Seismic Insights Into the Role of Rockfall in Rockslide Destruction Processes

The dynamic link between rockslide failure and rockfall activity remains elusive, primarily due to the challenge of detecting weak signals amidst high noise. We propose a seismic attribute guided deep learning framework that formulates rockfall detection as a time‐series segmentation task, facilitating the precise extraction of rockfall events from continuous monitoring data. Through self‐supervised pre‐training guided by seismic attributes (e.g., Sum of Squared Differences, SSD), our model captures noise‐robust features that substantially enhance its performance under low signal‐to‐noise ratio (SNR) scenarios. Comprehensive experiments show that, compared with the STA/LTA method commonly used in traditional monitoring and two U‐Net baselines based on waveform and spectral representations, the proposed method achieves improved detection performance and identifies more potential rockfall events than those listed in the existing catalog while maintaining physically consistent energy‐scaling and temporal evolution characteristics. Meanwhile, without re‐training, the model effectively reconstructs rockfall activity sequences at the unseen Illgraben rockslide (Switzerland), demonstrating stronger generalization capability than the purely data‐driven deep learning baselines and uncovering a distinct “growth–acceleration–quiescence–burst” evolutionary pattern prior to the main collapse. This seismic attribute guided deep learning approach provides a reliable technical strategy for short‐term rockslide risk assessment.