Remote Sensing (Aug 2024)

Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake

  • Siyuan Ma,
  • Xiaoyi Shao,
  • Chong Xu

DOI
https://doi.org/10.3390/rs16152861
Journal volume & issue
Vol. 16, no. 15
p. 2861

Abstract

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On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher.

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