Journal Article

Seismic Precursors to the Blatten, Switzerland Landslide Revealed by Unsupervised Machine Learning

Nishida, Kiwamu, Campillo, Michel, Favre‐Bulle, Guillaume, Seydoux, Léonard, Esfahani, Reza

2026

Overview

The transition from stable to unstable states in geological systems, such as landslides and fault zones, remains poorly understood. Seismic precursors and foreshocks related to the transition, if present, are often difficult to observe, and their interpretation remains challenging. Here, we report observations consistent with a nucleation process preceding the glacier collapse on 28 May 2025 in the village of Blatten, Switzerland. We identify three main groups of events using an unsupervised machine learning approach applied to 20 days of continuous seismic data recorded before the main event. We separate rockfalls from the seismic signatures associated with sliding‐related processes in the glacier. The observations are consistent with slip‐weakening behavior, with seismic activity accelerating during the final 2 days before failure. These results highlight the potential of unsupervised learning to identify such seismic precursors prior to collapse.

• Publisher: John Wiley & Sons, Inc
• Subject: Collapse / Earthquake prediction / Earthquakes / Failure / Fault zones / Geological faults / Glaciers / Landslides / Landslides & mudslides / Learning algorithms / Machine learning / Nucleation / Precursors / Rock falls / Rockfall / Seismic activity / Seismic data / Seismic response / Seismological data / Unsupervised learning