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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The modern picture of the snow slab avalanche release process involves a
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Antoine Guillemot, Laurent Baillet, Stéphane Garambois, Xavier Bodin, Agnès Helmstetter, Raphaël Mayoraz, and Eric Larose
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Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
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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.
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
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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
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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.
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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...
