Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Geophysical Research Letters

DOI: 10.1029/2025gl121175

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

Institutions: Institut des Sciences de la Terre, Université Grenoble Alpes, Centre National de la Recherche Scientifique, Institut de physique du globe de Paris, Université Paris Cité, Earthquake Research Institute, University of Tokyo, The University of Tokyo

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,190

OpenAlex ID: W4412707391

FWCI: 32.01

Cites:

Cited by: 7

Related to: 10