Project: Automatic Mass Movement Monitoring with Distributed Acoustic Sensing.

My research objectives: Monitoring mass movements, such as rockfalls, landslides and debris flows, is crucial in alpine regions with high-risk mountainous environments prone to sudden occurrences. Through innovative machine learning algorithms applied to Distributed Acoustic Sensing data, the objective is to provide early warning signs of impending major collapses, enhancing risk management for communities situated near steep slopes.

Last News (Newsletter #2 - February 2024): 

In recent months, our research has been exploring the capabilities of Distributed Acoustic Sensing (DAS) in seismic monitoring, particularly in identifying mass movements like rock avalanches. DAS, with its improved temporal and spatial resolution along fiber-optic cables, represents a significant advancement compared to traditional seismometer stations. Our study, centered around the Brienz landslide in Switzerland in June 2023, utilized a 10 km-long dark fiber, revealing 634 events associated with slope failures. However, challenges emerged due to anthropogenic noise interference, leading us to implement a neural network approach for more efficient seismic data analysis. I eagerly anticipate attending EGU 2024 to share our research and engage in discussions with fellow researchers.