Articles | Volume 5, issue 2
https://doi.org/10.5194/esurf-5-283-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/esurf-5-283-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 May 2017
Research article |
| 23 May 2017
Single-block rockfall dynamics inferred from seismic signal analysis
Institut de Physique du Globe de Strasbourg, CNRS UMR 7516, University of Strasbourg/EOST, 5 rue Descartes, 67084 Strasbourg, France
Jean-Philippe Malet
Institut de Physique du Globe de Strasbourg, CNRS UMR 7516, University of Strasbourg/EOST, 5 rue Descartes, 67084 Strasbourg, France
Franck Bourrier
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), 2 Rue de la Papeterie,
38402 Saint-Martin-d'Hères, France
Floriane Provost
Institut de Physique du Globe de Strasbourg, CNRS UMR 7516, University of Strasbourg/EOST, 5 rue Descartes, 67084 Strasbourg, France
Frédéric Berger
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), 2 Rue de la Papeterie,
38402 Saint-Martin-d'Hères, France
Pierrick Bornemann
Institut de Physique du Globe de Strasbourg, CNRS UMR 7516, University of Strasbourg/EOST, 5 rue Descartes, 67084 Strasbourg, France
Pascal Tardif
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), 2 Rue de la Papeterie,
38402 Saint-Martin-d'Hères, France
Eric Mermin
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), 2 Rue de la Papeterie,
38402 Saint-Martin-d'Hères, France
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Clément Hibert, François Noël, David Toe, Miloud Talib, Mathilde Desrues, Emmanuel Wyser, Ombeline Brenguier, Franck Bourrier, Renaud Toussaint, Jean-Philippe Malet, and Michel Jaboyedoff
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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.
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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.
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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.
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Publication in NHESS not foreseen
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Riley Finnegan, Jeffrey R. Moore, and Paul R. Geimer
Earth Surf. Dynam., 9, 1459–1479, https://doi.org/10.5194/esurf-9-1459-2021, https://doi.org/10.5194/esurf-9-1459-2021, 2021
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Mauro Häusler, Paul Richmond Geimer, Riley Finnegan, Donat Fäh, and Jeffrey Ralston Moore
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David J. Purnell, Natalya Gomez, William Minarik, David Porter, and Gregory Langston
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Jui-Ming Chang, Wei-An Chao, Hongey Chen, Yu-Ting Kuo, and Che-Ming Yang
Earth Surf. Dynam., 9, 505–517, https://doi.org/10.5194/esurf-9-505-2021, https://doi.org/10.5194/esurf-9-505-2021, 2021
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Large avalanches composed of ice and rock are a serious hazard to mountain communities and backcountry travellers. These processes shake the Earth and disturb the atmosphere, generating seismic waves and sound waves which can travel for hundreds of kilometers. In this study, we use the seismic waves and sound waves produced by two massive avalanches on a volcano in Alaska to reconstruct how the avalanches failed. Our method may assist with rapid emergency response to these global hazards.
Emanuele Marchetti, Alec van Herwijnen, Marc Christen, Maria Cristina Silengo, and Giulia Barfucci
Earth Surf. Dynam., 8, 399–411, https://doi.org/10.5194/esurf-8-399-2020, https://doi.org/10.5194/esurf-8-399-2020, 2020
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We present infrasonic and seismic array data of a powder snow avalanche, that was released on 5 February 2016, in the Dischma valley nearby Davos, Switzerland. Combining information derived from both arrays, we show how infrasound and seismic energy are radiated from different sources acting along the path. Moreover, infrasound transmits to the ground and affects the recorded seismic signal. Results highlight the benefits of combined seismo-acoustic array analyses for monitoring and research.
Tómas Jóhannesson, Jón Kristinn Helgason, and Sigríður Sif Gylfadóttir
Earth Surf. Dynam., 8, 173–175, https://doi.org/10.5194/esurf-8-173-2020, https://doi.org/10.5194/esurf-8-173-2020, 2020
Cristina Pérez-Guillén, Kae Tsunematsu, Kouichi Nishimura, and Dieter Issler
Earth Surf. Dynam., 7, 989–1007, https://doi.org/10.5194/esurf-7-989-2019, https://doi.org/10.5194/esurf-7-989-2019, 2019
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Avalanches and slush flows from Mt. Fuji are a major natural hazard as they may attain run-out distances of up to 4 km and destroy parts of the forest and infrastructure. We located and tracked them for the first time using seismic data. Numerical simulations were conducted to assess the precision of the seismic tracking. We also inferred dynamical properties characterizing these hazardous mass movements. This information is indispensable for assessing avalanche risk in the Mt. Fuji region.
Thomas Geay, Ludovic Michel, Sébastien Zanker, and James Robert Rigby
Earth Surf. Dynam., 7, 537–548, https://doi.org/10.5194/esurf-7-537-2019, https://doi.org/10.5194/esurf-7-537-2019, 2019
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This research has been conducted to develop the use of passive acoustic monitoring (PAM) for bedload monitoring in rivers. Monitored bedload acoustic signals depend on bedload characteristics (e.g., grain size distribution, fluxes) but are also affected by the environment in which the acoustic waves are propagated. This study focuses on the determination of propagation effects in rivers. An experimental approach has been conducted in several streams to estimate acoustic propagation laws.
Matthias Heck, Alec van Herwijnen, Conny Hammer, Manuel Hobiger, Jürg Schweizer, and Donat Fäh
Earth Surf. Dynam., 7, 491–503, https://doi.org/10.5194/esurf-7-491-2019, https://doi.org/10.5194/esurf-7-491-2019, 2019
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We used continuous seismic data from two small aperture geophone arrays deployed in the region above Davos in the eastern Swiss Alps to develop a machine learning workflow to automatically identify signals generated by snow avalanches. Our results suggest that the method presented could be used to identify major avalanche periods and highlight the importance of array processing techniques for the automatic classification of avalanches in seismic data.
Andreas Köhler and Christian Weidle
Earth Surf. Dynam., 7, 1–16, https://doi.org/10.5194/esurf-7-1-2019, https://doi.org/10.5194/esurf-7-1-2019, 2019
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The uppermost part of permanently frozen ground can thaw during summer and refreeze during winter. We use a method based on naturally generated seismic waves to continuously monitor these changes close to the research settlement of Ny-Ålesund in Svalbard between April and August 2016. Our results reveal some potential pitfalls when interpreting temporal variations in the data. However, we show that a careful data analysis makes this method a very useful tool for long-term permafrost monitoring.
Andrea Manconi, Velio Coviello, Maud Galletti, and Reto Seifert
Earth Surf. Dynam., 6, 1219–1227, https://doi.org/10.5194/esurf-6-1219-2018, https://doi.org/10.5194/esurf-6-1219-2018, 2018
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We evaluated the performance of the low-cost seismic Raspberry Shake (RS) sensors to identify and monitor rockfall activity in alpine environments. The sensors have been tested for a 1-year period in a high alpine environment, recording numerous rock failure events as well as local and distant earthquakes. This study demonstrates that the RS instruments provide a good option to build low seismic monitoring networks to monitor different kinds of geophysical phenomena.
Floriane Provost, Jean-Philippe Malet, Clément Hibert, Agnès Helmstetter, Mathilde Radiguet, David Amitrano, Nadège Langet, Eric Larose, Clàudia Abancó, Marcel Hürlimann, Thomas Lebourg, Clara Levy, Gaëlle Le Roy, Patrice Ulrich, Maurin Vidal, and Benjamin Vial
Earth Surf. Dynam., 6, 1059–1088, https://doi.org/10.5194/esurf-6-1059-2018, https://doi.org/10.5194/esurf-6-1059-2018, 2018
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Seismic sources generated by the deformation of unstable slopes are diverse in terms of signal properties and mechanisms. Standardized catalogues of landslide endogenous seismicity can help understanding the physical processes controlling slope dynamics. We propose a generic typology of seismic sources based on the analysis of signals recorded at various instrumented slopes. We demonstrate that the seismic signals present similar features at different sites and discuss their mechanical sources.
Florian Fuchs, Wolfgang Lenhardt, Götz Bokelmann, and the AlpArray Working Group
Earth Surf. Dynam., 6, 955–970, https://doi.org/10.5194/esurf-6-955-2018, https://doi.org/10.5194/esurf-6-955-2018, 2018
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The work demonstrates how seismic networks installed in the Alps can be used for country-wide real-time monitoring of rockslide activity. We suggest simple methods that allow us to detect, locate, and characterize rockslides using the seismic signals they generate. We developed an automatic procedure to locate rockslides with kilometer accuracy over hundreds of kilometers of distance. Our findings highlight how seismic networks can help us to understand the triggering of rockslides.
Naomi Vouillamoz, Sabrina Rothmund, and Manfred Joswig
Earth Surf. Dynam., 6, 525–550, https://doi.org/10.5194/esurf-6-525-2018, https://doi.org/10.5194/esurf-6-525-2018, 2018
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Seismic monitoring of active landslides enables the detection of microseismic signals generated by slope activity. We propose a classification of
microseismic signals observed at two active clay-rich debris slides and a simple method to constrain their source origin and their size
based on their signal amplitudes. A better understanding of landslide-induced microseismicity is crucial for the development of early warning systems
based on landslide-induced microseismic signal precursors.
Pippa L. Whitehouse
Earth Surf. Dynam., 6, 401–429, https://doi.org/10.5194/esurf-6-401-2018, https://doi.org/10.5194/esurf-6-401-2018, 2018
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This article is a contribution to a special issue on
Two centuries of modelling across scales. It describes the historical observations, evolving hypotheses, and early calculations that led to the development of the field of glacial isostatic sdjustment (GIA) modelling, which seeks to understand feedbacks between ice-sheet change, sea-level change, and solid Earth deformation. Recent and future advances are discussed. Future progress will likely involve an interdisciplinary approach.
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