Articles | Volume 10, issue 2
https://doi.org/10.5194/se-10-463-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-10-463-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Apr 2019
Research article |
| 04 Apr 2019
| 04 Apr 2019
Effects of finite source rupture on landslide triggering: the 2016 Mw 7.1 Kumamoto earthquake
Sebastian von Specht
CORRESPONDING AUTHOR
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
University of Potsdam, Institute of Environmental Science and Geography, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
Ugur Ozturk
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
University of Potsdam, Institute of Environmental Science and Geography, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
Potsdam Institute for Climate Impact Research (PIK) e.V., Telegrafenberg, 14473 Potsdam, Germany
Georg Veh
University of Potsdam, Institute of Environmental Science and Geography, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
Fabrice Cotton
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
Oliver Korup
University of Potsdam, Institute of Geosciences, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
University of Potsdam, Institute of Environmental Science and Geography, Karl-Liebknecht-Str. 24–25, 14476 Potsdam-Golm, Germany
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Cited
20 citations as recorded by crossref.
- Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting? U. Ozturk et al. 10.1007/s10346-021-01689-3
- Geohazards explained 10 U. Ozturk 10.1111/gto.12391
- Accelerating low-frequency ground motion simulation for finite fault sources using neural networks L. Lehmann et al. 10.1093/gji/ggad239
- Statistical analysis of the landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) using an automatic linear regression (LINEAR) and artificial neural network (ANN) model based on controlling parameters A. Vanani et al. 10.1007/s11069-023-06240-2
- Participatory Landslide Inventory (PLI): An Online Tool for the Development of a Landslide Inventory E. Perera et al. 10.1155/2022/2659203
- The influence of pulse-like ground motion on landslide triggering during the 2016 M<sub>w</sub> 7.1 Kumamoto earthquake Z. Li et al. 10.3208/jgssp.v10.OS-42-04
- Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
- Postglacial Patagonian mass movement: From rotational slides and spreads to earthflows E. Schönfeldt et al. 10.1016/j.geomorph.2020.107316
- Seismic Response of a Mountain Ridge Prone to Landsliding C. Rault et al. 10.1785/0120190127
- Earthquake-induced debris flows at Popocatépetl Volcano, Mexico V. Coviello et al. 10.5194/esurf-9-393-2021
- Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling J. Jones et al. 10.1029/2021JF006067
- The influence of pulse-like ground motion caused by the directivity effect on landslide triggering Z. Li et al. 10.1007/s10064-023-03514-8
- Applying Conservation of Energy to Estimate Earthquake Frequencies from Strain Rates and Stresses M. Ziebarth et al. 10.1029/2020JB020186
- Landslide Geometry Reveals its Trigger K. Rana et al. 10.1029/2020GL090848
- Landslides induced by the 2017 Mw7.3 Sarpol Zahab earthquake (Iran) A. Cheaib et al. 10.1007/s10346-021-01832-0
- Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction A. Dahal et al. 10.1038/s43247-024-01243-8
- Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations C. Song et al. 10.1038/s41467-022-35035-5
- Correlation of the state of crustal stresses, seismicity and landslide activity (Fergana basin, Tien Shan) Z. Kalmet’eva et al. 10.5800/GT-2019-10-4-0454
- Rockfall Activity Rates Before, During and After the 2010/2011 Canterbury Earthquake Sequence C. Massey et al. 10.1029/2021JF006400
- Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be Z. Fang et al. 10.1016/j.jag.2023.103631
20 citations as recorded by crossref.
- Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting? U. Ozturk et al. 10.1007/s10346-021-01689-3
- Geohazards explained 10 U. Ozturk 10.1111/gto.12391
- Accelerating low-frequency ground motion simulation for finite fault sources using neural networks L. Lehmann et al. 10.1093/gji/ggad239
- Statistical analysis of the landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) using an automatic linear regression (LINEAR) and artificial neural network (ANN) model based on controlling parameters A. Vanani et al. 10.1007/s11069-023-06240-2
- Participatory Landslide Inventory (PLI): An Online Tool for the Development of a Landslide Inventory E. Perera et al. 10.1155/2022/2659203
- The influence of pulse-like ground motion on landslide triggering during the 2016 M<sub>w</sub> 7.1 Kumamoto earthquake Z. Li et al. 10.3208/jgssp.v10.OS-42-04
- Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. 10.5194/nhess-24-823-2024
- Postglacial Patagonian mass movement: From rotational slides and spreads to earthflows E. Schönfeldt et al. 10.1016/j.geomorph.2020.107316
- Seismic Response of a Mountain Ridge Prone to Landsliding C. Rault et al. 10.1785/0120190127
- Earthquake-induced debris flows at Popocatépetl Volcano, Mexico V. Coviello et al. 10.5194/esurf-9-393-2021
- Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling J. Jones et al. 10.1029/2021JF006067
- The influence of pulse-like ground motion caused by the directivity effect on landslide triggering Z. Li et al. 10.1007/s10064-023-03514-8
- Applying Conservation of Energy to Estimate Earthquake Frequencies from Strain Rates and Stresses M. Ziebarth et al. 10.1029/2020JB020186
- Landslide Geometry Reveals its Trigger K. Rana et al. 10.1029/2020GL090848
- Landslides induced by the 2017 Mw7.3 Sarpol Zahab earthquake (Iran) A. Cheaib et al. 10.1007/s10346-021-01832-0
- Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction A. Dahal et al. 10.1038/s43247-024-01243-8
- Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations C. Song et al. 10.1038/s41467-022-35035-5
- Correlation of the state of crustal stresses, seismicity and landslide activity (Fergana basin, Tien Shan) Z. Kalmet’eva et al. 10.5800/GT-2019-10-4-0454
- Rockfall Activity Rates Before, During and After the 2010/2011 Canterbury Earthquake Sequence C. Massey et al. 10.1029/2021JF006400
- Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be Z. Fang et al. 10.1016/j.jag.2023.103631
Discussed (final revised paper)
Discussed (preprint)
Latest update: 30 May 2025
Short summary
We show the landslide response to the 2016 Kumamoto earthquake (Mw 7.1) in central Kyushu (Japan). Landslides are concentrated to the northeast of the rupture, coinciding with the propagation direction of the earthquake. This azimuthal variation in the landslide concentration is linked to the seismic rupture process itself and not to classical landslide susceptibility factors. We propose a new ground-motion model that links the seismic radiation pattern with the landslide distribution.
We show the landslide response to the 2016 Kumamoto earthquake (Mw 7.1) in central Kyushu...
