甘肃积石山Ms6.2级地震诱发滑坡快速评估

曾营; 张迎宾; 储峰; 柳静; 冯振海; 苏金蓉

doi:10.3969/j.issn.0258-2724.20240041

甘肃积石山Ms6.2级地震诱发滑坡快速评估

doi: 10.3969/j.issn.0258-2724.20240041

曾营^1,,
张迎宾^1, ,,
储峰²,
柳静¹,
冯振海¹,
苏金蓉³

1.
西南交通大学土木工程学院，四川成都 610031
2.
中国路桥工程有限责任公司，北京 100011
3.
四川省地震局，四川成都 610041

基金项目: 国家自然科学基金项目（52378370，52278372）

详细信息

作者简介:
曾营（1997—），男，博士研究生，研究方向为地质灾害风险性评价，E-mail：zengying@my.swjtu.edu.cn

通讯作者:
张迎宾（1983—），男，教授，博士，研究方向为岩土地震工程，E-mail：yingbinz719@swjtu.edu.cn

中图分类号: P642.2
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- HTML全文浏览量: 31
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- 被引次数: 0
出版历程
- 收稿日期: 2024-01-19
- 修回日期: 2024-04-10
- 网络出版日期: 2025-01-21

Rapid Assessment of Landslides Induced by Jishishan Ms6.2 Earthquake in Gansu Province

1.
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China
2.
China Road & Bridge Corporation, Beijing 100011, China
3.
Sichuan Earthquake Administration, Chengdu 610041, China

摘要

摘要:
震后迅速获取同震滑坡分布及灾情评估对于应急救援和重建工作至关重要，采用IDNPM（InSAR data-newmark physical fusion driver model）方法对2023年12月18日甘肃积石山地震引发的滑坡进行快速评估，以期迅速精准掌握滑坡灾害的宏观分布. 首先，通过时序SBAS-InSAR揭示该地区有着严重的冲沟发育和溯源侵蚀现象，这些地质特征为滑坡提供了有利的孕育环境；其次，运用IDNPM方法对积石山地震进行滑坡快速评估，预测出赵木川村、塔沙坡村、大河家镇等地的陡峭斜坡及沟壑两侧为地震诱发滑坡的高风险区域；最后，综合实地考察、数值模拟及卫星识别技术，验证该模型在实际应用中的可靠性. 结果表明：全区共有2.657%的高风险区，需要重点关注此类区域；对已发生崩滑的坡体紧急清理和加固，对于未发生滑移的区域，应采取监测和评估措施，以防范可能发生的震后次生滑坡事件；研究成果可为受灾区的灾后应急救援和恢复重建工作提供有力的数据支撑.
- 地震 /
- 滑坡 /
- 应急评估 /
- 预测模型 /
- InSAR技术
Abstract:
Rapidly obtaining co-seismic landslide distribution and conducting disaster assessments after earthquakes are vital for effective emergency relief and reconstruction. Therefore, in this study, the InSAR data-Newmark physical fusion driver model (IDNPM) was used to rapidly assess the landslides triggered by the earthquake in Jishishan, Gansu Province on December 18, 2023, with a view to quickly and accurately grasping the macroscopic distribution of landslide hazards. Firstly, through the time series SBAS-InSAR, it was revealed that there was serious gully development and retrogressive erosion in this area. These geological characteristics provided a favorable breeding environment for landslides. Secondly, the IDNPM was used to quickly evaluate the landslide of Jishishan earthquake, and it was predicted that the steep slopes and gully sides of Zhaomuchuan Village, Tashapo Village, and Dahejia Town were the high-risk areas for earthquake-induced landslide. Finally, based on the field investigation, numerical simulation, and satellite identification technology, the reliability of the model in practical application was proven. The results indicate that a total of 2.657% of the region is at high risk. There is a need to focus on such zones by urgently clearing and stabilizing slopes where landslides have occurred. For areas where no landslides have occurred, monitoring and assessment measures should be taken to guard against possible post-earthquake secondary landslide events. The research results can provide strong data support for emergency relief and reconstruction work after earthquakes in the affected areas.
- earthquake /
- landslide /
- emergency assessment /
- prediction model /
- InSAR technology

HTML全文

图 1 研究区位置及地形

Figure 1. Location and topographic map of study area

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图 2 哨兵影像覆盖图

Figure 2. Sentinel image coverage map

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图 3 强震动记录台站分布

Figure 3. Distribution of stations recording strong ground motion

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图 4 地震动参数分布图

Figure 4. Distribution of ground motion parameters

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图 5 Newmark计算参数分析

Figure 5. Newmark analysis of calculation parameters

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图 6 Newmark滑坡危险性分析结果

Figure 6. Newmark analysis results of landslide risks

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图 7 SBAS-InSAR形变速率

Figure 7. SBAS-InSAR deformation rate

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图 8 IDNPM滑坡危险性评价结果及野外考察对比（滑坡C-10、31、41、54来自于文献[11]）

Figure 8. IDNPM landslide risk assessment results and field investigation comparison （Landslides C-10, 31, 41, and 54 in the figure are from literature [11]）

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图 9 IDNPM滑坡危险性评估结果与数值模拟、卫星识别对照

Figure 9. Comparison of landslide risk assessment results of IDNPM with numerical simulations and satellite identification

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表 1 数据及来源

Table 1. Data and sources

数据名	数据格式	时间	数据源
Sentinel-1A	5 m × 20 m	2022～2023年	阿拉斯加卫星设备 https://search.asf.alaska.edu
精密定轨		2022～2023年	欧洲航天局https://scihub.copernicus.eu
DEM^①	12.5 m	2009年	阿拉斯加卫星设备 https://search.asf.alaska.edu
DEM^②	30 m	2021年	日本宇宙航空研究开发机构https://global.jaxa.jp
地层岩性	1∶500000	2013年	国家地质资料库http://www.ngac.org.cn
卫星影像	10 m	2023年	欧洲航天局https://scihub.copernicus.eu

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表 2 研究区岩性力学参数及取值

Table 2. Mechanical parameters and values of lithology in study area

序号	工程地质单元	c/kPa	$ \varphi /{{^\circ }} $	$ \gamma $/（kN·m⁻³）
1	坚硬岩	38	40.0	24.5
2	较硬岩	35	35.0	23.0
3	较软岩	30	24.5	17.8
4	较弱岩	25	22.2	17.5
5	松散岩	22	21.0	15.4

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