Tunnel Resistivity Inversion Method Utilizing Fuzzy C-means Clustering and Its Field Application

Water inrush disasters pose a significant challenge to tunnel construction.To mitigate this threat,it is crucial to employ location imaging techniques for underground water-bearing structures,as they play a pivotal role in disaster prevention.Due to its sensitivity to water body response,the tunnel resistivity detection method emerges as a practical approach for detecting water-bearing structures.The successful detection of water bodies has been achieved in determining their location and scale.However,in recent years,the geological conditions for tunnel construction have become increasingly complex,necessitating a higher level of detection accuracy.Therefore,there is an urgent need to research tunnel resistivity inversion methods that offer enhanced precision.By introducing the clustering algorithm into the tunnel resistivity inversion algorithm,the objective function and inversion equation of tunnel resistivity inversion based on fuzzy C-means clustering algorithm are established,and the tunnel resistivity inversion method based on fuzzy C-means clustering is proposed to realize the resistivity cluster inversion imaging.Additionally,numerical simulations were conducted for a representative water body located in front of the tunnel,and a field application test was performed at the Xianglushan tunnel of the Central Yunnan diversion project.The findings suggest that the tunnel resistivity method,utilizing fuzzy C-means clustering,offers a more precise depiction of water size at various scales compared to conventional resistivity inversion results and enhances the resolution and accuracy of the inversion outcomes.