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
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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21 citations as recorded by crossref.
- Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment J. Hirschberg et al. 10.5194/nhess-21-2773-2021
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- Assessment of environmental geological disaster susceptibility under a multimodel comparison to aid in the sustainable development of the regional economy C. Wang et al. 10.1007/s11356-022-22649-x
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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
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- 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
- Debris flows at Illgraben, Switzerland – From seismic wiggles to machine learning F. Walter et al. 10.1002/geot.202200039
- Microseismic Event Classification With Time-, Frequency-, and Wavelet-Domain Convolutional Neural Networks J. Jiang et al. 10.1109/TGRS.2023.3262412
- 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
- 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
- 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: 04 Oct 2023
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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