doi: 10.3321/j.issn:0001-5733.2006.02.035 [13] NEYAMADPOUR A, TAIB S, WAN ABDULLAH W A T. Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: a MATLAB application[J]. Computers & Geosciences,2009,35(11):2268-2274. [14] NEYAMADPOUR A, WAN ABDULLAH W A T, TAIB S. Inversion of quasi-3D DC resistivity imaging data using artificial neural networks[J]. Journal of Earth System Science,2010,119(1):27-40. doi: 10.1007/s12040-009-0061-2 [15] 戴前伟, 江沸菠.基于混沌振荡PSO-BP算法的电阻率层析成像非线性反演[J]. 中国有色金属学报,2013,23(10):2897-2904.DAI Q W, JIANG F B. Nonlinear inversion for electrical resistivity tomography based on chaotic oscillation PSO-BP algorithm[J]. The Chinese Journal of Nonferrous Metals,2013,23(10):2897-2904. [16] 能昌信, 孙晓晨, 徐亚, 等.基于深度卷积神经网络的场地污染非线性反演方法[J]. 中国环境科学,2019,39(12):5162-5172.NAI C X, SUN X C, XU Y, et al. A site pollution nonlinear inversion method based on deep convolutional neural network[J]. China Environmental Science,2019,39(12):5162-5172. [17] BUTLER S L, SINHA G. Forward modeling of applied geophysics methods using Comsol and comparison with analytical and laboratory analog models[J]. Computers & Geosciences,2012,42:168-176. [18] 王泽亚, 徐亚, 能昌信, 等.海滨垃圾填埋场渗滤液污染土壤的复电阻率特性[J]. 环境科学研究,2020,33(4):1021-1027.WANG Z Y, XU Y, NAI C X, et al. Complex resistivity properties of leachate-contaminated soil in coastal landfill[J]. Research of Environmental Sciences,2020,33(4):1021-1027. [19] 朱勇, 能昌信, 陆晓春, 等.铬污染土壤超低频复电阻率频散特性[J]. 环境科学研究,2013,26(5):555-560.ZHU Y, NAI C X, LU X C, et al. The complex resistivity dispersion properties of chromium-contaminated soil in the ultra-low frequency power supply[J]. Research of Environmental Sciences,2013,26(5):555-560. [20] 肖宏跃, 雷宛. 地电学教程[M]. 北京: 地质出版社, 2008: 150-163. [21] 李春华, 胡文, 叶春, 等.基于BP神经网络预测地表水净化装置总氮的去除效果[J]. 环境工程技术学报,2018,8(6):651-655. doi: 10.3969/j.issn.1674-991X.2018.06.086LI C H, HU W, YE C, et al. Study on prediction of total nitrogen removal effect of a surface water purification device based on BP neural network[J]. Journal of Environmental Engineering Technology,2018,8(6):651-655. doi: 10.3969/j.issn.1674-991X.2018.06.086 [22] 林佳敏, 陈金良, 林晶晶, 等.BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测[J]. 环境工程技术学报,2019,9(5):573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261LIN J M, CHEN J L, LIN J J, et al. The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model[J]. Journal of Environmental Engineering Technology,2019,9(5):573-578. ⊕ doi: 10.12153/j.issn.1674-991X.2019.03.261 -
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