Multi-dimensional geoelectrical resistivity imaging monitoring for debris flow based on neighborhood domain features

doi:10.13745/j.esf.sf.2023.2.60

Abstract

Abstract:

As a nonlinear system debris flows exhibit complex slope deformation patterns, and accurate quantification of the key parameters such as slip surface boundary change, rainfall infiltration boundary, head boundary and flow boundary during slope deformation is essential to studying the deformation mechanism of debris flows. Electrical resistivity imaging allows fast and multidimensional profile imaging based on compositional/structural differences between geotechnical bodies and difference in electrical property between strata. In this study, a dataset containing the “time-space-attribute” neighborhood parameters extracted from high-resolution shallow surface resistivity imaging data and various monitoring data was constructed by deep neural network learning, and used as input to generate, with rapid convergence, the relevant spatial weights matrix for monitoring items. On this basis, the deep resistivity imaging data were analyzed recursively, layer by layer, and a multidimensional internal structural model of the studied debris flow was accurately constructed based on the electrical characteristics of the debris flow to quantify its slope deformation process. This method was validated experimentally, which showed effective improvements in the real-timeliness and accuracy of various boundary parameters derived from geoelectrical resistivity imaging monitoring data. Results from this study have theoretical and methodological significances for understanding the slope deformation mechanism of debris flows.

Key words: debris-flow, resistivity imaging technique, neighborhood domain feature, deep neural network

CLC Number:

XU Haning, DENG Juzhi, XIAO Hui. Multi-dimensional geoelectrical resistivity imaging monitoring for debris flow based on neighborhood domain features[J]. Earth Science Frontiers, 2023, 30(6): 473-484.

Figures/Tables 16

Fig.1 Topographic features of the debris-flow deposit in Huangshi City (left), and planar layout of monitoring stations (right)

Fig.2 Bi-hourly rainfall (blue) and near-surface water content (orange) by GNSS systems

Fig.3 GNSS monitoring data by JC1, showing displacement time series in the northward (blue), eastward (red) and downward (yellow) directions

Fig.4 GNSS monitoring data by JC2

Fig.5 Top view of the debris-flow deposit, showing placement of GNSS systems

Fig.6 Two-dimensional spatial proximity relationships among resistivity measuring points

Fig.7 Methodology of nonlinear regression analysis with neighborhood deep neural network

Fig.8 Position of the added resistivity imaging monitoring system in relation to GNSS systems

Fig.9 Daily rainfall and soil water content

Fig.10 2D inversion of the March 20th resistivity imaging monitoring data

Fig.11 Error analysis for training dataset

Fig.12 Comparison of monitoring data with three regression models

Fig.13 Comparison of error rates for three algorithms

Table 1 Performance evaluation of three regression models

分析方法	RMSE	MAE	MSE	R²
线性回归模型	0.847	0.887	0.807	0.913
非线性回归模型	0.550	0.405	0.550	0.959
邻近域深度神经网络非线性回归模型	0.265	0.283	0.298	0.992

Fig.14 Comparison of model accuracy between two input datasets

Fig.15 Comparison of resistivity tomography models with imaging data between two input datasets

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