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
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- Cluster Analysis of Slope Hazard Seismic Recordings Based Upon Unsupervised Deep Embedded Clustering C. Wang et al. 10.1785/0220230011
- Analysis of Thermally Induced Strain Effects on a Jointed Rock Mass through Microseismic Monitoring at the Acuto Field Laboratory (Italy) G. Grechi et al. 10.3390/app13042489
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- Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate C. Hibert et al. 10.5194/esurf-12-641-2024
- Cyborg insect repeatable self-righting locomotion assistance using bio-inspired 3D printed artificial limb M. Montagut Marques et al. 10.1038/s44182-024-00009-w
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- A Clustering-Classification Approach in Categorizing Vulnerability of Roads and Bridges Using Public Assistance Big Data A. Bhattacharyya et al. 10.2139/ssrn.4165442
- Seismic Advances in Process Geomorphology K. Cook & M. Dietze 10.1146/annurev-earth-032320-085133
- Intelligent detection of underground openings and surrounding disturbed zones W. Meng et al. 10.1016/j.tust.2024.106122
- Characterizing the evolution of mass flow properties and dynamics through analysis of seismic signals: insights from the 18 March 2007 Mt. Ruapehu lake-breakout lahar B. Walsh et al. 10.5194/nhess-23-1029-2023
- Machine Learning Improves Debris Flow Warning M. Chmiel et al. 10.1029/2020GL090874
30 citations as recorded by crossref.
- Rockfall alarm system for railway monitoring: Integrating seismic detection, localization, and characterization T. Rebert et al. 10.1190/geo2023-0058.1
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- Analysis of Thermally Induced Strain Effects on a Jointed Rock Mass through Microseismic Monitoring at the Acuto Field Laboratory (Italy) G. Grechi et al. 10.3390/app13042489
- Rock avalanche-induced air blasts: Implications for landslide risk assessments Y. Zhuang et al. 10.1016/j.geomorph.2024.109111
- Event recognition in marine seismological data using Random Forest machine learning classifier P. Domel et al. 10.1093/gji/ggad244
- Seismometer Records of Ground Tilt Induced by Debris Flows M. Wenner et al. 10.1785/0120210271
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- RockNet: Rockfall and Earthquake Detection and Association via Multitask Learning and Transfer Learning W. Liao et al. 10.1109/TGRS.2023.3284008
- Automated classification of seismic signals recorded on the Åknes rock slope, Western Norway, using a convolutional neural network N. Langet & F. Silverberg 10.5194/esurf-11-89-2023
- Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring B. Xin et al. 10.3390/s24154892
- Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment J. Hirschberg et al. 10.5194/nhess-21-2773-2021
- A clustering-classification approach in categorizing vulnerability of roads and bridges using public assistance big data A. Bhattacharyya et al. 10.1016/j.ijdrr.2022.103448
- Machine Learning and Imbalanced Learning Approaches in Condition-Based Monitoring and Predictive Maintenance: A Systematic Literature Review A. Mutemi & F. Bacao 10.2139/ssrn.3980484
- Near-real-time detection of co-seismic ionospheric disturbances using machine learning Q. Brissaud & E. Astafyeva 10.1093/gji/ggac167
- Discrimination between icequakes and earthquakes in southern Alaska: an exploration of waveform features using Random Forest algorithm A. Kharita et al. 10.1093/gji/ggae106
- Rapid resilience assessment framework for mountain tunnels subjected to near-fault seismic ground motions S. Meng et al. 10.1016/j.soildyn.2024.108746
- How water, temperature, and seismicity control the preconditioning of massive rock slope failure (Hochvogel) J. Leinauer et al. 10.5194/esurf-12-1027-2024
- Real-Time Classification of Anthropogenic Seismic Sources from Distributed Acoustic Sensing Data: Application for Pipeline Monitoring C. Huynh et al. 10.1785/0220220078
- Domain Knowledge Informed Multitask Learning for Landslide-Induced Seismic Classification J. Li et al. 10.1109/LGRS.2023.3279068
- Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate C. Hibert et al. 10.5194/esurf-12-641-2024
- Cyborg insect repeatable self-righting locomotion assistance using bio-inspired 3D printed artificial limb M. Montagut Marques et al. 10.1038/s44182-024-00009-w
- Debris flows at Illgraben, Switzerland – From seismic wiggles to machine learning F. Walter et al. 10.1002/geot.202200039
- Machine learning prediction of groundwater heights from passive seismic wavefield A. Abi Nader et al. 10.1093/gji/ggad160
- A Clustering-Classification Approach in Categorizing Vulnerability of Roads and Bridges Using Public Assistance Big Data A. Bhattacharyya et al. 10.2139/ssrn.4165442
- Seismic Advances in Process Geomorphology K. Cook & M. Dietze 10.1146/annurev-earth-032320-085133
- Intelligent detection of underground openings and surrounding disturbed zones W. Meng et al. 10.1016/j.tust.2024.106122
- Characterizing the evolution of mass flow properties and dynamics through analysis of seismic signals: insights from the 18 March 2007 Mt. Ruapehu lake-breakout lahar B. Walsh et al. 10.5194/nhess-23-1029-2023
1 citations as recorded by crossref.
Latest update: 12 Nov 2024
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...
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