Articles | Volume 6, issue 2
https://doi.org/10.5194/esurf-6-525-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/esurf-6-525-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jun 2018
Research article |
| 27 Jun 2018
| 27 Jun 2018
Characterizing the complexity of microseismic signals at slow-moving clay-rich debris slides: the Super-Sauze (southeastern France) and Pechgraben (Upper Austria) case studies
Naomi Vouillamoz
CORRESPONDING AUTHOR
Institute for Geophysics, University of Stuttgart, 70174 Stuttgart,
Germany
Sabrina Rothmund
Institute for Geophysics, University of Stuttgart, 70174 Stuttgart,
Germany
Manfred Joswig
Institute for Geophysics, University of Stuttgart, 70174 Stuttgart,
Germany
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Cited
23 citations as recorded by crossref.
- Towards a standard typology of endogenous landslide seismic sources F. Provost et al. 10.5194/esurf-6-1059-2018
- Landslide monitoring using seismic ambient noise correlation: challenges and applications M. Le Breton et al. 10.1016/j.earscirev.2021.103518
- Review on the Geophysical and UAV-Based Methods Applied to Landslides Y. Hussain et al. 10.3390/rs14184564
- Monitoring of Sobradinho landslide (Brasília, Brazil) and a prototype vertical slope by time-lapse interferometry Y. Hussain et al. 10.1590/2317-4889201920180085
- Short Communication: Monitoring rockfalls with the Raspberry Shake A. Manconi et al. 10.5194/esurf-6-1219-2018
- A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model J. Jiang et al. 10.1016/j.srs.2024.100189
- Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings J. Li et al. 10.3390/s23010243
- Variation in Rayleigh wave ellipticity as a possible indicator of earthflow mobility: a case study of Sobradinho landslide compared with pile load testing Y. Hussain et al. 10.15446/esrj.v24n2.81974
- Seismic constraints on rock damaging related to a failing mountain peak: the Hochvogel, Allgäu M. Dietze et al. 10.1002/esp.5034
- Ensemble and Self-supervised Learning for Improved Classification of Seismic Signals from the Åknes Rockslope D. Lee et al. 10.1007/s11004-022-10037-7
- Locating Rockfalls Using Inter‐Station Ratios of Seismic Energy at Dolomieu Crater, Piton de la Fournaise Volcano J. Kuehnert et al. 10.1029/2020JF005715
- Multiple Geophysical Techniques for Investigation and Monitoring of Sobradinho Landslide, Brazil Y. Hussain et al. 10.3390/su11236672
- Seasonal Slow Landslide Displacement Is Accommodated by mm‐Scale Stick‐Slip Events N. Finnegan et al. 10.1029/2022GL099548
- Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies J. Li et al. 10.1109/TGRS.2020.3032664
- [Retracted] Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment S. Wang et al. 10.1155/2022/5479007
- 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
- Vibration signal characteristics of debris flow initiation and motion experiments by considering clay content Z. Li et al. 10.3389/feart.2025.1519353
- Typological analysis of slidequakes emitted from landslides: experiments on an expander body pile and Sobradinho landslide (Brasilia, Brazil) Y. Hussain et al. 10.1590/0370-44672018720110
- Characterisation of precursory seismic activity towards early warning of landslides via semi-supervised learning D. Murray et al. 10.1038/s41598-024-84067-y
- Microseismic monitoring illuminates phases of slope failure in soft soils G. Yfantis et al. 10.1016/j.enggeo.2020.105940
- A Review of the Advantages and Limitations of Geophysical Investigations in Landslide Studies V. Pazzi et al. 10.1155/2019/2983087
- Determination of the seismic signatures of landslides in soft soils: A methodology based on a field scale shear box G. Yfantis et al. 10.1016/j.enggeo.2020.105853
- Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath A. Schöpa et al. 10.5194/esurf-6-467-2018
22 citations as recorded by crossref.
- Towards a standard typology of endogenous landslide seismic sources F. Provost et al. 10.5194/esurf-6-1059-2018
- Landslide monitoring using seismic ambient noise correlation: challenges and applications M. Le Breton et al. 10.1016/j.earscirev.2021.103518
- Review on the Geophysical and UAV-Based Methods Applied to Landslides Y. Hussain et al. 10.3390/rs14184564
- Monitoring of Sobradinho landslide (Brasília, Brazil) and a prototype vertical slope by time-lapse interferometry Y. Hussain et al. 10.1590/2317-4889201920180085
- Short Communication: Monitoring rockfalls with the Raspberry Shake A. Manconi et al. 10.5194/esurf-6-1219-2018
- A human-on-the-loop approach for labelling seismic recordings from landslide site via a multi-class deep-learning based classification model J. Jiang et al. 10.1016/j.srs.2024.100189
- Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings J. Li et al. 10.3390/s23010243
- Variation in Rayleigh wave ellipticity as a possible indicator of earthflow mobility: a case study of Sobradinho landslide compared with pile load testing Y. Hussain et al. 10.15446/esrj.v24n2.81974
- Seismic constraints on rock damaging related to a failing mountain peak: the Hochvogel, Allgäu M. Dietze et al. 10.1002/esp.5034
- Ensemble and Self-supervised Learning for Improved Classification of Seismic Signals from the Åknes Rockslope D. Lee et al. 10.1007/s11004-022-10037-7
- Locating Rockfalls Using Inter‐Station Ratios of Seismic Energy at Dolomieu Crater, Piton de la Fournaise Volcano J. Kuehnert et al. 10.1029/2020JF005715
- Multiple Geophysical Techniques for Investigation and Monitoring of Sobradinho Landslide, Brazil Y. Hussain et al. 10.3390/su11236672
- Seasonal Slow Landslide Displacement Is Accommodated by mm‐Scale Stick‐Slip Events N. Finnegan et al. 10.1029/2022GL099548
- Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies J. Li et al. 10.1109/TGRS.2020.3032664
- [Retracted] Research on Microseismic Source Location Method Based on Waveform Characteristics Monitored by Nanomaterial Sensor under the Background of Metal Oxide Polluted Environment S. Wang et al. 10.1155/2022/5479007
- 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
- Vibration signal characteristics of debris flow initiation and motion experiments by considering clay content Z. Li et al. 10.3389/feart.2025.1519353
- Typological analysis of slidequakes emitted from landslides: experiments on an expander body pile and Sobradinho landslide (Brasilia, Brazil) Y. Hussain et al. 10.1590/0370-44672018720110
- Characterisation of precursory seismic activity towards early warning of landslides via semi-supervised learning D. Murray et al. 10.1038/s41598-024-84067-y
- Microseismic monitoring illuminates phases of slope failure in soft soils G. Yfantis et al. 10.1016/j.enggeo.2020.105940
- A Review of the Advantages and Limitations of Geophysical Investigations in Landslide Studies V. Pazzi et al. 10.1155/2019/2983087
- Determination of the seismic signatures of landslides in soft soils: A methodology based on a field scale shear box G. Yfantis et al. 10.1016/j.enggeo.2020.105853
Latest update: 04 Feb 2025
Short summary
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.
Seismic monitoring of active landslides enables the detection of microseismic signals generated...
Special issue