Abstract: To address challenges including complex underground pipeline surroundings,severe noise interference,and long-distance signal attenuation of optical fibers,this paper proposes a multi-modal feature fusion-based identification method for external damage incidents of optical cables to reduce the false alarm rate induced by third-party construction damage.The proposed me-thod constructs multi-modal input by combining time-series data and time-frequency image data of optical fibers signals for comprehensive event characterization.It extracts time-and frequency-domain features and models spatial correlations across different fiber channels to fully depict event attributes.A hierarchical attention fusion mechanism is further introduced:intra-modal attention captures critical information within individual modalities,while inter-modal attention learns complementary mutual information across modalities;gated units are adopted for adaptive weighted fusion to establish a robust identification model for optical fiber external damage events.Experimental results demonstrate that the proposed model improves the classification accuracy of fiber vibration signals across six event categories including manual excavation,excavator operation and suspected incidents.Compared with conventional machine learning and deep learning baseline models,the recall rate of the proposed model is increased by 20 percentage points and 30 percentage points respectively.

Key words: Multimodal data, Time-frequency domain features, Attention mechanisms, Channel correlation, Optical fiber external damage event identification