Multi-branch long short-time memory convolution neural network for event identification in fiber-optic distributed disturbance sensor based on φ-OTDR

In this paper, we propose a novel neural network model named by Multi-branch Long Short-Time Memory Convolution Neural Network (MLSTM-CNN) for identifying disturbance signals in distributed optical fiber sensing system based on phase-sensitive optical time domain refiectometry (phi-OTDR). By unifying feature extraction and classification in a framework, MLSTM-CNN automatically extracts features at different time scales leveraging multi-branch layer and learnable LSTM layers, and then the disturbance signals are identified in the learnable CNN layers. Through constructing 25.05 km phi-OTDR experimental system, four kinds of real disturbance events, including watering, climbing, knocking, and pressing, and a false disturbance event can be effectively identified. Experimental results show that the average identification rate can reach 95.7%, and nuisance alarm rate (NAR) is 4.3%. Compared with the LSTM and CNN model, the recognition accuracy of the proposed model can be improved and the signal processing time can be efficiently reduced as well.