Vibration area localization and event recognition for underground power optical cable in multiple laying scenarios based on deep learning

The current ϕ-OTDR vibration localization and recognition methods based on predominantly relies on assumptions such as bare fiber sensing, simulated experimental environments, or single known laying scenario. Most of them either focus on the localization or recognition of events, while even some studies that consider both ignore the improvement of performance to meet real-time requirements, which limits their practical application in multiple laying scenarios. To solve the above problems, we propose a method for vibration area localization and event recognition of the underground power optical cable based on PGSD-YOLO and 1DCNN-BiGRU-AFM. First, with real multiple laying scenarios of buried underground and manholes, using an underground power optical cable as distributed optical fiber vibration sensing, a ϕ-OTDR system is built to collect signals of vibration events. And then, high-pass and low-pass filters are combined for denoising to improve the signal quality. Secondly, PGSD-YOLO is designed to localize the vibration area and obtain its laying scenario. PGSD-YOLO combines the YOLOv11 with the multi-scale attention of PMSAM to enhance the feature extraction ability. Through the dynamic sampling strategy of DySample, the information loss of signals is reduced, and GSConv and VoVGSCSP are used to optimize feature fusion. Finally, based on the obtained scenario labels and the time-domain signals, 1DCNN-BiGRU-AFM is designed to recognize vibration events. 1DCNN-BiGRU-AFM combines the feature extraction ability of 1DCNN and the timing analysis ability of BiGRU, and optimizes feature fusion through the AFM mechanism. From experimental results, both PGSD-YOLO and 1DCNN-BiGRU-AFM meet the real-time and performance requirements in multiple scenarios.