A spatial and temporal signal fusion based intelligent event recognition method for buried fiber distributed sensing system

Due to the characteristics of high sensitivity, fast response speed and multi-point monitoring, Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted attention in the field of perimeter security. However, the intrusion signals are susceptible to interference by ambient signals and difficult to be recognized. In the field of the vibration event recognition of Φ-OTDR system, the deep-learning based methods achieve great recognition ability. However, the previous works are mostly built on single signal source like temporal signal and may not completely adapt to different environment. In this work, a novel phenomenon is reported that a specific variation pattern of light intensity, which is related to the type of vibration source, is hided in the backscattering traces in spatial domain. Inspired by this, a lightweight model and data composition method is proposed to fuse spatial information with temporal correlation information based on end-to-end CNN-LSTM combined model and bicubic scaling. The experiment is conducted on a portable computer (a Nvidia GPU RTX 2080 with 2944 compute unified device architecture cores) with 8 event types and shows that this method can achieve 95.56% validation accuracy through less than 6 min training. Compared with previous method trained by single image structure signal, this lightweight work can achieve higher validation accuracy faster.