Development characteristics and susceptibility assessment of coseismic geological hazards of Jishishan MS 6.2 earthquake, Gansu Province, China
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摘要:
2023年12月18日，甘肃积石山县发生MS 6.2地震，诱发的同震地质灾害严重威胁到人民生命和财产安全，因此及时总结分析同震地质灾害发育规律并进行县域易发性评价，对支撑震后恢复重建至关重要。通过应急排查、野外调查与结果分析，对同震地质灾害发育特征进行分析总结；以震后排查的同震新增和加剧隐患点为分析样本，采用Pearson相关性系数与随机森林Gini系数分析方法，筛选了15个影响因子，并运用机器学习-随机森林模型对积石山县进行同震地质灾害易发性评价。结果表明，震区同震地质灾害总体发育程度不强，规模以小型为主，崩滑流地质灾害隐患可分为3大类、8个亚类，绝大部分分布在黄土丘陵区；积石山县同震地质灾害随机森林模型易发性评价(AUC=0.961)结果显示，极高易发区面积占比约8.67%，主要分布在胡林家乡、徐扈家乡、柳沟乡等乡镇，且县域及各乡镇易发性分级结构与隐患点密度分布吻合程度高。评价结果对已有排查隐患点以外的震裂山体或潜在崩滑流灾害具有重要指示作用，可为积石山县灾后恢复重建规划提供决策支撑，同时将Pearson相关性系数与随机森林Gini系数的影响因子筛选方法及机器学习模型——随机森林应用于易发性评价中，可为其他山地丘陵区地质灾害易发性评价提供参考。
Abstract:Objective On December 18, 2023, an MS 6.2 earthquake occurred in Jishishan County, Gansu Province, China. Coseismic geological hazards induced by the earthquake crucially threatened the safety of personnel and property. Existing research is mainly concentrated in the vicinity of active faults and the concentrated distribution area of hidden danger points. Moreover, no special susceptibility assessment studies have been carried out on coseismic geological hazards in the administrative area of Jishishan County, making it challenging to meet the needs of the county's post-disaster recovery and reconstruction planning. Hence, the development laws of coseismic geological hazards must be summarized and analyzed crucially, and county susceptibility must be analyzed in time to support post-earthquake recovery and reconstruction.
Methods The development characteristics of coseismic geological hazards are analyzed and summarized through emergency investigations, field surveys, and result analysis. Using the newly added and exacerbated coseismic hazard points identified during post-earthquake investigations as analysis samples, influencing factors were selected using the Pearson correlation coefficient and random forest Gini coefficient analysis methods. Then, a machine learning-random forest model was applied to assess the susceptibility of coseismic geological hazards in Jishishan County.
Results In analyzing the development characteristics of coseismic geological hazards, we identified 134 instances of increased and exacerbated hazards in Jishishan County. Overall, the degree of development of these hazards was relatively low, with primarily small-scale occurrences. These hazards were categorized into three main types and eight sub-categories: ① Collapse (including cut slope loess collapse, high loess collapse, and high rock collapse); ② Landslide (encompassing loess landslide, secondary sand/mudstone landslide, and potential landslide); and ③ Debris flow (comprising gully debris flow and slope debris flow). In terms of factor selection, 15 influencing factors were screened. Regarding the susceptibility assessment results, the AUC value of the susceptibility assessment results of coseismic geological hazards in most Jishishan counties was 0.961, and the results showed that the areas of extremely high susceptibility accounted for approximately 8.67 %, mainly distributed in Hulinjia, Xuhujia, Liugoujia, and other townships. The statistical results of the proportion of susceptibility zones in 17 townships in Jishishan County showed that the top three townships with the largest proportions of extremely high-susceptibility areas are Hulinjia (24.67%), Xuhujia (21.24%), and Biezang (20.94%).
Conclusion (1) Most coseismic geological hazards in Jishishan are distributed in the loess hilly area, with few occurrences in the Jishishan area and the right bank terrace of the Yellow River. (2) The influence of elevation and peak ground acceleration (PGA) on hazard occurrence is notably greater than that of other factors, playing a predominant role in developing coseismic geological hazards. (3) Utilizing the random forest model, the susceptibility assessment of coseismic geological hazards in Jishishan County demonstrates high accuracy, with hidden danger points clustered in highly susceptible areas. This alignment between susceptibility assessment results and the spatial distribution of hidden dangers underscores the reliability of the assessment outcomes.
Significance In addition to identifying existing hidden danger points, this study offers predictive insights into slope deformation and potential landslides significantly affected by seismic cracking. The assessment results exhibit high accuracy and reliability, offering valuable geological safety support for post-disaster recovery and reconstruction planning in the county.
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Key words:
- Jishishan earthquake /
- coseismic /
- geological hazards /
- susceptibility /
- random forest /
- influencing factor
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图 1 积石山县区域地质背景图(图件要素数据来源：活动断裂据震区活动断裂野外调查编绘；速报震中及余震数据来源：国家地震科学数据中心
https://data.earthquake.cn/datashare ；修正震中数据来源：房立华等，2023)Figure 1.
表 1 初选影响因子数据来源说明表
Table 1. Data source for initial influence factors
序号 分类 要素 致灾指示意义、数据说明及处理方法 1~6 地形地貌 坡度、坡向、高程、平面曲率、剖面曲率、起伏度 指示坡体几何形态属性，基于ALOS DEM(12.5 m分辨率) 7 地层岩性 地层岩性分区 指示斜坡岩土体的物理力学强度特征，基于1∶20万地质图(公开版) 8~9 河流 距一级河流距离、距二级河流距离 指示坡脚侵蚀及坡体的水文地质特征，使用1∶25万基础地理信息数据 10~11 道路 距一级道路距离、距二级道路距离 指示人类工程活动对斜坡破环的影响，使用1∶25万基础地理信息数据 12 地震 地震动峰值加速度(PGA) 指示地震震动效应对斜坡破坏的影响，基于实测地震烈度台站记录，采用考虑断层距的地震动峰值加速度经验衰减关系(肖亮，2012)拟合计算生成，1 km栅格 13 地质构造 距断层距离 指示地质体结构破碎及对地质灾害发育的影响，基于震区活动断裂野外调查编测 14~15 环境地质特征 水流强度指数(SPI)、地形湿度指数(TWI) 指示影响斜坡发育的水文环境地质特征，基于ALOS DEM(12.5 m分辨率)栅格计算生成 16 修正归一化水体指数(MNDWI) 指示影响斜坡发育的水文环境地质特征，基于landsat8遥感影响数据，30 m分辨率 17~18 土地利用类型、归一化建筑指数(NDBI) 指示影响斜坡发育的人类活动环境地质特征，基于landsat8遥感影响数据，30 m分辨率 19 归一化植被指数(NDVI) 指示影响斜坡发育的自然环境地质特征，基于landsat8遥感影响数据，30 m分辨率 
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