التفاصيل البيبلوغرافية
| العنوان: |
甘肃积石山县MS6. 2 地震同震地质灾害发育特征与易发性评价. (Chinese) |
| Alternate Title: |
Development characteristics and susceptibility assessment of coseismic geological hazards of Jishishan MS6. 2 earthquake, Gansu Province, China. (English) |
| المؤلفون: |
刘 帅, 何 斌, 王 涛, 刘甲美, 曹佳文, 王浩杰, 张 帅, 李 坤, 李 冉, 张永军, 窦晓东, 吴中海, 陈 鹏, 丰成君 |
| المصدر: |
Journal of Geomechanics; Apr2024, Vol. 30 Issue 2, p314-331, 18p |
| Abstract (English): |
[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 postearthquake 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 subcategories: 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 highsusceptibility 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
2023 年12 月18 日, 甘肃积石山县发生MS6. 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 FROM AUTHOR] |
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