Discussion on Refined Identification, Verification, Prevention and Control Models for Geo-hazards Risk

XUE Qiang; DONG Ying; ZHANG Maosheng; LI Lin; GAO Bo; MENG Xiaojie; GUO Xiaopeng

doi:10.12401/j.nwg.2024092

2025 Vol. 58, No. 2

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Article navigation > Northwestern Geology > 2025 > 58(2): 66-79

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XUE Qiang, DONG Ying, ZHANG Maosheng, LI Lin, GAO Bo, MENG Xiaojie, GUO Xiaopeng. 2025. Discussion on Refined Identification, Verification, Prevention and Control Models for Geo-hazards Risk. Northwestern Geology, 58(2): 66-79. doi: 10.12401/j.nwg.2024092

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XUE Qiang, DONG Ying, ZHANG Maosheng, LI Lin, GAO Bo, MENG Xiaojie, GUO Xiaopeng. 2025. Discussion on Refined Identification, Verification, Prevention and Control Models for Geo-hazards Risk. Northwestern Geology, 58(2): 66-79. doi: 10.12401/j.nwg.2024092

Discussion on Refined Identification, Verification, Prevention and Control Models for Geo-hazards Risk

1.
Key Laboratory for Geo-hazards in Loess Area, Observation and Research Station of Geo-hazards in Loess Plateau, MNR, Xi’an Center of China Geological Survey/Northwest China Center for Geoscience Innovation, Xi’an 710119, Shaanxi, China
2.
School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 712000, Shaanxi, China

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Corresponding author: DONG Ying, dongy329@163.com

Received Date: 05 March 2024

Revised Date: 23 September 2024

Accepted Date: 09 October 2024

Publish Date: 20 April 2025

Abstract

Abstract

Accurate identification of geo-hazards is the key to achieving a shift from post disaster assistance to pre disaster prevention, and precise prevention and control of geo-hazards is the key to achieving a shift from reducing disaster losses to mitigating disaster risks. This paper takes the loess landslide and collapse disasters in Yulin area as an example. A set of geo-hazards fine identification and risk prevention technology method system has been established through surface deformation identification, identification of high and steep landslide-prone slopes, verification of dangerous slope sections, field verification, and implementation of risk prevention and control measures. The results show that: ① A total of 493380 high and steep landslide-prone slopes were identified based on DEM data in Yulin area, and 635 surface deformation sections were identified using InSAR technology. On this basis, A total of 31988 dangerous slope sections (landslide-prone slopes or surface deformation sections with threatening objects) were identified and verified based on optical remote sensing data and elements at risk information. The number of dangerous slope sections accounts for 6.48% of the number of landslide-prone slopes and surface deformation sections. The accuracy of identification has been improved, and "Precise to Slope" of potential geo-hazards identification has been achieved. ② We conducted field verification on dangerous slope sections identified by remote sensing, dangerous and disaster situations reported on-site by the masses, and registered geo-hazards by entering villages and households, and named the potential geo-hazards with "village group + household head name". Risk classification of potential geo-hazards based on slope structure, deformation signs, threat objects, etc. A total of 37523 potential geo-hazards were verified in the field in Yulin area. The risk base of potential geo-hazards has been accurately determined, and "Precise to Household" of potential geo-hazards verification has been achieved. ③ We have developed potential geo-hazards early warning response plans and risk prevention and control measures for different risk levels. The graded prevention and control pattern of potential geo-hazards in cities, counties, towns, and villages has been formed, and "Precise Control" of potential geo-hazards risk has been achieved. The timely and successful application of potential geo-hazards risk identification, verification, and prevention and control models has effectively controlled the geo-hazards risk in Yulin area, and provided a demonstration for potential geo-hazards identification and verification.
- geo-hazards /
- early identification /
- remote sensing interpretation /
- field verification /
- risk prevention and control /
- Yulin area

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References

[1]	董永路. 陕西省子洲县崩塌、滑坡灾害易发性与危险性评价[D]. 西安: 长安大学, 2023. Google Scholar DONG Yonglu. Vulnerability and risk assessment of collapse and landslide disasters in Zizhou County, Shaanxi Province[D]. Xi’an: Chang’an University, 2023. Google Scholar
[2]	葛大庆, 戴可人, 郭兆成, 等. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J]. 武汉大学学报·信息科学版, 2019, 44（7）: 949−956. Google Scholar GE Daqing, DAI Keren, GUO Zhaocheng, et al. Early Identification of Serious Geological Hazards with Integrated Remote Sensing Technologies: Thoughts and Recommendations[J]. Geomatics and Information Science of Wuhan University, 2019, 44（7）: 949−956. Google Scholar
[3]	杜鹏, 陈宁生, 伍康林, 等. 基于随机森林模型的藏东南地区滑坡易发性评价及主控因素分析[J]. 成都理工大学学报(自然科学版), 2024, 51（2）: 328−344. Google Scholar DU Peng, CHENG Ningsheng, WU Kanglin, et al. Evaluation of landslide susceptibility in southeast Xizang based on arandom forestmodel[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2024, 51（2）: 328−344. Google Scholar
[4]	郭华东. 雷达对地观测理论与应用[M]. 北京: 科学出版社, 2000. Google Scholar
[5]	韩守富, 赵宝强, 殷宗敏, 等. 基于PS InSAR技术的黄土高原地质灾害隐患识别[J]. 兰州大学学报(自然科学版), 2020, 56（1）: 1−7. Google Scholar HAN Shoufu, ZHAO Baoqiang, YIN Zongmin, et al. The identification of potential geological hazards on the Loess Plateau based on PS InSAR technology[J]. Journal of Lanzhou University: Natural Sciences, 2020, 56（1）: 1−7. Google Scholar
[6]	贾俊, 李志忠, 郭小鹏, 等. 多源遥感技术在降雨诱发勉县地质灾害调查中的应用[J]. 西北地质, 2023, 56（3）: 268−280. doi: 10.12401/j.nwg.2023069 CrossRef Google Scholar JIA Jun, LI Zhizhong, GUO Xiaopeng, et al. Application of Multi–source Remote Sensing Technology on Investigation of Geological Disasters Induced by Rainfall in Mian County[J]. Northwestern Geology, 2023, 56（3）: 268−280. doi: 10.12401/j.nwg.2023069 CrossRef Google Scholar
[7]	兰恒星, 彭建兵, 祝艳波, 等. 黄河流域地质地表过程与重大灾害效应研究与展望[J]. 中国科学: 地球科学, 2022, 65（2）: 234−256. Google Scholar LAN Hengxing, PENG Jianbing, ZHU Yanbo, et al. Research on geological and surfacial processes and major disaster effects in the Yellow River Basin[J]. Science China Earth Sciences, 2022, 65（2）: 234−256. Google Scholar
[8]	廖明生, 王腾. 时间序列InSAR技术与应用[M]. 北京: 科学出版社, 2014. Google Scholar
[9]	刘传正, 陈春利. 中国地质灾害防治成效与问题对策[J]. 工程地质学报, 2020, 28（2）: 375−383. Google Scholar LIU Chuanzheng, CHEN Chunli. Achievements and countermeasures in risk reduction of geological disasters in China[J]. Journal of Engineering Geology, 2020, 28（2）: 375−383. Google Scholar
[10]	林明明, 赵勇, 王坤, 等. 基于多源时序InSAR技术的滑坡隐患早期识别[J]. 西北地质, 2024, 57（6）: 268−277. Google Scholar LIN Mingming, ZHAO Yong, WANG Kun, et al. Early Identification of Potential Dangers of Loess Landslide Based on Multi-Source and Time Series InSAR[J]. Northwestern Geology, 2024, 57（6）: 268−277. Google Scholar
[11]	刘传正. 崩塌滑坡灾害风险识别方法初步研究[J]. 工程地质学报, 2019, 27（1）: 88−97. Google Scholar LIU Chuanzheng. Analysis methods on the risk identification of landslide disasters[J]. Journal of Engineering Geology, 2019, 27（1）: 88−97. Google Scholar
[12]	苏晓军, 张毅, 贾俊, 等. 基于InSAR技术的秦岭南部略阳县潜在滑坡灾害识别研究[J]. 山地学报, 2021, 39（1）: 59−70. Google Scholar SU Xiaojun, ZHANG Yi, JIA Jun, et al. InSAR-based monitoring and identification of potential landslides in Lueyang County, the Southern Qinling Mountains, China[J]. Mountain Research, 2021, 39（1）: 59−70. Google Scholar
[13]	孙萍萍, 张茂省, 贾俊, 等. 中国西部黄土区地质灾害调查研究进展[J]. 西北地质, 2022, 55（3）: 96−107. Google Scholar SUN Pingping, ZHANG Maosheng, JIA Jun, et al. Geo-hazards research and investigation in the Loess Regions of Western China[J]. Northwestern Geology, 2022, 55（3）: 96−107. Google Scholar
[14]	唐辉明. 重大滑坡预测预报研究进展与展望[J]. 地质科技通报, 2022, 41（6）: 1−13. Google Scholar TANG Huiming. Advance and prospects of major landslides prediction and forecasting[J]. Bulletin of Geological Science and Technology, 2022, 41（6）: 1−13. Google Scholar
[15]	唐然, 任穗川, 范宣梅, 等. 大型红层缓倾岩层滑坡形成机制——以川北断渠滑坡为例[J]. 成都理工大学学报(自然科学版), 2024, 51（4）: 673−686. Google Scholar TANG Ran, REN Suichuan, FAN Xuanmei, et al. Formation mechanism of large-scale red bed gently inclined strata landslide: Taking Duangu landslide in north Sichuan as an example[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2024, 51（4）: 673−686. Google Scholar
[16]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报·信息科学版, 2019, 44（7）: 957−966. Google Scholar XU Qiang, DONG Xiujun, LI Weile. Integrated space-air-ground early detection, monitoring and warning for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44（7）: 957−966. Google Scholar
[17]	吴铭洋, 陈宁生, 杨溢, 等. 三工河小流域泥石流生态—岩土工程调控措施减缓土壤侵蚀的定量研究[J]. 成都理工大学学报(自然科学版), 2024, 51（1）: 117−129. Google Scholar WU Mingyang, CHEN Ningsheng, YANG Yi, et al. Quantitative study on the mitigation of soil erosion by comprehensive ecological and geotechnical debris flow treatment measures in small basins: A case study of the Sangong River Basin[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2024, 51（1）: 117−129. Google Scholar
[18]	许强. 对地质灾害隐患早期识别相关问题的认识与思考[J]. 武汉大学学报·信息科学版, 2020, 45（11）: 1651−1659. Google Scholar XU Qiang. Understanding and consideration of related issues in early identification of potential geohazards[J]. Geomatics and Information Science of Wuhan University, 2020, 45（11）: 1651−1659. Google Scholar
[19]	薛强, 毕俊擘, 李政国, 等. 三维激光扫描技术在阎家沟滑坡变形监测中的应用[J]. 中国地质灾害与防治学报, 2018, 29（3）: 108−112. Google Scholar XUE Oiang, Bl Junbo, Ll Zhengguo, et al. Application of 3D laser scanning technology in deformation monitoring of the Yanjiagou Landslide[J]. The Chinese Journal of Geological Hazard and Control, 2018, 29（3）: 108−112. Google Scholar
[20]	薛强, 张茂省, 董英, 等. 基于DEM和遥感的黄土地质灾害精细化风险识别——以陕北黄土高原区米脂县为例[J]. 中国地质, 2023, 50（3）: 926−942. doi: 10.12029/gc20220801001 CrossRef Google Scholar XUE Qiang, ZHANG Maosheng, DONG Ying, et al. Refinement risk identification of loess geo-hazards based on DEM and remote sensing: Taking Mizhi County in the Loess Plateau of Northern Shaanxi as an example[J]. Geology in China, 2023, 50（3）: 926−942. doi: 10.12029/gc20220801001 CrossRef Google Scholar
[21]	殷跃平, 王鲁琦, 赵鹏, 等. 三峡库区高陡岸坡溃屈失稳机理及防治研究[J]. 水利学报, 2022, 53（4）: 379−391. Google Scholar YIN Yueping, WANG Luqi, ZHAO Peng, et al. Crashed failure mechanism & prevention of fractured high-steep slope in the Three Gorges Reservoir, China[J]. Journal of Hydraulic Engineering, 2022, 53（4）: 379−391. Google Scholar
[22]	张茂省, 李林, 唐亚明, 等. 基于风险理念的黄土滑坡调查与编图研究[J]. 工程地质学报, 2011, 19（1）: 43−51. doi: 10.3969/j.issn.1004-9665.2011.01.007 CrossRef Google Scholar ZHANG Maosheng, LI Lin, TANG Yaming, et al. Risk management based landslide investigation and mapping in loess area[J]. Journal of Engineering Geology, 2011, 19（1）: 43−51. doi: 10.3969/j.issn.1004-9665.2011.01.007 CrossRef Google Scholar
[23]	张林梵. 基于时序InSAR的黄土滑坡隐患早期识别—以白鹿塬西南区为例[J]. 西北地质, 2023, 56（3）: 250−257. Google Scholar ZHANG Linfan. Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan[J]. Northwestern Geology, 2023, 56（3）: 250−257. Google Scholar
[24]	张茂省, 贾俊, 王毅, 等. 基于人工智能(AI)的地质灾害防控体系建设[J]. 西北地质, 2019, 52（2）: 103−116. Google Scholar ZHANG Maosheng, JIA Jun, WANG Yi, et al. Construction of geological disaster prevention and control system based on AI[J]. Northwestern Geology, 2019, 52（2）: 103−116. Google Scholar
[25]	张茂省, 薛强, 贾俊, 等. 地质灾害风险管理理论方法与实践[M]. 北京: 科学出版社, 2021. Google Scholar ZHANG Maosheng, XUE Qiang, JIA Jun, et al. Theory, method and practice of geological hazard risk management[M]. Beijing: Science Press, 2021. Google Scholar
[26]	张勤, 赵超英, 陈雪蓉. 多源遥感地质灾害早期识别技术进展与发展趋势[J]. 测绘学报, 2022, 51（6）: 885−896. doi: 10.11947/j.issn.1001-1595.2022.6.chxb202206012 CrossRef Google Scholar ZHANG Qin, ZHAO Chaoying, CHEN Xuerong. Technical progress and development trend of geological hazards early identification with multi-source remote sensing[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51（6）: 885−896. doi: 10.11947/j.issn.1001-1595.2022.6.chxb202206012 CrossRef Google Scholar