| Citation: |
Jun Sun, Yu Zhuang, Ai-guo Xing, 2024. Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases, China Geology, 7, 263-275. doi: 10.31035/cg2023138
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Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases
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Abstract
Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance, high mobility and strong destructive power. Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters. This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events. Specifically, for the historical landslide cases, the landslide-induced seismic signal, geophysical surveys, and possible in-situ drone/phone videos (multi-source data collaboration) can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical (rheological) parameters. Subsequently, the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events. Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou, China gives reasonable results in comparison to the field observations. The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region (2019 Shuicheng landslide). The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.
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References
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Jun Sun, Yu Zhuang, Ai-guo Xing, 2024. Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases, China Geology, 7, 263-275. doi: 10.31035/cg2023138
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Figure 1.
Framework for the landslide runout prediction.
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Figure 2.
(a) Locations of the landslide disasters in Guizhou, China. (b) Overview of the Shuicheng landslide.
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Figure 3.
Dynamic and deposit characteristics of the Shuicheng landslide (after Zhang YB et al., 2020 and Zhuang Y et al., 2021). a–Seismic signal analysis results; b–e–evolution process of the Shuicheng landslide simulated by DAN3D; f–g–ERT results testing the modeled landslide deposits. In the ERT inversion images, white dashed lines represent the hypothetical boundary from ERT results, and red dashed lines represent DAN3D simulated results.
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Figure 4.
View of the Jianshanying landslide. a–Jianshanying mountain in Shuicheng, Guizhou, China; b–shows the landslide region and zone division; c–the potential landslide at the top of mountain.
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Figure 5.
Daily rainfall of the study area.
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Figure 6.
Longitudinal profile of the Jianshanying landslide.
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Figure 7.
Sliding process of the Jianshanying landslide simulated by Dan3D.
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Figure 8.
Maximum velocity contour of the Jianshanying landslide.
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Figure 9.
Runout prediction of the potential landslide.
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Figure 10.
Disaster scope of the potential landslide.