Intelligent Identification of Embankment Termite Nest Hidden Danger by Electrical Resistivity Tomography

Traditional electrical resistivity tomography (ERT) technology confronts bottlenecks such as the volume effect in the detection of termite nests in levees, while the ERT based on deep learning has insufficient interpretation accuracy due to small sample data. This study proposes an intelligent ERT diagnosis framework that integrates generative adversarial networks (GANs) with semantic segmentation models. The GAN-enhanced networks (GFU-Net and GFL-Net) are developed, incorporating a Squeeze-and-Excitation (SE) attention mechanism to suppress false anomalies. Additionally, a comprehensive loss function combining binary cross-entropy (BCE) and the Focal loss function is used to address the issue of sample imbalance. Using forward modeling based on the finite difference method (FDM), a termite nest hidden danger ERT dataset, which includes seven types of high-resistance anomaly configurations, is generated. Numerical simulations demonstrate that GFL-Net achieves a mean intersection-over-union (mIoU) of 97.68% and a spatial positioning error of less than 0.04 m. In field validation on a red clay embankment in Jiangxi Province, this method significantly improves the positioning accuracy of hidden termite nests compared to traditional least squares (LS) inversion. Excavation verification results show that the maximum error in the horizontal center and top burial depth of the termite nest identified by GFL-Net is less than 7% and 16%, respectively. The research findings provide reliable technical support for the accurate identification of termite nest hidden dangers in embankments.