A Surveillance System for Urban Utility Tunnel Subject to Third-Party Threats Based on Fiber-Optic DAS and FPN-BiLSTM Network

Inevitably influenced by the complicated underground geological structure in practical applications, the received signal response of the same disturbance event is inconsistent at different sensor nodes, which is an enormous challenge in large area safety monitoring applications based on the distributed acoustic sensing technology (DAS). Thus, in this paper the combination of the feature pyramid network and a bidirectional long short-term memory network (FPN-BiLSTM) is first introduced to perform an accuracy and efficiency identification of third-party threats under complicated underground geological structure. Firstly, the comprehensive spatio-temporal-spectral (STS) three-dimensional feature map of signal target is formed from some adjacent sensor nodes. In order to alleviate the high computational burden in the FPN model, the three-dimensional feature map is segmented into n time sequences. Then the sequences of data are sequentially transmitted to the FPN model for feature extraction. Subsequently, a Bi-LSTM network is applied to further extract the tandem contextual information among the sequences of time-frequency spatial feature vectors obtained by the FPN model. After that, the comprehensive STS multi-dimension feature vectors are extracted by the FPN-BiLSTM network for the identification of the target events. Finally, the field test results prove the proposed method can achieve a high recognition accuracy rate of 93.96% for five typical events with a fast response time of 0.463 s, which is superior to the traditional network models.