Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5805-2021
© 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/tc-15-5805-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Dec 2021
Research article |
| 23 Dec 2021
| 23 Dec 2021
Effect of snowfall on changes in relative seismic velocity measured by ambient noise correlation
Antoine Guillemot
CORRESPONDING AUTHOR
Laboratoire ISTerre, Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont Blanc, 38000 Grenoble, France
Alec van Herwijnen
Avalanche Formation Group, WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Eric Larose
Laboratoire ISTerre, Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont Blanc, 38000 Grenoble, France
Stephanie Mayer
Avalanche Formation Group, WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Laurent Baillet
Laboratoire ISTerre, Univ. Grenoble Alpes, CNRS, Univ. Savoie Mont Blanc, 38000 Grenoble, France
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EGUsphere, https://doi.org/10.5194/egusphere-2026-3388, https://doi.org/10.5194/egusphere-2026-3388, 2026
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Snow slab avalanches release when a crack propagates through a weak layer buried beneath a dense snow slab. In supershear avalanches, this crack outruns shear waves and creates a Mach-cone pattern, similar to a sonic boom. We show that snow layering bends this cone, offering new insight into fracture experiments and avalanche release.
Melin Walet, Jakob Schöttner, Valentin Adam, Florian Rheinschmidt, Philipp Rosendahl, Philipp Weißgraeber, Jürg Schweizer, and Alec van Herwijnen
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Dry snow avalanches start when a weak snow layer fails and a crack spreads through the snowpack, but field data is limited. We tested a weak layer of coarse snow grains in field fracture experiments. Using videos of snow movement, we derived the stiffness of the weak layer and calculated the resistance to crack growth. Crack resistance increased during the season as the grains in the weak layer grew and bonded more strongly. This data can be used to improve avalanche release models.
Jürg Schweizer, Benjamin Reuter, Christoph Mitterer, Fabiano Monti, and Stephanie Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2026-3913, https://doi.org/10.5194/egusphere-2026-3913, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Avalanche forecasting increasingly relies on numerical predictions of snow stability. We present the first comparison of traditional as well as AI-based stability metrics and validate them against avalanche observations. The metrics show a similar level of performance. Combining both process-based and data-driven metrics provides the most reliable forecasts.
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
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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.
Mohit Mishra, Gildas Besançon, Guillaume Chambon, and Laurent Baillet
Nat. Hazards Earth Syst. Sci., 26, 1621–1633, https://doi.org/10.5194/nhess-26-1621-2026, https://doi.org/10.5194/nhess-26-1621-2026, 2026
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This work was initiated in the context of a large interdisciplinary research project about Risk at Grenoble University, France. It relates to the challenging topic of landslide monitoring, and combines geotechnical sciences with techniques from control system engineering. Considering a specific modelling approach, the study provides a methodology towards estimation of some landslide parameters and their use in motion prediction. This could then be extended to the design of alert systems.
Simon Filhol, Clément Misset, Noélie Bontemps, Diego Cusicanqui, Emmanuel Paquet, Marie Dumont, Olivier Gagliardini, Pascal Lacroix, Simon Gascoin, Guillaume Thirel, Julien Brondex, Pascal Hagenmuller, Eric Larose, Philipp Schoeneich, Denis Roy, Emmanuel Thibert, Nicolas Eckert, Félix de Montety, Robin Mainieri, Alexandre Hauet, Frédéric Gottardi, Johan Berthet, Alexandre Baratier, Frédéric Liébault, Małgorzata Chmiel, Guillaume Piton, Guillaume Chambon, Guillaume James, Philippe Frey, Philip Deline, Laurent Astrade, Christian Vincent, Dominique Laigle, Alain Recking, Fatima Karbou, Adrien Mauss, Mylène Bonnefoy-Demongeot, Firmin Fontaine, Mickael Langlais, Etienne Berthier, and Antoine Blanc
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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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On June 21 2024, the village of La Bérarde, in the French Alps, was devastated by a flood destroying centuries old buildings. This study is an interdisciplinary work to decipher the causes and chronology of the event. The flood started with decadal rain falling on a thick snowpack. A lake observed on top of a glacier few days prior, had drained post event. With climate change, should we expect more similar compound events for alpine communities?
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.
Michael Lombardo, Amelie Fees, Anders Kaestner, Alec van Herwijnen, Jürg Schweizer, and Peter Lehmann
The Cryosphere, 19, 4437–4458, https://doi.org/10.5194/tc-19-4437-2025, https://doi.org/10.5194/tc-19-4437-2025, 2025
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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.
Frank Techel, Ross S. Purves, Stephanie Mayer, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 25, 3333–3353, https://doi.org/10.5194/nhess-25-3333-2025, https://doi.org/10.5194/nhess-25-3333-2025, 2025
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We tested how well fully data- and model-driven avalanche forecasts compare to human-made forecasts, which also integrate added context like field observations or model output. Using data from Switzerland over three winters, we found that models – even without this extra input – performed nearly as well. While human forecasts still have a slight edge, model predictions already offer reliable support for daily avalanche forecasting.
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
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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
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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.
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.
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
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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.
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
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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
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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
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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.
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
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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.
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
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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
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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
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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.
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
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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
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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.
Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, https://doi.org/10.5194/nhess-22-1911-2022, 2022
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Can the resolution of forecasts of avalanche danger be increased by using a combination of absolute and comparative judgments? Using 5 years of Swiss avalanche forecasts, we show that, on average, sub-levels assigned to a danger level reflect the expected increase in the number of locations with poor snow stability and in the number and size of avalanches with increasing forecast sub-level.
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
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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.
Cited articles
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss
avalanche warning: Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002.
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014.
Bensen, G. D., Ritzwoller, M. H., Barmin, M. P., Levshin, A. L., Lin, F.,
Moschetti, M. P., Shapiro, N. M., and Yang, Y.: Processing seismic ambient
noise data to obtain reliable broad-band surface wave dispersion
measurements, Geophys. J. Int., 169, 1239–1260,
https://doi.org/10.1111/j.1365-246X.2007.03374.x, 2007.
Berger, J.: A note on thermoelastic strains and tilts, J. Geophys. Res., 80, 274–277, https://doi.org/10.1029/JB080i002p00274, 1975.
Brenguier, F., Shapiro, N. M., Campillo, M., Ferrazzini, V., Duputel, Z.,
Coutant, O., and Nercessian, A.: Towards forecasting volcanic eruptions
using seismic noise, Nat. Geosci., 1, 126–130, https://doi.org/10.1038/ngeo104, 2008.
Campillo, M. and Paul, A.: Long-Range Correlations in the Diffuse Seismic
Coda, Science, 299, 547–549, https://doi.org/10.1126/science.1078551, 2003.
Capelli, A., Kapil, J. C., Reiweger, I., Or, D., and Schweizer, J.: Speed
and attenuation of acoustic waves in snow: Laboratory experiments and
modeling with Biot's theory, Cold Reg. Sci. Technol., 125, 1–11,
https://doi.org/10.1016/j.coldregions.2016.01.004, 2016.
Clements, T. and Denolle, M. A.: Tracking Groundwater Levels Using the
Ambient Seismic Field, Geophys. Res. Lett., 45, 6459–6465, https://doi.org/10.1029/2018GL077706, 2018.
Davies, J. H. and Davies, D. R.: Earth's surface heat flux, Solid Earth, 1, 5–24, https://doi.org/10.5194/se-1-5-2010, 2010.
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., Mcclung,
D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The
International Classification for Seasonal Snow on the Ground, UNESCO, IHP (International Hydrological Programme)–VII, Technical Documents in Hydrology, 83, 2009.
Gassenmeier, M., Sens-Schönfelder, C., Delatre, M., and Korn, M.:
Monitoring of environmental influences on seismic velocity at the geological
storage site for CO2 in Ketzin (Germany) with ambient seismic noise, Geophys. J. Int., 200, 524–533, https://doi.org/10.1093/gji/ggu413, 2015.
Gerling, B., Löwe, H., and van Herwijnen, A.: Measuring the Elastic
Modulus of Snow, Geophys. Res. Lett., 44, 11088–11096, https://doi.org/10.1002/2017GL075110, 2017.
Gradon, C., Brenguier, F., Stammeijer, J., Mordret, A., Hindriks, K.,
Campman, X., Lynch, R., Boué, P., and Chmiel, M.: Seismic Velocity
Response to Atmospheric Pressure Using Time-Lapse Passive Seismic
Interferometry, B. Seismol. Soc. Am., 111, 3451–3458, https://doi.org/10.1785/0120210069, 2021.
Grêt, A., Snieder, R., and Scales, J.: Time-lapse monitoring of rock
properties with coda wave interferometry: Time-lapse monitoring of rock
properties, J. Geophys. Res., 111, 148–227,
https://doi.org/10.1029/2004JB003354, 2006.
Guillemot, A., Helmstetter, A., Larose, É., Baillet, L., Garambois, S.,
Mayoraz, R., and Delaloye, R.: Seismic monitoring in the Gugla rock glacier
(Switzerland): ambient noise correlation, microseismicity and modelling,
Geophys. J. Int., 221, 1719–1735, https://doi.org/10.1093/gji/ggaa097, 2020.
Hadziioannou, C., Larose, E., Coutant, O., Roux, P., and Campillo, M.:
Stability of monitoring weak changes in multiply scattering media with
ambient noise correlation: Laboratory experiments, J. Acoust. Soc. Am., 125, 3688–3695, 2009.
Hadziioannou, C., Larose, E., Baig, A., Roux, P., and Campillo, M.:
Improving temporal resolution in ambient noise monitoring of seismic wave
speed, J. Geophys. Res.-Sol. Ea., 116, B07304 pp., https://doi.org/10.1029/2011JB008200, 2011.
Heck, M., Hobiger, M., van Herwijnen, A., Schweizer, J., and Fäh, D.:
Localization of seismic events produced by avalanches using multiple signal
classification, Geophys. J. Int., 216, 201–217,
https://doi.org/10.1093/gji/ggy394, 2018.
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.
Hillers, G., Campillo, M., and Ma, K.-F.: Seismic velocity variations at
TCDP are controlled by MJO driven precipitation pattern and high fluid
discharge properties, Earth Planet Sc. Lett., 391, 121–127,
https://doi.org/10.1016/j.epsl.2014.01.040, 2014.
Hillers, G., Ben-Zion, Y., Campillo, M., and Zigone, D.: Seasonal variations
of seismic velocities in the San Jacinto fault area observed with ambient
seismic noise, Geophys. J. Int., 202, 920–932,
https://doi.org/10.1093/gji/ggv151, 2015.
Hotovec-Ellis, A. J., Gomberg, J., Vidale, J. E., and Creager, K. C.: A
continuous record of intereruption velocity change at Mount St. Helens from
coda wave interferometry, J. Geophys. Res.-Sol. Ea., 119, 2199–2214,
https://doi.org/10.1002/2013JB010742, 2014.
James, S. R., Knox, H. A., Abbott, R. E., and Screaton, E. J.: Improved
moving window cross-spectral analysis for resolving large temporal seismic
velocity changes in permafrost, Geophys. Res. Lett., 44, 4018–4026,
https://doi.org/10.1002/2016GL072468, 2017.
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.
Le Breton, M.: Impulse
response from rainfall to displacement, chapter 8, in: Suivi temporel d'un glissement de terrain à l'aide d'étiquettes RFID passives, couplé à l'observation de pluviométrie et de bruit sismique ambiant, PhD Thesis, Université Grenoble Alpes, ISTerre, Grenoble, France, 204–227, 2019.
Le Breton, M., Bontemps, N., Guillemot, A., Baillet, L., and Larose, É.:
Landslide monitoring using seismic ambient noise correlation: challenges and
applications, Earth-Sci. Rev., 216, 103518,
https://doi.org/10.1016/j.earscirev.2021.103518, 2021.
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and
Zimmerli, M.: snowpack model calculations for avalanche warning based upon a
new network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157, https://doi.org/10.1016/S0165-232X(99)00022-1, 1999.
Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., and
Jaboyedoff, M.: Ambient seismic noise monitoring of a clay landslide: Toward
failure prediction, J. Geophys. Res., 117, F01030,
https://doi.org/10.1029/2011JF002159, 2012.
Meier, U., Shapiro, N. M., and Brenguier, F.: Detecting seasonal variations
in seismic velocities within Los Angeles basin from correlations of ambient
seismic noise, Geophys. J. Int., 181, 985–996,
https://doi.org/10.1111/j.1365-246X.2010.04550.x, 2010.
Miao, Y., Shi, Y., Zhuang, H. Y., Wang, S. Y., Liu, H. B., and Yu, X. B.:
Influence of Seasonal Frozen Soil on Near-Surface Shear Wave Velocity in
Eastern Hokkaido, Japan, Geophys. Res. Lett., 46, 9497–9508,
https://doi.org/10.1029/2019GL082282, 2019.
Mordret, A., Mikesell, T. D., Harig, C., Lipovsky, B. P., and Prieto, G. A.:
Monitoring southwest Greenland's ice sheet melt with ambient seismic noise, Science Adv., 2, e1501538, https://doi.org/10.1126/sciadv.1501538, 2016.
Moreau, L., Stehly, L., Boué, P., Lu, Y., Larose, E., and Campillo, M.:
Improving ambient noise correlation functions with an SVD-based Wiener
filter, Geophys. J. Int., 211, 418–426, https://doi.org/10.1093/gji/ggx306,
2017.
Obermann, A., Planès, T., Larose, E., Sens-Schönfelder, C., and
Campillo, M.: Depth sensitivity of seismic coda waves to velocity
perturbations in an elastic heterogeneous medium, Geophys. J. Int., 194,
372–382, https://doi.org/10.1093/gji/ggt043, 2013.
Planès, T., Rittgers, J. B., Mooney, M. A., Kanning, W., and Draganov,
D.: Monitoring the tidal response of a sea levee with ambient seismic noise,
J. Appl. Geophys., 138, 255–263, https://doi.org/10.1016/j.jappgeo.2017.01.025, 2017.
Richter, T., Sens-Schönfelder, C., Kind, R., and Asch, G.: Comprehensive
observation and modeling of earthquake and temperature-related seismic
velocity changes in northern Chile with passive image interferometry, J. Geophys. Res.-Sol. Ea., 119,
4747–4765, https://doi.org/10.1002/2013JB010695, 2014.
Rivet, D., Brenguier, F., and Cappa, F.: Improved detection of preeruptive
seismic velocity drops at the Piton de La Fournaise volcano, Geophys. Res.
Lett., 42, 2015GL064835, https://doi.org/10.1002/2015GL064835, 2015.
Sayers, C. M.: Porosity dependence of elastic moduli of snow and firn, J. Glaciol., 67, 1–9, https://doi.org/10.1017/jog.2021.25, 2021.
Schweizer, J. and Camponovo, C.: The temperature dependence of the effective
elastic shear modulus of snow, Cold Reg. Sci. Technol., 35, 55–64,
https://doi.org/10.1016/S0165-232x(02)00030-7, 2002.
Schweizer, J. and Jamieson, J. B.: Snowpack properties for snow profile
analysis, Cold Reg. Sci. Technol., 37, 233–241,
https://doi.org/10.1016/S0165-232X(03)00067-3, 2003.
Sens-Schönfelder, C. and Wegler, U.: Passive image interferometry and
seasonal variations of seismic velocities at Merapi Volcano, Indonesia,
Geophys. Res. Lett., 33, L21302, https://doi.org/10.1029/2006GL027797, 2006.
Sidler, R.: A porosity-based Biot model for acoustic waves in snow, J. Glaciol., 61, 789–798, https://doi.org/10.3189/2015JoG15J040, 2015.
Steinmann, R., Hadziioannou, C., and Larose, E.: Effect of centimetric
freezing of the near subsurface on Rayleigh and Love wave velocity in
ambient seismic noise correlations, Geophys. J. Int., 224, 626–636, https://doi.org/10.1093/gji/ggaa406, 2021.
Tarkov, A. P. and Vavakin, V. V.: Poisson's ratio behaviour in various
crystalline rocks: application to the study of the Earth's interior, Phys. Earth Planet. In., 29, 24–29, https://doi.org/10.1016/0031-9201(82)90134-0, 1982.
Taylor, A. and Blum, J. D.: Relation between soil age and silicate
weathering rates determined from the chemical evolution of a glacial
chronosequence, Geology, 23, 979–982,
https://doi.org/10.1130/0091-7613(1995)023<0979:RBSAAS>2.3.CO;2, 1995.
Tsai, V. C.: A model for seasonal changes in GPS positions and seismic wave
speeds due to thermoelastic and hydrologic variations, J. Geophys. Res.-Sol. Ea., 116, B04404, https://doi.org/10.1029/2010JB008156, 2011.
van Herwijnen, A. and Schweizer, J.: Seismic sensor array for monitoring an
avalanche start zone: design, deployment and preliminary results, J. Glaciol., 57, 267–276, https://doi.org/10.3189/002214311796405933, 2011.
van Herwijnen, A. and Miller, D. A.: Experimental and numerical investigation of the sintering rate of snow, J. Glaciol., 59, 269–274,
https://doi.org/10.3189/2013JoG12J094, 2013.
Voisin, C., Garambois, S., Massey, C., and Brossier, R.: Seismic noise
monitoring of the water table in a deep-seated, slow-moving landslide,
Interpretation, 4, SJ67–SJ76, https://doi.org/10.1190/INT-2016-0010.1,
2016.
Voisin, C., Guzmán, M. A. R., Réfloch, A., Taruselli, M., and
Garambois, S.: Groundwater Monitoring with Passive Seismic Interferometry, J. Water Res. Protect., 9, 1414, https://doi.org/10.4236/jwarp.2017.912091, 2017.
Wang, Q.-Y., Brenguier, F., Campillo, M., Lecointre, A., Takeda, T., and
Aoki, Y.: Seasonal Crustal Seismic Velocity Changes Throughout Japan,
J. Geophys. Res.-Sol. Ea., 122, 7987–8002, https://doi.org/10.1002/2017JB014307, 2017.
Wathelet, M.: Array recordings of ambient vibrations: surface-wave inversion,
PhD Thesis Université de Liège, 2005.
Wathelet, M., Jongmans, D., and Ohrnberger, M.: Surface-wave inversion using
a direct search algorithm and its application to ambient vibration
measurements, Near Surf. Geophys., 2, 211–221,
https://doi.org/10.3997/1873-0604.2004018, 2004.
Wever, N., Fierz, C., Mitterer, C., Hirashima, H., and Lehning, M.: Solving Richards Equation for snow improves snowpack meltwater runoff estimations in detailed multi-layer snowpack model, The Cryosphere, 8, 257–274, https://doi.org/10.5194/tc-8-257-2014, 2014.
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.
Ambient noise correlation is a broadly used method in seismology to monitor tiny changes in...
