基于时序电阻率法的土石坝体渗漏动态监测研究OA

During leakage detection in reservoir dam structures,conventional geophysical methods are limited to one-time surveys.Furthermore,the inherent non-uniqueness of geophysical inversion poses a significant challenge in enhancing the resolution accuracy of inverted profiles for detection purposes.This study aimed to reveal the spatio-temporal evolution pattern of dam seepage by establishing a long-term real-time monitoring system using time-lapse electrical resistivity tomography(ERT),thereby providing critical technical support for targeted remediation.By integrating Internet of Things(IoT)and 5G technologies with Artificial Intelligence(AI),the system achieves real-time data acquisition,transmission,and automated inversion.This method enables the analysis of electrical anomaly characteristics within leakage zones via dynamic imaging and facilitates correlating seepage extent with resistivity changes in response to rainfall events.Consequently,this approach significantly enhances the reliability and efficiency of dam seepage detection.It successfully implements long-term dynamic monitoring of dam leakage,offering a novel approach for ensuring the operational safety of reservoirs.