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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