Skip to main content

ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 12 - 2024 | doi: 10.3389/feart.2024.1429421
This article is part of the Research Topic Prevention, Mitigation, and Relief of Compound and Chained Natural Hazards Volume II View all 4 articles

Application of different earthquake-induced Landslide hazard assessment models on the 2022 Ms 6.8 Luding Earthquake

Provisionally accepted
Yao Lu Yao Lu 1Siyuan Ma Siyuan Ma 2*Xia Chaoxu Xia Chaoxu 2
  • 1 Shaanxi Earthquake Agency, Xi’an, China
  • 2 Institute of Geology, China Earthquake Administration, Beijing, China

The final, formatted version of the article will be published soon.

    Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model's prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment.

    Keywords: 2022 Ms6.8 Luding earthquake, Coseismic landslide, Emergency assessment, Newmark model, Logistic regression (LR) model

    Received: 08 May 2024; Accepted: 07 Aug 2024.

    Copyright: © 2024 Lu, Ma and Chaoxu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Siyuan Ma, Institute of Geology, China Earthquake Administration, Beijing, 100029, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.