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Phase-sensitive optical time domain reflectometer (Φ-OTDR) can detect the occurrence of external vibration, but lacks of ability to classify multiple similar events. With the help of machine learning method, the events can be basically classified. An event recognition method based on deep learning and traditional classifier is proposed in this paper to further improve the classification accuracy. Firstly, the temporal-spatial data matrix from Φ-OTDR is directly sent to convolutional neural network (CNN) and the data features can be obtained automatically. Then these data features are transmitted to a traditional classifier for further classification. The traditional classifier can find a better classification hyperplane to improve the classification accuracy. In this paper, the most suitable classifier is support vector machine (SVM). As CNN works as a black box, T-distributed stochastic neighbor embedding (T-SNE) method and gradient-weighted class activation mapping (Grad-CAM) method are applied to visualize CNN's working process, which illustrates the correctness of feature extraction. Experimental results based on 11,997 images under eight event categories show that the classification accuracy can be improved by the traditional classifier. The CNN+SVM strategy performs the best and reaches 94.17 % accuracy, which offers 2.04 % improvement compared with using CNN alone. [ABSTRACT FROM AUTHOR]

This paper proposed a method for discriminating two close perturbations, whose interval is less than spatial resolution, in Φ-OTDR system. The mathematical model under two close perturbations is studied and the frequency features of temporal signal from every sensing positions are obtained. The model shows that if the two close perturbations have different frequencies, they can be distinguished through spatial-frequency analysis. When the two perturbations have even the same frequency, they can still be distinguished by inspecting higher-order harmonic frequencies. Both computer simulation and experiments have been carried out to support the effectiveness of this method. [ABSTRACT FROM AUTHOR]