Articles | Volume 21, issue 1
https://doi.org/10.5194/nhess-21-339-2021
© Author(s) 2021. 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-21-339-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jan 2021
Research article |
| 27 Jan 2021
| 27 Jan 2021
Near-real-time automated classification of seismic signals of slope failures with continuous random forests
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zürich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
Clément Hibert
Université de Strasbourg, CNRS, EOST/IPGS UMR 7516, 67000 Strasbourg, France
Alec van Herwijnen
WSL Institute for Snow Avalanche Research SLF, Davos, Switzerland
Lorenz Meier
Geopraevent Ltd., Zürich, Switzerland
Fabian Walter
Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zürich, Switzerland
Related authors
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
Short summary
Short summary
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.
Grégoire Bobillier, Bertil Trottet, Bastian Bergfeld, Ron Simenhois, Alec van Herwijnen, Jürg Schweizer, and Johan Gaume
Nat. Hazards Earth Syst. Sci., 25, 2215–2223, https://doi.org/10.5194/nhess-25-2215-2025, https://doi.org/10.5194/nhess-25-2215-2025, 2025
Short summary
Short summary
Our study investigates the initiation of snow slab avalanches. Combining experimental data with numerical simulations, we show that on gentle slopes, cracks form and propagate due to compressive fractures within a weak layer. On steeper slopes, crack velocity can increase dramatically after approximately 5 m due to a fracture mode transition from compression to shear. Understanding these dynamics provides a crucial missing piece in the puzzle of dry-snow slab avalanche formation.
Philipp L. Rosendahl, Johannes Schneider, Grégoire Bobillier, Florian Rheinschmidt, Bastian Bergfeld, Alec van Herwijnen, and Philipp Weißgraeber
Nat. Hazards Earth Syst. Sci., 25, 1975–1991, https://doi.org/10.5194/nhess-25-1975-2025, https://doi.org/10.5194/nhess-25-1975-2025, 2025
Short summary
Short summary
Avalanche formation depends on crack propagation in weak snow layers, but the conditions that stop a crack remain unclear. We show that slab touchdown reduces the energy driving crack growth, which can halt propagation even under static conditions. This suggests that crack arrest is influenced not only by snowpack variability or dynamics but also by mechanical interactions within the snowpack. Our findings refine avalanche prediction models and improve hazard assessment.
Jiahui Kang, Fabian Walter, Tobias Halter, Patrick Paitz, and Andreas Fichtner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1725, https://doi.org/10.5194/egusphere-2025-1725, 2025
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Amelie Fees, Michael Lombardo, Alec van Herwijnen, Peter Lehmann, and Jürg Schweizer
The Cryosphere, 19, 1453–1468, https://doi.org/10.5194/tc-19-1453-2025, https://doi.org/10.5194/tc-19-1453-2025, 2025
Short summary
Short summary
Glide-snow avalanches release at the soil–snow interface due to a loss of friction, which is suspected to be linked to interfacial water. The importance of the interfacial water was investigated with a spatio-temporal monitoring setup for soil and local snow on an avalanche-prone slope. Seven glide-snow avalanches were released on the monitoring grid (winter seasons 2021/22 to 2023/24) and provided insights into the source, quantity, and spatial distribution of interfacial water before avalanche release.
Michael Lombardo, Amelie Fees, Anders Kaestner, Alec van Herwijnen, Jürg Schweizer, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-304, https://doi.org/10.5194/egusphere-2025-304, 2025
Short summary
Short summary
Water flow in snow is important for many applications including snow hydrology and avalanche forecasting. This work investigated the role of capillary forces at the soil-snow interface during capillary rise experiments using neutron radiography. The results showed that the properties of both the snow and the transitional layer below the snow affected the water flow. This work will allow for better representations of water flow across the soil-snow interface in snowpack models.
Bastian Bergfeld, Karl W. Birkeland, Valentin Adam, Philipp L. Rosendahl, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 321–334, https://doi.org/10.5194/nhess-25-321-2025, https://doi.org/10.5194/nhess-25-321-2025, 2025
Short summary
Short summary
To release a slab avalanche, a crack in a weak snow layer beneath a cohesive slab has to propagate. Information on that is essential for assessing avalanche risk. In the field, information can be gathered with the propagation saw test (PST). However, there are different standards on how to cut the PST. In this study, we experimentally investigate the effect of these different column geometries and provide models to correct for imprecise field test geometry effects on the critical cut length.
Stephanie Mayer, Martin Hendrick, Adrien Michel, Bettina Richter, Jürg Schweizer, Heini Wernli, and Alec van Herwijnen
The Cryosphere, 18, 5495–5517, https://doi.org/10.5194/tc-18-5495-2024, https://doi.org/10.5194/tc-18-5495-2024, 2024
Short summary
Short summary
Understanding the impact of climate change on snow avalanche activity is crucial for safeguarding lives and infrastructure. Here, we project changes in avalanche activity in the Swiss Alps throughout the 21st century. Our findings reveal elevation-dependent patterns of change, indicating a decrease in dry-snow avalanches alongside an increase in wet-snow avalanches at elevations above the current treeline. These results underscore the necessity to revisit measures for avalanche risk mitigation.
Amelie Fees, Alec van Herwijnen, Michael Lombardo, Jürg Schweizer, and Peter Lehmann
Nat. Hazards Earth Syst. Sci., 24, 3387–3400, https://doi.org/10.5194/nhess-24-3387-2024, https://doi.org/10.5194/nhess-24-3387-2024, 2024
Short summary
Short summary
Glide-snow avalanches release at the ground–snow interface, and their release process is poorly understood. To investigate the influence of spatial variability (snowpack and basal friction) on avalanche release, we developed a 3D, mechanical, threshold-based model that reproduces an observed release area distribution. A sensitivity analysis showed that the distribution was mostly influenced by the basal friction uniformity, while the variations in snowpack properties had little influence.
Floriane Provost, Dimitri Zigone, Emmanuel Le Meur, Jean-Philippe Malet, and Clément Hibert
The Cryosphere, 18, 3067–3079, https://doi.org/10.5194/tc-18-3067-2024, https://doi.org/10.5194/tc-18-3067-2024, 2024
Short summary
Short summary
The recent calving of Astrolabe Glacier in November 2021 presents an opportunity to better understand the processes leading to ice fracturing. Optical-satellite imagery is used to retrieve the calving cycle of the glacier ice tongue and to measure the ice velocity and strain rates in order to document fracture evolution. We observed that the presence of sea ice for consecutive years has favoured the glacier extension but failed to inhibit the growth of fractures that accelerated in June 2021.
Gwendolyn Dasser, Valentin T. Bickel, Marius Rüetschi, Mylène Jacquemart, Mathias Bavay, Elisabeth D. Hafner, Alec van Herwijnen, and Andrea Manconi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1510, https://doi.org/10.5194/egusphere-2024-1510, 2024
Short summary
Short summary
Understanding snowpack wetness is crucial for predicting wet snow avalanches, but detailed data is often limited to certain locations. Using satellite radar, we monitor snow wetness spatially continuously. By combining different radar tracks from Sentinel-1, we improved spatial resolution and tracked snow wetness over several seasons. Our results indicate higher snow wetness to correlate with increased wet snow avalanche activity, suggesting our method can help identify potential risk areas.
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-76, https://doi.org/10.5194/gmd-2024-76, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Avalanche seismic detection systems are key for forecasting, but distinguishing avalanches from other seismic sources remains challenging. We propose novel autoencoder models to automatically extract features and compare them with standard seismic attributes. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers have the highest sensitivity to detect avalanches, while the standard seismic classifier performs better overall.
Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff
Earth Surf. Dynam., 12, 641–656, https://doi.org/10.5194/esurf-12-641-2024, https://doi.org/10.5194/esurf-12-641-2024, 2024
Short summary
Short summary
Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
Short summary
Short summary
We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Mathieu Le Breton, Éric Larose, Laurent Baillet, Yves Lejeune, and Alec van Herwijnen
The Cryosphere, 17, 3137–3156, https://doi.org/10.5194/tc-17-3137-2023, https://doi.org/10.5194/tc-17-3137-2023, 2023
Short summary
Short summary
We monitor the amount of snow on the ground using passive radiofrequency identification (RFID) tags. These small and inexpensive tags are wirelessly read by a stationary reader placed above the snowpack. Variations in the radiofrequency phase delay accurately reflect variations in snow amount, known as snow water equivalent. Additionally, each tag is equipped with a sensor that monitors the snow temperature.
Bastian Bergfeld, Alec van Herwijnen, Grégoire Bobillier, Philipp L. Rosendahl, Philipp Weißgraeber, Valentin Adam, Jürg Dual, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 23, 293–315, https://doi.org/10.5194/nhess-23-293-2023, https://doi.org/10.5194/nhess-23-293-2023, 2023
Short summary
Short summary
For a slab avalanche to release, the snowpack must facilitate crack propagation over large distances. Field measurements on crack propagation at this scale are very scarce. We performed a series of experiments, up to 10 m long, over a period of 10 weeks. Beside the temporal evolution of the mechanical properties of the snowpack, we found that crack speeds were highest for tests resulting in full propagation. Based on these findings, an index for self-sustained crack propagation is proposed.
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
Short summary
Short summary
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.
François Noël, Michel Jaboyedoff, Andrin Caviezel, Clément Hibert, Franck Bourrier, and Jean-Philippe Malet
Earth Surf. Dynam., 10, 1141–1164, https://doi.org/10.5194/esurf-10-1141-2022, https://doi.org/10.5194/esurf-10-1141-2022, 2022
Short summary
Short summary
Rockfall simulations are often performed to make sure infrastructure is safe. For that purpose, rockfall trajectory data are needed to calibrate the simulation models. In this paper, an affordable, flexible, and efficient trajectory reconstruction method is proposed. The method is tested by reconstructing trajectories from a full-scale rockfall experiment involving 2670 kg rocks and a flexible barrier. The results highlight improvements in precision and accuracy of the proposed method.
Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer
The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, https://doi.org/10.5194/tc-16-4593-2022, 2022
Short summary
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.
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
Short summary
Short summary
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.
Antoine Guillemot, Alec van Herwijnen, Eric Larose, Stephanie Mayer, and Laurent Baillet
The Cryosphere, 15, 5805–5817, https://doi.org/10.5194/tc-15-5805-2021, https://doi.org/10.5194/tc-15-5805-2021, 2021
Short summary
Short summary
Ambient noise correlation is a broadly used method in seismology to monitor tiny changes in subsurface properties. Some environmental forcings may influence this method, including snow. During one winter season, we studied this snow effect on seismic velocity of the medium, recorded by a pair of seismic sensors. We detected and modeled a measurable effect during early snowfalls: the fresh new snow layer modifies rigidity and density of the medium, thus decreasing the recorded seismic velocity.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
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
Short summary
Short summary
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.
Bastian Bergfeld, Alec van Herwijnen, Benjamin Reuter, Grégoire Bobillier, Jürg Dual, and Jürg Schweizer
The Cryosphere, 15, 3539–3553, https://doi.org/10.5194/tc-15-3539-2021, https://doi.org/10.5194/tc-15-3539-2021, 2021
Short summary
Short summary
The modern picture of the snow slab avalanche release process involves a
dynamic crack propagation phasein which a whole slope becomes detached. The present work contains the first field methodology which provides the temporal and spatial resolution necessary to study this phase. We demonstrate the versatile capabilities and accuracy of our method by revealing intricate dynamics and present how to determine relevant characteristics of crack propagation such as crack speed.
Eef C. H. van Dongen, Guillaume Jouvet, Shin Sugiyama, Evgeny A. Podolskiy, Martin Funk, Douglas I. Benn, Fabian Lindner, Andreas Bauder, Julien Seguinot, Silvan Leinss, and Fabian Walter
The Cryosphere, 15, 485–500, https://doi.org/10.5194/tc-15-485-2021, https://doi.org/10.5194/tc-15-485-2021, 2021
Short summary
Short summary
The dynamic mass loss of tidewater glaciers is strongly linked to glacier calving. We study calving mechanisms under a thinning regime, based on 5 years of field and remote-sensing data of Bowdoin Glacier. Our data suggest that Bowdoin Glacier ungrounded recently, and its calving behaviour changed from calving due to surface crevasses to buoyancy-induced calving resulting from basal crevasses. This change may be a precursor to glacier retreat.
Bettina Richter, Alec van Herwijnen, Mathias W. Rotach, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 20, 2873–2888, https://doi.org/10.5194/nhess-20-2873-2020, https://doi.org/10.5194/nhess-20-2873-2020, 2020
Short summary
Short summary
We investigated the sensitivity of modeled snow instability to uncertainties in meteorological input, typically found in complex terrain. The formation of the weak layer was very robust due to the long dry period, indicated by a widespread avalanche problem. Once a weak layer has formed, precipitation mostly determined slab and weak layer properties and hence snow instability. When spatially assessing snow instability for avalanche forecasting, accurate precipitation patterns have to be known.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
Short summary
Short summary
Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
Cited articles
Abellán, A., Vilaplana, J. M., Calvet, J., García-Sellés, D., and Asensio, E.: Rockfall monitoring by Terrestrial Laser Scanning – case study of the basaltic rock face at Castellfollit de la Roca (Catalonia, Spain), Nat. Hazards Earth Syst. Sci., 11, 829–841, https://doi.org/10.5194/nhess-11-829-2011, 2011. a
Allen, R.: Automatic phase pickers: Their present use and future prospects,
Bull. Seismol. Soc. Am., 72, S225–S242, 1982. a
Allen, S. and Huggel, C.: Extremely warm temperatures as a potential cause of recent high mountain rockfall, Global Planet. Change, 107, 59—9, https://doi.org/10.1016/j.gloplacha.2013.04.007, 2013. a
Allstadt, K.: Extracting source characteristics and dynamics of the August 2010 Mount Meager landslide from broadband seismograms, J. Geophys. Res.-Earth, 118, 1472–1490, https://doi.org/10.1002/jgrf.20110, 2013. a, b
Allstadt, K. E., Matoza, R. S., Lockhart, A. B., Moran, S. C., Caplan-Auerbach, J., Haney, M. M., Thelen, W. A., and Malone, S. D.: Seismic and acoustic signatures of surficial mass movements at volcanoes, J. Volcanol. Geoth. Res., 364, 76–106, https://doi.org/10.1016/j.jvolgeores.2018.09.007, 2018. a
Badoux, A., Graf, C., Rhyner, J., Kuntner, R., and McArdell, B. W.: A
debris-flow alarm system for the Alpine Illgraben catchment: Design and
performance, Nat. Hazards, 49, 517–539, https://doi.org/10.1007/s11069-008-9303-x, 2009. a
Baer, P., Huggel, C., McArdell, B. W., and Frank, F.: Changing debris flow
activity after sudden sediment input: a case study from the Swiss Alps, Geol. Today, 33, 216–223, https://doi.org/10.1111/gto.12211, 2017. a, b
Bennett, G. L., Molnar, P., McArdell, B. W., Schlunegger, F., and Burlando, P.: Patterns and controls of sediment production, transfer and yield in the
Illgraben, Geomorphology, 188, 68–82, https://doi.org/10.1016/j.geomorph.2012.11.029,
2013. a, b
Beyreuther, M., Barsch, R., Krischer, L., Megies, T., Behr, Y., and Wassermann, J.: ObsPy: A python toolbox for seismology, Seismol. Res. Lett.,
81, 530–533, https://doi.org/10.1785/gssrl.81.3.530, 2010. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a, b
Burtin, A., Bollinger, L., Vergne, J., Cattin, R., and Nábělek, J. L.: Spectral analysis of seismic noise induced by rivers: A new tool to monitor spatiotemporal changes in stream hydrodynamics, J. Geophys. Res.-Solid, 113, B05301, https://doi.org/10.1029/2007JB005034, 2008. a
Burtin, A., Hovius, N., and Turowski, J. M.: Seismic monitoring of torrential and fluvial processes, Earth Surf. Dynam., 4, 285–307, https://doi.org/10.5194/esurf-4-285-2016, 2016. a
Chawla, N. V.: Data Mining for Imbalanced Datasets: An Overview, Springer US, Boston, MA, 875–886, https://doi.org/10.1007/978-0-387-09823-4_45, 2010. a
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P.: SMOTE:
Synthetic minority over-sampling technique, J. Artific. Intel. Res., 16, 321–357, https://doi.org/10.1613/jair.953, 2002. a
Christ, M., Kempa-Liehr, A. W., and Feindt, M.: Distributed and parallel time
series feature extraction for industrial big data applications, preprint: available at: http://arxiv.org/abs/1610.07717 (last access: 21 January 2021), 2016. a
Coe, J. A., Bessette-Kirton, E. K., and Geertsema, M.: Increasing rock-avalanche size and mobility in Glacier Bay National Park and Preserve,
Alaska detected from 1984 to 2016 Landsat imagery, Landslides, 15, 393–407,
https://doi.org/10.1007/s10346-017-0879-7, 2018. a
Coviello, V., Arattano, M., Comiti, F., Macconi, P., and Marchi, L.: Seismic
Characterization of Debris Flows: Insights into Energy Radiation and
Implications for Warning, J. Geophys. Res.-Earth, 124, 1440–1463, https://doi.org/10.1029/2018JF004683, 2019. a, b
Dammeier, F., Moore, J. R., Hammer, C., Haslinger, F., and Loew, S.: Automatic detection of alpine rockslides in continuous seismic data using hidden Markov models, J. Geophys. Res.-Earth, 121, 351–371, https://doi.org/10.1002/2015JF003647, 2016. a, b, c, d
Deparis, J., Jongmans, D., Cotton, F., Baillet, L., Thouvenot, F., and Hantz,
D.: Analysis of rock-fall and rock-fall avalanche seismograms in the French
Alps, Bull. Seismol. Soc. Am., 98, 1781–1796, https://doi.org/10.1785/0120070082, 2008. a
Dietze, M., Turowski, J. M., Cook, K. L., and Hovius, N.: Spatiotemporal patterns, triggers and anatomies of seismically detected rockfalls, Earth Surf. Dynam., 5, 757–779, https://doi.org/10.5194/esurf-5-757-2017, 2017. a, b
Ekström, G. and Stark, C. P.: Simple scaling of catastrophic landslide
dynamics, Science, 339, 1416–1419, https://doi.org/10.1126/science.1232887, 2013. a
Farin, M., Tsai, V. C., Lamb, M. P., and Allstadt, K. E.: A physical model of
the high-frequency seismic signal generated by debris flows, Earth Surf. Proc. Land., 44, 2529–2543, https://doi.org/10.1002/esp.4677, 2019. a
Fawcett, T.: An introduction to ROC analysis, Pattern Recognit. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006. a
Fernández-Delgado, M., Cernadas, E., Barro, S., and Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems?, J. Mach. Learn. Res., 15, 3133–3181, 2014. a
Geopravent: Bondo, Val Bondasca, Seismic, updated hourly, available at: https://data.geopraevent.ch/index.php (last access: 21 January 2021), 2017. a
Gimbert, F., Tsai, V. C., and Lamb, M. P.: A physicalmodel for seismic noise
generation by turbulent flow in rivers, J. Geophys. Res.-Earth, 119, 2209–2238, https://doi.org/10.1002/2014JF003201, 2014. a
Hammer, C., Ohrnberger, M., and Fäh, D.: Classifying seismic waveforms
from scratch: A case study in the alpine environment, Geophys. J. Int., 192, 425–439, https://doi.org/10.1093/gji/ggs036, 2013. a, b
Hammer, C., Fäh, D., and Ohrnberger, M.: Automatic detection of wet-snow avalanche seismic signals, Nat. Hazards, 86, 601–618,
https://doi.org/10.1007/s11069-016-2707-0, 2017. a
Heck, M., Hammer, C., Van Herwijnen, A., Schweizer, J., and Fäh, D.:
Automatic detection of snow avalanches in continuous seismic data using hidden Markov models, Nat. Hazards Earth Syst. Sci., 18, 383–396, https://doi.org/10.5194/nhess-18-383-2018, 2018. a, b, c, d
Heck, M., Van Herwijnen, A., Hammer, C., Hobiger, M., Schweizer, J., and
Fäh, D.: Automatic detection of avalanches combining array classification and localization, Earth Surf. Dynam., 7, 491–503, https://doi.org/10.5194/esurf-7-491-2019, 2019. a
Helmstetter, A. and Garambois, S.: Seismic monitoring of Schilienne rockslide (French Alps): Analysis of seismic signals and their correlation with rainfalls, J. Geophys. Res.-Earth, 115, 03016, https://doi.org/10.1029/2009JF001532, 2010. a, b, c, d
Hibert, C., Mangeney, A., Grandjean, G., and Shapiro, N. M.: Slope instabilities in Dolomieu crater, Réunion Island: From seismic signals
to rockfall characteristics, J. Geophys. Res.-Earth, 116, F04032, https://doi.org/10.1029/2011JF002038, 2011. a, b
Hibert, C., Mangeney, A., Grandjean, G., Baillard, C., Rivet, D., Shapiro, N. M., Satriano, C., Maggi, A., Boissier, P., Ferrazzini, V., and Crawford, W.: Automated identification, location, and volume estimation of rockfalls at Piton de la Fournaise volcano, J. Geophys. Res.-Earth, 119, 1082–1105, https://doi.org/10.1002/2013JF002970, 2014. a
Hibert, C., Provost, F., Malet, J. P., Maggi, A., Stumpf, A., and Ferrazzini,
V.: Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm, J. Volcanol. Geoth. Res., 340, 130–142, https://doi.org/10.1016/j.jvolgeores.2017.04.015, 2017. a, b, c, d, e, f
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y.,
Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin, S., Orlove, B., and Steltzer, H.: High Mountain Areas, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., available at: http://report.ipcc.ch/srocc/pdf/SROCC_FinalDraft_Chapter2.pdf (last access: 26 January 2021), 2019. a
Krischer, L., Megies, T., Barsch, R., Beyreuther, M., Lecocq, T., Caudron, C., and Wassermann, J.: ObsPy: A bridge for seismology into the scientific
Python ecosystem, Comput. Sci. Discov., 8, 014003, https://doi.org/10.1088/1749-4699/8/1/014003, 2015. a
Lai, V. H., Tsai, V. C., Lamb, M. P., Ulizio, T. P., and Beer, A. R.: The
Seismic Signature of Debris Flows: Flow Mechanics and Early Warning at Montecito, California, Geophys. Res. Lett., 45, 5528–5535, https://doi.org/10.1029/2018GL077683, 2018. a
Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L., Guéguen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S.,
Gimbert, F., and Massey, C.: Environmental seismology: What can we learn on
earth surface processes with ambient noise?, J. Appl. Geophys., 116, 62–74, https://doi.org/10.1016/j.jappgeo.2015.02.001, 2015. a
Lemaître, G., Nogueira, F., and Aridas, C. K.: Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning, J. Mach. Learn. Res., 18, 559–563, 2017. a
Maggi, A., Ferrazzini, V., Hibert, C., Beauducel, F., Boissier, P., and
Amemoutou, A.: Implementation of a multistation approach for automated event
classification at Piton de la Fournaise volcano, Seismol. Res. Lett., 88, 878–891, https://doi.org/10.1785/0220160189, 2017. a
Malfante, M., Dalla Mura, M., Metaxian, J. P., Mars, J. I., Macedo, O., and
Inza, A.: Machine Learning for Volcano-Seismic Signals: Challenges and
Perspectives, IEEE Sig. Process. Mag., 35, 20–30, https://doi.org/10.1109/MSP.2017.2779166, 2018. a
Marchetti, E., Walter, F., Barfucci, G., Genco, R., Wenner, M., Ripepe, M.,
McArdell, B., and Price, C.: Infrasound Array Analysis of Debris Flow Activity and Implication for Early Warning, J. Geophys. Res.-Earth, 124, 567–587, https://doi.org/10.1029/2018JF004785, 2019. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.:
Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a, b
Phillips, M., Wolter, A., Lüthi, R., Amann, F., Kenner, R., and Bühler, Y.: Rock slope failure in a recently deglaciated permafrost rock wall at Piz Kesch (Eastern Swiss Alps), February 2014, Earth Surf. Proc. Land., 42, 426–438, https://doi.org/10.1002/esp.3992, 2017. a
Rosser, N., Lim, M., Petley, D., Dunning, S., and Allison, R.: Patterns of
precursory rockfall prior to slope failure, J. Geophys. Res.-Earth, 112, F04014, https://doi.org/10.1029/2006JF000642, 2007. a
Schlunegger, F., Badoux, A., McArdell, B. W., Gwerder, C., Schnydrig, D.,
Rieke-Zapp, D., and Molnar, P.: Limits of sediment transfer in an alpine
debris-flow catchment, Illgraben, Switzerland, Quaternary Sci. Rev., 28, 1097–1105, https://doi.org/10.1016/j.quascirev.2008.10.025, 2009. a
Stone, M.: Cross-Validatory Choice and Assessment of Statistical Predictions,
J. Roy. Stat. Soc. Ser. B, 36, 111–133, https://doi.org/10.1111/j.2517-6161.1974.tb00994.x, 1974. a
Suriñach, E., Furdada, G., Sabot, F., Biescas, B., and Vilaplana, J. M.:
On the characterization of seismic signals generated by snow avalanches for
monitoring purposes, Ann. Glaciol., 32, 268–274, https://doi.org/10.3189/172756401781819634, 2001. a
Suriñach, E., Vilajosana, I., Khazaradze, G., Biescas, B., Furdada, G.,
and Vilaplana, J. M.: Seismic detection and characterization of landslides and other mass movements, Nat. Hazards Earth Syst. Sci., 5, 791–798, https://doi.org/10.5194/nhess-5-791-2005, 2005. a
Tsai, V. C., Minchew, B., Lamb, M. P., and Ampuero, J. P.: A physical model
for seismic noise generation from sediment transport in rivers, Geophys. Res. Lett., 39, L02404, https://doi.org/10.1029/2011GL050255, 2012. a
van Herwijnen, A. and Schweizer, J.: Monitoring avalanche activity using a
seismic sensor, Cold Reg. Sci. Technol., 69, 165–176, https://doi.org/10.1016/j.coldregions.2011.06.008, 2011. a
van Westen, C. J., van Asch, T. W., and Soeters, R.: Landslide hazard and risk zonation – Why is it still so difficult?, Bull. Eng. Geol. Environ., 65, 167–184, https://doi.org/10.1007/s10064-005-0023-0, 2006. a
Vilajosana, I., Suriñach, E., Abellán, A., Khazaradze, G., Garcia, D., and Llosa, J.: Rockfall induced seismic signals: case study in Montserrat, Catalonia, Nat. Hazards Earth Syst. Sci., 8, 805–812, https://doi.org/10.5194/nhess-8-805-2008, 2008.
a
Walter, F., Burtin, A., McArdell, B. W., Hovius, N., Weder, B., and Turowski,
J. M.: Testing seismic amplitude source location for fast debris-flow detection at Illgraben, Switzerland, Nat. Hazards Earth Syst. Sci., 17, 939–955, https://doi.org/10.5194/nhess-17-939-2017, 2017. a
Walter, F., Amann, F., Kos, A., Kenner, R., Phillips, M., de Preux, A., Huss,
M., Tognacca, C., Clinton, J., Diehl, T., and Bonanomi, Y.: Direct observations of a three million cubic meter rock-slope collapse with almost
immediate initiation of ensuing debris flows, Geomorphology, 351, 106933,
https://doi.org/10.1016/j.geomorph.2019.106933, 2020. a, b
Wenner, M., Walter, F., McArdell, B., and Farinotti, D.: Deciphering
debris-flow seismograms at Illgraben, Switzerland, in: Debris-Flow Hazards
Mitigation: Mechanics, Monitoring, Modeling, and Assessment – Proceedings of
the 7th International Conference on Debris-Flow Hazards Mitigation, Colorado School of Mines, 10–13 June 2019, Golden, Colorado, 222–229, https://doi.org/10.25676/11124/173215, 2019. a
Wenner, M.: Automatic_classification_Bondo, available at:
https://github.com/michaelawenner/Automatic_classification_Bondo, last access: 21 January 2021. a
Yuan, B., Tan, Y. J., Mudunuru, M. K., Marcillo, O. E., Delorey, A. A., Roberts, P. M., Webster, J. D., Gammans, C. N. L., Karra, S., Guthrie, G. D., and Johnson, P. A.: Using machine learning to discern eruption in noisy environments: A case study using CO2-driven cold-water geyser in Chimayó, New Mexico, Seismol. Res. Lett., 90, 591–603, https://doi.org/10.1785/0220180306, 2019. a
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Mass movements constitute a risk to property and human life. In this study we use machine...
Altmetrics
Final-revised paper
Preprint