Articles | Volume 26, issue 6
https://doi.org/10.5194/nhess-26-2921-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-2921-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jun 2026
Research article |
| 25 Jun 2026
| 25 Jun 2026
Exploring seismic mass-movement data with anomaly detection and dynamic time warping
Swiss Data Science Center, École Polytechnique Fédérale de Lausanne, EPFL INN Building, Station 14, 1015 Lausanne, Switzerland
Fabian Walter
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Patrick Paitz
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Matthias Meyer
Swiss Data Science Center, Eidgenössische Technische Hochschule Zürich, Andreasstrasse 5, 8092 Zürich, Switzerland
Michele Volpi
Swiss Data Science Center, Eidgenössische Technische Hochschule Zürich, Andreasstrasse 5, 8092 Zürich, Switzerland
Mathieu Salzmann
Swiss Data Science Center, École Polytechnique Fédérale de Lausanne, EPFL INN Building, Station 14, 1015 Lausanne, Switzerland
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Pascal Edme, Cristina Pérez-Guillén, Alec van Herwijnen, Johannes Aichele, Andri Simeon, Patrick Paitz, Fabian Walter, and Andreas Fichtner
EGUsphere, https://doi.org/10.5194/egusphere-2026-2373, https://doi.org/10.5194/egusphere-2026-2373, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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We present a near real-time snow avalanche detection method using Distributed Acoustic Sensing (DAS) on a ~10 km telecom cable following a road pass in the Swiss Alps. Avalanches are discriminated from vehicle traffic through distinct dual frequency spatio-temporal signatures. The proposed workflow achieved low false alert rates and detected 73 avalanches over one winter, demonstrating robust and cost-effective monitoring potential.
Gwendolyn Dasser, Alessandro Maissen, Michele Volpi, Jordan Aaron, Florian Denzinger, Hugo Raetzo, and Andrea Manconi
EGUsphere, https://doi.org/10.5194/egusphere-2026-375, https://doi.org/10.5194/egusphere-2026-375, 2026
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We aim to improve early landslide detection and monitoring by automating signal segmentation of Sentinel-1 radar interferograms. We first compared how different experts map mass-movement related signals on interferograms using ten case studies, identifying dataset uncertainties and limitations. We then trained and evaluated deep learning approaches to perform segmentation. Finally showing that models can achieve results comparable to expert variability while improving efficiency.
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev., 18, 8751–8776, https://doi.org/10.5194/gmd-18-8751-2025, https://doi.org/10.5194/gmd-18-8751-2025, 2025
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Avalanche detection systems are crucial for forecasting, but distinguishing avalanches from other seismic sources remains a challenge. We propose novel autoencoder models to automatically extract features and compare them with engineered seismic features. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers exhibit the highest sensitivity in detecting avalanches, while the engineered seismic classifier performs better overall.
Jiahui Kang, Fabian Walter, Tobias Halter, Patrick Paitz, and Andreas Fichtner
Earth Surf. Dynam., 13, 1133–1155, https://doi.org/10.5194/esurf-13-1133-2025, https://doi.org/10.5194/esurf-13-1133-2025, 2025
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Soil strength is influenced by wetness conditions, affecting slope stability, agricultural productivity, etc. Monitoring soil moisture is essential for risk management. We used Distributed Acoustic Sensing to monitor the deformation of a grass-covered slope over two summer months. We observed both long-term drying and daily "breathing" cycles: nighttime swelling and daytime shrinkage. By integrating strain and soil moisture data, we provide new field-scale insights into soil strength evolution.
Jakob Boyd Pernov, William H. Aeberhard, Michele Volpi, Eliza Harris, Benjamin Hohermuth, Sakiko Ishino, Ragnhild B. Skeie, Stephan Henne, Ulas Im, Patricia K. Quinn, Lucia M. Upchurch, and Julia Schmale
Atmos. Chem. Phys., 25, 6497–6537, https://doi.org/10.5194/acp-25-6497-2025, https://doi.org/10.5194/acp-25-6497-2025, 2025
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Particulate methanesulfonic acid (MSAp) is vital for the Arctic climate system. Numerical models struggle to reproduce the MSAp seasonal cycle. We evaluate three numerical models and one reanalysis product’s ability to simulate MSAp. We develop data-driven models for MSAp at four Arctic stations. The data-driven models outperform the numerical models and reanalysis product and identified precursor source-, chemical-processing-, and removal-related features as being important for modeling MSAp.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 1331–1351, https://doi.org/10.5194/nhess-25-1331-2025, https://doi.org/10.5194/nhess-25-1331-2025, 2025
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This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
Janneke van Ginkel, Fabian Walter, Fabian Lindner, Miroslav Hallo, Matthias Huss, and Donat Fäh
The Cryosphere, 19, 1469–1490, https://doi.org/10.5194/tc-19-1469-2025, https://doi.org/10.5194/tc-19-1469-2025, 2025
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This study on Glacier de la Plaine Morte in Switzerland employs various passive seismic analysis methods to identify complex hydraulic behaviours at the ice–bedrock interface. In 4 months of seismic records, we detect spatio-temporal variations in the glacier's basal interface, following the drainage of an ice-marginal lake. We identify a low-velocity layer, whose properties are determined using modelling techniques. This low-velocity layer results from temporary water storage subglacially.
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025, https://doi.org/10.5194/gmd-18-1829-2025, 2025
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Accurately measuring snow height is key for modeling approaches in climate science, snow hydrology, and avalanche forecasting. Erroneous snow height measurements often occur when snow height is low or changes, for instance during snowfall in summer. We prepare a new benchmark dataset with annotated snow height data and demonstrate how to improve the measurement quality using modern deep-learning approaches. Our approach can be easily implemented in a data pipeline for snow modeling.
Alessandro Maissen, Frank Techel, and Michele Volpi
Geosci. Model Dev., 17, 7569–7593, https://doi.org/10.5194/gmd-17-7569-2024, https://doi.org/10.5194/gmd-17-7569-2024, 2024
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By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
Fabian Walter, Elias Hodel, Erik S. Mannerfelt, Kristen Cook, Michael Dietze, Livia Estermann, Michaela Wenner, Daniel Farinotti, Martin Fengler, Lukas Hammerschmidt, Flavia Hänsli, Jacob Hirschberg, Brian McArdell, and Peter Molnar
Nat. Hazards Earth Syst. Sci., 22, 4011–4018, https://doi.org/10.5194/nhess-22-4011-2022, https://doi.org/10.5194/nhess-22-4011-2022, 2022
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Debris flows are dangerous sediment–water mixtures in steep terrain. Their formation takes place in poorly accessible terrain where instrumentation cannot be installed. Here we propose to monitor such source terrain with an autonomous drone for mapping sediments which were left behind by debris flows or may contribute to future events. Short flight intervals elucidate changes of such sediments, providing important information for landscape evolution and the likelihood of future debris flows.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
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A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Sebastian Landwehr, Michele Volpi, F. Alexander Haumann, Charlotte M. Robinson, Iris Thurnherr, Valerio Ferracci, Andrea Baccarini, Jenny Thomas, Irina Gorodetskaya, Christian Tatzelt, Silvia Henning, Rob L. Modini, Heather J. Forrer, Yajuan Lin, Nicolas Cassar, Rafel Simó, Christel Hassler, Alireza Moallemi, Sarah E. Fawcett, Neil Harris, Ruth Airs, Marzieh H. Derkani, Alberto Alberello, Alessandro Toffoli, Gang Chen, Pablo Rodríguez-Ros, Marina Zamanillo, Pau Cortés-Greus, Lei Xue, Conor G. Bolas, Katherine C. Leonard, Fernando Perez-Cruz, David Walton, and Julia Schmale
Earth Syst. Dynam., 12, 1295–1369, https://doi.org/10.5194/esd-12-1295-2021, https://doi.org/10.5194/esd-12-1295-2021, 2021
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The Antarctic Circumnavigation Expedition surveyed a large number of variables describing the dynamic state of ocean and atmosphere, freshwater cycle, atmospheric chemistry, ocean biogeochemistry, and microbiology in the Southern Ocean. To reduce the dimensionality of the dataset, we apply a sparse principal component analysis and identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and
hotspotsof interaction. Code and data are open access.
Małgorzata Chmiel, Fabian Walter, Lukas Preiswerk, Martin Funk, Lorenz Meier, and Florent Brenguier
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-205, https://doi.org/10.5194/nhess-2021-205, 2021
Preprint withdrawn
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The hanging glacier on Switzerland’s Mount Eiger regularly produces ice avalanches which threaten tourist activity and nearby infrastructure. Reliable forecasting remains a challenge as physical processes leading to ice rupture are not fully understood yet. We propose a new method for hanging glacier monitoring using repeating englacial seismic signals. Our approach allows monitoring temperature and meltwater driven changes occurring in the hanging glacier at seasonal and diurnal timescales.
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Short summary
We use anomaly detection to automatically find patterns in seismic data that may signal dangerous mass-movement events such as landslides, glacier collapses, or debris flows. Because such movements are rare, our approach reduces the amount of data that must be analyzed to find them, whether by experts, semi-supervised methods or clustering procedures. We demonstrate the usefulness of our approach by mining for mass movements in Switzerland and Greenland.
We use anomaly detection to automatically find patterns in seismic data that may signal...
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