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.
Highlights
Large earthquakes can trigger a large number of co-seismic landslides, especially in mountainous areas, and the damage caused by co-seismic landslides is much higher than that caused by the earthquake itself [1]
Many methods have been applied to landslide susceptibility assessment, which can be classified into qualitative approaches based on expert experiences [12] and quantitative approaches based on statistical analysis [13,14]
Taking the Lushan earthquake as an example, this work examined the influence of seismogenic faults on the predictive mapping of landslide probability using logistic regression (LR)
Summary
Large earthquakes can trigger a large number of co-seismic landslides, especially in mountainous areas, and the damage caused by co-seismic landslides is much higher than that caused by the earthquake itself [1]. A lot of studies have been conducted on several aspects of earthquake-induced landslides, such as landslide inventory [2,3,4,5], the spatial distribution of landslides [6,7], and landslide susceptibility assessment [8,9,10]. Many methods have been applied to landslide susceptibility assessment, which can be classified into qualitative approaches based on expert experiences [12] and quantitative approaches based on statistical analysis [13,14]. The statistical analysis method is widely used for landslide susceptibility assessment because of its objective evaluation results and high prediction accuracy. Some studies [18,19] have shown that the logic regression model is relatively effective in regional landslide prediction and has been verified in many earthquake events [20,21,22]
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