## 1 Introduction  
Metro tunnels are an essential part of urban infrastructure, and the monitoring of the structural safety of tunnels is significant for train safety [1, 2, 3]. Illegal or unauthorized high-energy intrusions, such as blasting operations, drilling activities, and excavation by heavy machinery, may damage the structure of the tunnel walls and lead to tunnel collapses [4, 5]. Therefore, the timely detection and identification of these intrusions is particularly important for the prevention of accidents. Compared to traditional electrical sensors, fiber optic sensors have the advantages of electromagnetic immunity, high sensitivity, long distance transmission, and multiplexing [6, 7]; they have become one of the most reliable tunnel monitoring technologies in recent years. Fiber optic vibration sensing can be classified into the following three categories, according to the sensing principle: scattering [8; 9], fiber grating [10], and interferometric [11; 12]. Among these, Distributed Acoustic Sensing (DAS) technology, which is based on backward Rayleigh scattering, is one of the most mature fiber optic sensing technologies for vibration measurement [13; 14]; however, in long-distance quantitative detection, the signal-to-noise ratio and accuracy of the collected information are low due to the weak intensity of the backscattered light, which is prone to false alarms. To overcome the problem of low sensitivity, [PERSON] et al. [15] utilized scattering-enhanced optical fibers with ultra-weak grating arrays as distributed sensing units, achieving the timely and accurate identification and localization of illegal intrusions on drilling rigs in simulation experiments. The method still suffers from interference fading and weak signals [16]. Compared to scattering and grating array-sensing technologies, interferometric sensing directly monitors disturbance events along the fiber by detecting changes in the phase of forward- or backward-reflected light signals [17]. It has the advantages of a high signal-to-noise ratio, a high measurement accuracy, easy data processing, and a good real-time performance [18]. In this paper, the designed quasi-distributed optical fiber sensing system based on a Michelson interferometer [19] is employed to collect the vibration signals from the tunnel walls. A large number of sensors are required for the comprehensive and thorough monitoring of metro tunnels, which will generate massive amounts of signal data. Deep learning models [20; 21; 22] are capable of extracting high-level features from huge amounts of data, with a superior performance compared to traditional methods [23; 24]. [PERSON] et al. [25] applied deep neural networks to a 33 km fiber optic sensing system to identify and classify signals of external intrusion events, which is sufficient to quickly identify and locate disruptive events in complex environments with large amounts of monitoring data. [PERSON] et al. [26] proposed a method that combines a back propagation neural network to locate leaks in pipelines, with a root mean square error as low as 1.01%. [PERSON] et al. [8] proposed an unsupervised deep learning method that learns only normal DAS data features from normal events; the method achieves a detection rate of 91.5%. A two-layer convolutional neural network (CNN)-based classifier [27] was developed, where one layer was used to distinguish between third-party activities and environmental disturbances, and the other was used to determine the specific type of third-party event. The classifier achieved an accuracy rate of over 97%. [PERSON] et al. [28] combined a CNN and support vector machine (SVM), detecting that the accuracy of five fiber optic sensor signals can reach more than 98% in oil pipeline monitoring applications. The above methods demonstrate the great potential of CNNs in detection tasks.