Effects of Seismogenic Faults on the Predictive Mapping of Probability to Earthquake-Triggered Landslides

Abstract

The seismogenic fault is crucial for spatial prediction of co-seismic landslides, e.g., in logistic regression (LR) analysis considering influence factors. On one hand, earthquake-induced landslides are usually densely distributed along the seismogenic fault; on the other hand, different sections of the seismogenic fault may have distinct landslide-triggering capabilities due to their different mechanical properties. However how the feature of a fault influence mapping of landslide occurrence probability remains unclear. Relying on the landslide data of the 2013 Lushan, China Mw 6.6 earthquake, this study attempted to further address this issue. We quantified the seismogenic fault effects on landslides into three modes: the distance effect, the different part effects, and the combined effects of the two. Four possible cases were taken into consideration: zoning the study area vertical and parallel to the fault (case 1), zoning the study area only vertical to the fault (case 2), zoning the study area only parallel to the fault (case 3), and without such study-area zonations (case 4). Using the LR model, predictive landslide probability maps were prepared on these four cases. The model also fully considered other influencing factors of earthquake landslides, including elevation, slope, aspect, topographic wetness index (TWI), peak ground acceleration (PGA), lithology, rainfall, distance from the epicenter, distance from the road, and distance from the river. Then, cross-comparisons and validations were conducted on these maps. For training datasets, results show that the success rates of earthquake-triggered landslides for the former three scenarios were 85.1%, 84.2%, and 84.7%, respectively, while that of the model for case 4 was only 84%. For testing datasets, the prediction rates of the four LRs were 84.45%, 83.46%, 84.22%, and 83.61%, respectively, as indicated by comparing the test dataset and the landslide probability map. This means that the effects of the seismogenic fault, which are represented by study-area zonations vertical and parallel to the fault proper, are significant to the predictive mapping of earthquake-induced landslides.

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

“…Here we conclude by noting that the selected predictors are in line with those selected by other studies in the field of EQtLs susceptibility (e.g., Shao et al, 2019) and reflect factors considered particularly favorable in inducing landslides in the Italian territory by national reports (link here). Further, as mentioned above, the possible presence of collinear predictors is handled by the neural network.…”

Seismic And Geomorphic Assessment For Coseismic Landslides Zonation In Tropical Volcanic Contexts

“…The baseline of the methodology developed follows the MVM method and its modifications over time (Mora et al 1993;Ruiz et al 2019a). This method is similar to the logic applied in other studies worldwide (e.g., Tian et al 2018;Shao et al 2019;Chen et al 2020). Other studies (Xu et al 2013;Fan et al 2018;Li et al 2020;Li et al 2021) have carried out an inverse process, evaluating based on the catalog of landslides, the higher incidence of these events and determining the possible trace of the fault responsible for the earthquake.…”

“…This is accomplished through a seismological probabilistic analysis from the seismic catalog to determine the Mmax and complemented by means of a deterministic analysis based on earthquake scenarios obtained from the seismotectonic analyses and seismic potential assessment. This aspect is highly relevant since it has been used in studies such Xu et al (2013), Morell et al (2018, Shao et al (2019), Chen et al (2021) and Zhang et al (2021) who evaluate the effects in areas with respect to the seismogenic fault, and its intensity is crucial for spatial prediction of intensities and coseismic landslides. Our results show that the method is accurate for use in landslide susceptibility zoning and applicable to land use planning.…”

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

“…The current landslide susceptibility assessment models can be categorized into qualitative and quantitative models [22]. Qualitative models assess landslide susceptibility based on factors defined by experts, while quantitative models rely on statistical and machine learning techniques such as logistic regression [23,24], random forests [25], artificial neural networks [22], convolutional neural networks [26], support vector machines [27], and decision trees [28]. In recent years, with the advancement of machine learning technologies, these algorithms have been widely applied in landslide susceptibility assessment.…”