LR-Net for Weak Vibration Event Location and Recognition With DAS

Distributed acoustic sensing (DAS) has been deployed across various large-scale infrastructures for safety monitoring and health maintenance operations. Among these applications, the localization and recognition of vibrations are common and critical post-processing tasks. Currently, multi-event vibration localization and recognition present a significant challenge for post-processing algorithms. Moreover, weak vibrations, which are marked by short durations and limited propagation ranges, further exacerbate the difficulty of the accurate location and recognition. Consequently, these challenges contribute to a high false alarm rate and missed detection rate across the DAS system. To address those challenges, this paper proposes a location and recognition convolutional neural network (LR-Net) that can achieves end-to-end and multi-event recognition and localization along the fiber within a single sample. In the model, we propose the location-attention-mechanism feature fusion and squeeze framework (LAMFS) and dynamic matching strategy (DMS) to enable the model to focus on weak vibration and enhance its fitting ability. In the field experiments conducted in three typical scenarios, LR-Net achieves a 99.1% mAP for six types of events with merely average location error of ±1.5m. Moreover, the Nuisance Alarm Rate (NAR) and the Missing Alarm Rate (MAR) were only 1.4% and 1.06% respectively. These results demonstrate superior performance compared with other deep learning models. Above all, the proposed algorithm possesses significant practical value and can be adapted to other scenarios such as pipeline leak detection, perimeter security, and protection of important facilities.