Distinguishing between human-made interferences and natural background disturbances is of great significance for the safe operation of terrestrial optical cables because human-caused damage can be halted through timely intervention. To address the problem of small-sample disturbance recognition in distributed acoustic sensing (DAS) systems, this paper proposes a fused CNN–SVM classification model based on hybrid features. A convolutional neural network is employed to extract the high-level spatiotemporal features of disturbance signals, which are subsequently fused with statistical features and fed into a support vector machine for classification. Evaluated on open-source data, the proposed model achieves accuracy improvements of 9.1%, 8.7%, and 2.7% over the conventional CNN, the statistical-feature-based SVM, and the conventional CNN-SVM model, respectively. Furthermore, based on field-measured data, a dataset comprising 5664 samples was constructed, covering four typical disturbance-event types: background noise, drilling, knocking, and digging. The field classification results demonstrate that the three-layer convolutional structure of the model achieves a recognition accuracy of 98.5%. Both the ROC curves and multiple evaluation metrics indicate that the proposed three-layer fused CNN–SVM model delivers better classification performance and more balanced category recognition, offering a feasible reference for similar fiber disturbance engineering tasks.
Qiao et al. (Fri,) studied this question.