Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework

Version 1 : Received: 12 June 2024 / Approved: 12 June 2024 / Online: 12 June 2024 (11:59:45 CEST)

How to cite: Liu, T.; Zang, M.; Peng, J.; Xu, C. Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework. Preprints 2024, 2024060837. https://doi.org/10.20944/preprints202406.0837.v1 Liu, T.; Zang, M.; Peng, J.; Xu, C. Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework. Preprints 2024, 2024060837. https://doi.org/10.20944/preprints202406.0837.v1

Abstract

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high susceptibility areas for coseismic landslides. The study explores factors influencing model fit, revealing that the inclusion of both epicentral distance and the distance to seismogenic fault and in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations.

Keywords

2022 Luding earthquake; coseismic landslide susceptibility; Newmark displacement regression model; the distance to seismogenic fault; epicentral distance

Subject

Environmental and Earth Sciences, Geography

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