Seismic Monitoring of Rockfalls Enhanced by LiDAR and Photogrammetric Data: Towards Automatic Detection and Early Warning.

Continuous seismic monitoring has proven effective for detecting rockfalls, yet most studies rely on multiple stations or dense arrays, increasing cost and complexity. This study demonstrates that a single seismic station, located approximately 100 m from the event site, can detect small rockfalls (<0.005 m³), characterize their dynamics, and estimate their volumes.

The approach relies on careful signal processing, combining STA/LTA analysis, envelope calculation, and parameters such as amplitude, duration, and frequency, to reveal distinctive features of rockfall events. This methodology emphasizes the quality of extracted information over the quantity of data, enabling real-time identification of minor precursory events even amidst diverse environmental and anthropogenic noise.

LiDAR and photogrammetry provide high-resolution spatial data to calibrate and validate detections, but their limited temporal resolution prevents continuous monitoring. Controlled block-fall experiments further optimized station placement and confirmed the system’s sensitivity. These results demonstrate the potential of cost-effective, single-station seismic monitoring for automatic rockfall detection and early warning, offering a practical solution for hazardous mountainous regions.