Extracting more and more accurate information to understand the detected vibration or acoustic targets better, has always been an important goal in signal recognition for Distributed Acoustic Sensor(DAS) with optical fiber. In this paper, we use one-dimensional Convolution Neural Networks(1D-CNNs) to extract the detailed temporal structure information at each signal node and utilize a bidirectional Long Short Term Memory(BiLSTM) network to dig out the spatial relationship among the different signal nodes, and then propose a novel identification method by treating the spatial-and temporalinformation in a different way, which is denoted as the 1DCNNs-BiLSTM model. The experimental results on the field data show better recognition performance can be achieved in the safety monitoring of the buried optical communication cable in urban with DAS. It helps to improve the recognition rate further compared with the other deep-learning methods frequently or possibly used for DAS signal recognition, such as the 1D-CNNs with a single temporal feature extraction, and 1DCNN-CNN and 2D-CNN models with simultaneous spatiotemporal feature learning. To the best of our knowledge, it is the first time to simultaneously extract and utilize the detailed temporal structure feature and the overall spatial connection through a customized deep learning network.

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This article proposes a new recognition method for DAS by using a mCNN-HMM combined model, which can identify the vibration sources more correctly by extracting the structural and sequential information of the signals simultaneously.

It is proposed a novel identification method by using CNN-BiLSTM model, with which better smart sensing ability can be achieved in the safety monitoring of the buried optical communication cable in urban with DAS.