Abstract:

To address the issues of insufficient resolution in the high-density electrical resistivity method and limited accuracy in anomaly indentification for road collapse hazard detection, resolution tests for road collapse hazard detection based on high-density electrical resistivity method and investigation of an anomaly identification method using GMM clustering were conducted. Forward modeling was performed using the finite difference method, while inversion process was carried out using the Gauss-Newton method. Numerical simulations were conducted to assess the effect of different electrode spacing configurations on detection resolution. In the context of pipeline leakage-induced road collapse, geoelectric models for underground anomalies at various stages of development were designed, and GMM clustering analysis was applied to optimize the inversion results of the high-density electrical resistivity method. The results demonstrate that adjusting the electrode spacing and measurement parameters can significantly improve detection resolution. At a depth of 4.5 meters, the location and shape of underground anomalies at a scale of 1 meter can be effectively characterized by reducing the electrode spacing. An electrode spacing of 0.5 meters can balance detection accuracy and computational efficiency, corresponding to half the scale of the target anomaly. For anomalies buried at the same depth, the resistivity recovery of low-resistance anomalies is superior to that of high-resistance anomalies, providing the basis for parameter optimization for detecting various anomaly types. The feasibility of high-density electrical resistivity method to detect leakage-induced detects at different stages is demonstrated through tests on underground cavity models induced by pipeline leakage, while the identification accuracy of anomaly regions is further enhanced by the GMM-based clustering analysis.

Key words: Gaussian mixture model(GMM)clustering, high-density resistivity method, road collapse, hazards detection, resolution