Abstract: The M_S7.0 Jiuzhaigou earthquake in Sichuan Province of 8 August 2017 triggered a large number of landslides. A comprehensive and objective panorama of these landslides is of great significance for understanding the mechanism, intensity, spatial pattern and law of these coseismic landslides, recovery and reconstruction of earthquake affected area, as well as prevention and mitigation of landslide hazard. The main aim of this paper is to present the use of remote sensing images, GIS technology and Logistic Regression(LR)model for earthquake triggered landslide hazard mapping related to the 2017 Jiuzhaigou earthquake. On the basis of a scene post-earthquake Geoeye-1 satellite image(0.5m resolution), we delineated 4834 co-seismic landslides with an area of 9.63km². The ten factors were selected as the influencing factors for earthquake triggered landslide hazard mapping of Jiuzhaigou earthquake, including elevation, slope angle, aspect, horizontal distance to fault, vertical distance to fault, distance to epicenter, distance to roads, distance to rivers, TPI index, and lithology. Both landsliding and non-landsliding samples were needed for LR model. Centroids of the 4834 initial landslide polygons were extracted for landslide samples and the 4832 non-landslide points were randomly selected from the landslide-free area. All samples(4834 landslide sites and 4832 non-landslide sites)were randomly divided into the training set(6767 samples)and validation set(2899 samples). The logistic regression model was used to carry out the landslide hazard assessment of the Jiuzhaigou earthquake and the results show that the landslide hazard assessment map based on LR model is very consistent with the actual landslide distribution. The areas of Wuhuahai-Xiamo, Huohuahai and Inter Continental Hotel of Jiuzhai-Ruyiba are high hazard areas. In order to quantitatively evaluate the prediction results, the trained model calculated with the training set was evaluated by training set and validation set as the input of the model to get the output results of the two sets. The ROC curve was used to evaluate the accuracy of the model. The ROC curve for LR model was drawn and the AUC values were calculated. The evaluation result shows good prediction accuracy. The AUC values for the training and validation data set are 0.91 and 0.89, respectively. On the whole, more than 78.5% of the landslides in the study area are concentrated in the high and extremely high hazard zones. Landslide point density and landslide area density increase very rapidly as the level of hazard increases. This paper provides a scientific reference for earthquake landslides, disaster prevention and mitigation in the earthquake area.

Key words: Jiuzhaigou M_S7.0 earthquake, landslide, logistic regression model, landslide hazard assessment

摘要： 发生于2017年8月8日的四川九寨沟M_S7.0地震触发了大量的同震滑坡。基于Geoeye-1震后0.5m分辨率的遥感影像开展极震区同震滑坡解译，圈定了4834处滑坡。选择高程、坡度、坡向、水平断层距离、垂直断层距离、震中距离、河流距离、道路距离、TPI指数以及岩性共10个因子作为地震滑坡的影响因子，应用逻辑回归（Logistic Regression，LR）模型开展九寨沟地震滑坡危险性评价，并对评价结果的合理性进行检验。结果表明，基于LR模型的滑坡危险性评价图与实际滑坡发育情况十分吻合，其中五花海-夏莫段、火花海和九寨天堂洲际大饭店-如意坝段均为滑坡危险性极高的区域。采用ROC曲线对危险性评价结果进行模型成功率与预测率的定量评价，结果显示，LR模型的预测精度较为理想，训练样本集和验证样本集的AUC值分别为0.91和0.89。文中结论为震区恢复重建工作中地震滑坡的防灾减灾提供了科学参考。

关键词: 九寨沟M_S7.0地震, 滑坡, 逻辑回归模型, 危险性评估