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Peer-reviewedCC BY-NCGeophysical Research Letters

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

  • Reza Esfahani1
  • Michel Campillo1
  • Léonard Seydoux2
  • Kiwamu Nishida3
  • Guillaume Favre‐Bulle4
4 affiliations
  1. 1Institut des sciences de la Terre Université Grenoble Alpes Grenoble France
  2. 2Institut de physique du globe de Paris UMR CNRS Université Paris‐Cité Paris France
  3. 3Earthquake Research Institute University of Tokyo Tokyo Japan
  4. 4Service des dangers naturels Sion Switzerland
Open at sourceDOICite

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.

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Cite

BibTeXRIS
BibTeX
@article{esfahani2026seismic,
  title = {{Seismic Precursors to the Blatten, Switzerland Landslide Revealed by Unsupervised Machine Learning}},
  author = {Reza Esfahani and Michel Campillo and Léonard Seydoux and Kiwamu Nishida and Guillaume Favre‐Bulle},
  journal = {Geophysical Research Letters},
  year = {2026},
  month = may,
  doi = {10.1029/2025gl121175},
  url = {https://doi.org/10.1029/2025gl121175},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Seismic Precursors to the Blatten, Switzerland Landslide Revealed by Unsupervised Machine Learning
AU  - Reza Esfahani
AU  - Michel Campillo
AU  - Léonard Seydoux
AU  - Kiwamu Nishida
AU  - Guillaume Favre‐Bulle
PY  - 2026
DA  - 2026/05/15
JO  - Geophysical Research Letters
DO  - 10.1029/2025gl121175
UR  - https://doi.org/10.1029/2025gl121175
AB  - 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.
SN  - 0094-8276
SN  - 1944-8007
ER  -