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    Volume 48 Issue 5
    May  2023
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    Article Navigation >  Earth Science  > 2023  >  48(5): 1960-1976
    Li Yongwei, Xu Linrong, Zhang Liangliang, Lu Zhiqiang, Su Na, 2023. Study on Development Patterns and Susceptibility Evaluation of Coseismic Landslides within Mountainous Regions Influenced by Strong Earthquakes. Earth Science, 48(5): 1960-1976. doi: 10.3799/dqkx.2022.224
    Citation: Li Yongwei, Xu Linrong, Zhang Liangliang, Lu Zhiqiang, Su Na, 2023. Study on Development Patterns and Susceptibility Evaluation of Coseismic Landslides within Mountainous Regions Influenced by Strong Earthquakes. Earth Science, 48(5): 1960-1976. doi: 10.3799/dqkx.2022.224
    shu

    Study on Development Patterns and Susceptibility Evaluation of Coseismic Landslides within Mountainous Regions Influenced by Strong Earthquakes

    doi: 10.3799/dqkx.2022.224
    • 1.

      School of Civil Engineering, Central South University, Changsha 410075, China

    • 2.

      National Engineering Research Center of High-Speed Railway Construction Technology, Central South University, Changsha 410075, China

    • Received Date: 2022-09-21
      Available Online: 2023-06-06
    • Publish Date: 2023-05-25
      Abstract
    • Coseismic landslides are imperceptible and widely distributed in the mountainous regions with abundant vegetation influenced by strong earthquakes, which hinders the development of prevention and control for disaster. Landslide susceptibility evaluation can predict the landslide-prone areas. But few studies have obtained the high-precision landslide susceptibility maps, and it was difficult to uncover the patterns of coseismic landslides under the complex geo-environment, because the lack of data and there was no unified standards for quantification of data based on traditional landslide susceptibility evaluation method. Therefore, the multi-source monitoring data, spatial analysis and deep learning method were adopted in this work to find out the development patterns, seismic response mechanism of landslide and to obtain landslide susceptibility maps. The main conclusions are as follows: the seismic response mechanism of landslide was proposed based on the seismic response analysis of landform and rock structure. The CNN (convolutional neural network) and DNN (deep neural network) achieved the relatively good area under the curve (AUC) value (AUC were 0.901 and 0.865, respectively), which could obtain the high-precision landslide susceptibility maps. Extremely high and high susceptibility region of landslides were widely distributed in 13 gullies, such as Danzu gully and so on. And these gullies were more prone to debris flow under rainstorm.

       

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