Articles | Volume 18, issue 1
https://doi.org/10.5194/nhess-18-383-2018
© Author(s) 2018. 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-18-383-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2018
Research article |
| 25 Jan 2018
| 25 Jan 2018
Automatic detection of snow avalanches in continuous seismic data using hidden Markov models
Matthias Heck
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Conny Hammer
Swiss Seismological Service (SED), ETH Zurich, Zurich, Switzerland
Alec van Herwijnen
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Jürg Schweizer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Donat Fäh
Swiss Seismological Service (SED), ETH Zurich, Zurich, Switzerland
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Cited
22 citations as recorded by crossref.
- Facilitating adoption of AI in natural disaster management through collaboration M. Kuglitsch et al. 10.1038/s41467-022-29285-6
- Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain F. Walter et al. 10.1038/s41467-020-15824-6
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. 10.3390/atmos15111343
- Snow avalanche susceptibility along Mughal Road, North-western Himalaya using geospatial techniques I. Bhat et al. 10.1007/s12517-023-11839-7
- Seismic Advances in Process Geomorphology K. Cook & M. Dietze 10.1146/annurev-earth-032320-085133
- Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 2: Unsupervised learning for source process characterization R. Latto et al. 10.5194/tc-18-2081-2024
- Fluvial seismology: A seismological approach to advancing the continuous dynamic monitoring of river hydrology J. Yang et al. 10.1360/TB-2024-0099
- Automatic detection of avalanches combining array classification and localization M. Heck et al. 10.5194/esurf-7-491-2019
- Near-real-time automated classification of seismic signals of slope failures with continuous random forests M. Wenner et al. 10.5194/nhess-21-339-2021
- A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya P. Kaur et al. 10.1007/s11069-021-04958-5
- Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping A. Mosavi et al. 10.1109/ACCESS.2020.3014816
- Debris flows at Illgraben, Switzerland – From seismic wiggles to machine learning F. Walter et al. 10.1002/geot.202200039
- Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing P. Paitz et al. 10.1029/2022JF007011
- Mass wasting susceptibility assessment of snow avalanches using machine learning models B. Choubin et al. 10.1038/s41598-020-75476-w
- An end-to-end DNN-HMM based system with duration modeling for robust earthquake detection C. Murúa et al. 10.1016/j.cageo.2023.105434
- Joint detection and classification of rockfalls in a microseismic monitoring network L. Feng et al. 10.1093/gji/ggaa287
- Seismic location and tracking of snow avalanches and slush flows on Mt. Fuji, Japan C. Pérez-Guillén et al. 10.5194/esurf-7-989-2019
- Estimation of Avalanche Development and Frontal Velocities Based on the Spectrogram of the Seismic Signals Generated at the Vallée de la Sionne Test Site E. Suriñach et al. 10.3390/geosciences10030113
- Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway M. Eckerstorfer et al. 10.3390/rs11232863
- Evaluating the performance of an operational infrasound avalanche detection system at three locations in the Swiss Alps during two winter seasons S. Mayer et al. 10.1016/j.coldregions.2019.102962
- Applications of artificial intelligence for disaster management W. Sun et al. 10.1007/s11069-020-04124-3
- On recent advances in avalanche research J. Schweizer 10.1016/j.coldregions.2017.10.014
21 citations as recorded by crossref.
- Facilitating adoption of AI in natural disaster management through collaboration M. Kuglitsch et al. 10.1038/s41467-022-29285-6
- Distributed acoustic sensing of microseismic sources and wave propagation in glaciated terrain F. Walter et al. 10.1038/s41467-020-15824-6
- Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems N. Denissova et al. 10.3390/atmos15111343
- Snow avalanche susceptibility along Mughal Road, North-western Himalaya using geospatial techniques I. Bhat et al. 10.1007/s12517-023-11839-7
- Seismic Advances in Process Geomorphology K. Cook & M. Dietze 10.1146/annurev-earth-032320-085133
- Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 2: Unsupervised learning for source process characterization R. Latto et al. 10.5194/tc-18-2081-2024
- Fluvial seismology: A seismological approach to advancing the continuous dynamic monitoring of river hydrology J. Yang et al. 10.1360/TB-2024-0099
- Automatic detection of avalanches combining array classification and localization M. Heck et al. 10.5194/esurf-7-491-2019
- Near-real-time automated classification of seismic signals of slope failures with continuous random forests M. Wenner et al. 10.5194/nhess-21-339-2021
- A multi-model decision support system (MM-DSS) for avalanche hazard prediction over North-West Himalaya P. Kaur et al. 10.1007/s11069-021-04958-5
- Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping A. Mosavi et al. 10.1109/ACCESS.2020.3014816
- Debris flows at Illgraben, Switzerland – From seismic wiggles to machine learning F. Walter et al. 10.1002/geot.202200039
- Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing P. Paitz et al. 10.1029/2022JF007011
- Mass wasting susceptibility assessment of snow avalanches using machine learning models B. Choubin et al. 10.1038/s41598-020-75476-w
- An end-to-end DNN-HMM based system with duration modeling for robust earthquake detection C. Murúa et al. 10.1016/j.cageo.2023.105434
- Joint detection and classification of rockfalls in a microseismic monitoring network L. Feng et al. 10.1093/gji/ggaa287
- Seismic location and tracking of snow avalanches and slush flows on Mt. Fuji, Japan C. Pérez-Guillén et al. 10.5194/esurf-7-989-2019
- Estimation of Avalanche Development and Frontal Velocities Based on the Spectrogram of the Seismic Signals Generated at the Vallée de la Sionne Test Site E. Suriñach et al. 10.3390/geosciences10030113
- Near-Real Time Automatic Snow Avalanche Activity Monitoring System Using Sentinel-1 SAR Data in Norway M. Eckerstorfer et al. 10.3390/rs11232863
- Evaluating the performance of an operational infrasound avalanche detection system at three locations in the Swiss Alps during two winter seasons S. Mayer et al. 10.1016/j.coldregions.2019.102962
- Applications of artificial intelligence for disaster management W. Sun et al. 10.1007/s11069-020-04124-3
1 citations as recorded by crossref.
Latest update: 30 Dec 2024
Short summary
In this study we use hidden Markov models, a machine learning algorithm to automatically identify avalanche events in a continuous seismic data set recorded during the winter 2010. With additional post processing steps, we detected around 70 avalanche events. Although not every detection could be confirmed as an avalanche, we clearly identified the two main avalanche periods of the winter season 2010 in our classification results.
In this study we use hidden Markov models, a machine learning algorithm to automatically...
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