突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例

许强 彭大雷 何朝阳 亓星 赵宽耀 修德皓

许强, 彭大雷, 何朝阳, 亓星, 赵宽耀, 修德皓. 2020: 突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例. 工程地质学报, 28(1): 111-121. doi: 10.13544/j.cnki.jeg.2019-038
引用本文: 许强, 彭大雷, 何朝阳, 亓星, 赵宽耀, 修德皓. 2020: 突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例. 工程地质学报, 28(1): 111-121. doi: 10.13544/j.cnki.jeg.2019-038
XU Qiang, PENG Dalei, HE Chaoyang, QI Xing, ZHAO Kuanyao, XIU Dehao. 2020: THEORY AND METHOD OF MONITORING AND EARLY WARNING FOR SUDDEN LOESS LANDSLIDE—A CASE STUDY AT HEIFANGTAI TERRACE. JOURNAL OF ENGINEERING GEOLOGY, 28(1): 111-121. doi: 10.13544/j.cnki.jeg.2019-038
Citation: XU Qiang, PENG Dalei, HE Chaoyang, QI Xing, ZHAO Kuanyao, XIU Dehao. 2020: THEORY AND METHOD OF MONITORING AND EARLY WARNING FOR SUDDEN LOESS LANDSLIDE—A CASE STUDY AT HEIFANGTAI TERRACE. JOURNAL OF ENGINEERING GEOLOGY, 28(1): 111-121. doi: 10.13544/j.cnki.jeg.2019-038

突发型黄土滑坡监测预警理论方法研究——以甘肃黑方台为例

doi: 10.13544/j.cnki.jeg.2019-038
基金项目: 

国家自然科学基金重点项目 41630640

国家创新研究群体科学基金项目 41521002

国家自然科学基金重大项目 41790445

详细信息
    作者简介:

    许强(1968-),男,博士,教授,博士生导师,主要从事地质灾害评价预测与防治处理研究.E-mail:xq@cdut.edu.cn

  • 中图分类号: P642.A

THEORY AND METHOD OF MONITORING AND EARLY WARNING FOR SUDDEN LOESS LANDSLIDE—A CASE STUDY AT HEIFANGTAI TERRACE

Funds: 

the Key Program of National Natural Science Foundation of China 41630640

the Science Fund for Creative Research Groups of the National Natural Science Foundation of China 41521002

the Major Program of National Natural Science Foundation of China 41790445

  • 摘要: 灌溉诱发的黄土滑坡大多数具有明显的突发性特征;斜坡破坏过程变形量小,历时短,具有较大的危险性。由于此类黄土滑坡加速变形阶段经历时间较短,GNSS系统和裂缝计等传统监测手段难以获取加速变形阶段系统完整的监测数据,更难以提前预警。针对这一难题,自主研发了自适应智能变频裂缝仪,它能够根据滑坡变形快慢自动调整采样频率。基于获取的黑方台多个突发型黄土滑坡的全过程变形-时间曲线,对这些变形曲线特征和规律进行分析研究,建立了针对性的黄土滑坡综合预警模型。将变形速率阈值和改进切线角作为滑坡预警的重要指标,建立了4级预警判据,通过自主研发的“地质灾害实时监测预警系统”实现滑坡的实时自动预警,并将预警信息与当地的群防群测信息平台对接,为防灾应急避让提供直接依据。2017年以来已先后6次对黑方台黄土滑坡实施成功预警,避免了重大人员伤亡,取得显著的防灾减灾效果。
  • 图  1  岩土体蠕变曲线簇

    Figure  1.  Slope displacement-time curve type

    图  2  黑方台地区黄土滑坡分布和监测系统布置

    a.黄土滑坡和监测设备分布;b.研究区典型剖面

    Figure  2.  Loess distribution and monitoring systems logout on the Heifangtai terrace

    图  3  研究区典型突发型黄土滑坡和监测设备布置图

    a.党川段;b.焦家段;c.磨石沟段

    Figure  3.  Typical sudden loess landslide and logout of monitoring systems in the three section

    图  4  自适应智能变频裂缝仪的组成及监测数据

    a.自适应智能变频裂缝仪监测站;b.一体化自适应智能变频裂缝仪;c.自适应调整采样频率示意图

    Figure  4.  Components of the improved self-adaptive crack gauge and monitoring data

    图  5  第1次成功监测数据不同去噪方法对比分析

    a.匀速变形阶段对比分析;b.全变形过程对比分析;c.融合两种方法滤波监测数据;d.融合两种方法加速度变形阶段累计位移-时间曲线

    Figure  5.  Comparison and analysis of different filtering methods for monitoring data of CJ8# in the Heifangtai area

    图  6  黑方台突发型黄土滑坡典型变形曲线特征

    a.党川3#滑坡裂缝计1#匀速变形阶段累计位移-时间曲线;b.陈家8#滑坡裂缝计5#监测的加速阶段累计位移-时间曲线

    Figure  6.  Typical deformation curve characteristics of sudden loess landslide on the Heifangtai terrace

    图  7  陈家8#裂缝计7#监测的累计位移、切线角、变形速率和变形速率增量变化规律

    a.全过程累计位移-时间曲线;b.加速度阶段累计位移-时间曲线

    Figure  7.  Incremental variation of cumulative displacement, tangent angle, deformation rate and deformation rate of CJ8#monitored by LFJ7#

    图  8  预警系统警报发布流程(Huang et al., 2015)

    Figure  8.  Alert publishing flow of the early warning system

    图  9  自2017年以来6次成功预警黑方台黄土滑坡分布图

    Figure  9.  Six times successful early warning of sudden loess landslides since 2017

    图  10  滑坡前后正射影像、高程变化和现场监测系统布置(位置见图 3a)

    a.滑前正射影像和监测系统布置;b.滑后正射影像及监测仪器损坏情况;c.滑坡前后高程变化

    Figure  10.  Orthophoto image, elevation change and in-site monitoring system layout before and after landslide(location see Fig. 3a)

    图  11  党川4#滑坡累计位移-时间、变形速率、变形速率增量和切线角曲线及预警过程

    a.变形全过程监测曲线;b.中加速阶段和临滑阶段监测曲线(局部放大)

    Figure  11.  Cumulative displacement-time, deformation rate, increment of deformation rate and tangent angle curve and early warning process of DC4# Landslide

    表  1  基于变形速率阈值和变形过程综合预警判据

    Table  1.   Comprehensive early warning model based on deformation rate threshold and deformation process

    变形阶段 初始变形阶段 匀速变形阶段 初加速阶段 中加速阶段 临滑阶段
    预警指标 第1步 变形速率/V V < V1 V1V < V2 V2V < V3 VV3
    变形速率增量/ΔV ΔV < 0 ΔV≈0 ΔV>0
    第2步 切线角/a a < 45° a ≈45° 45° < a < 80° 80°≤a < 85° a≥85°
    危险性预警级别
    其中,V1=3mm·d-1V2=10mm·d-1V3=20mm·d-1
    下载: 导出CSV

    表  2  党川4#突发型黄土滑坡成功预警的过程

    Table  2.   Successful early warning process for the DC4# sudden loess landslide

    预警时间 预警判据 预警等级 应急处理
    切线角a
    /(°)
    变形速率V
    /mm·d-1
    速率增量ΔV
    /mm·d-1
    2017-8-26 15:00 64.57 3.36 0.038 注意级 以短信、微信形式给专家、镇政府及相关人员发布蓝色预警信息,每天检查数据,每周发布监控公告
    2017-9-26 21:00 76.92 10.30 0.104 警示级 以短信、微信形式给专家、镇政府及相关人员发布黄色预警信息,每天检查数据,每周发布监控公告,群防群测人员加密监测
    2017-9-30 05:00 82.44 20.14 0.349 警戒级 以短信、微信、电话形式给专家、镇政府及相关人员发布橙色预警信息,每天24 h进行连续的综合监测和全面检查,专家磋商和讨论滑坡发展态势
    2017-9-30 17:50 85.08 30.62 0.462 警报级 以短信、微信、电话形式给专家、镇政府及相关人员发布红色预警信息;更频繁地检查数据,主干道封闭,当地人员被通知,进行24 h全面监测,专家磋商和讨论滑坡发展态势
    2017-9-30 20:55 87.33 62.07 1.592 以电话方式正式向镇政府及相关人员发布红色预警信息,当地政府采用必要的紧急疏散和快速应急工作
    下载: 导出CSV
  • Dong W W, Zhu H H, Sun Y J, et al. 2016. Current status and new progress on slope deformation monitoring technologies[J]. Journal of Engineering Geology, 24(6): 1088-1095. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201606008
    Dong X J, Xu Q, Tang C, et al. 2015. Characteristics of landslide displacement-time curve by physical simulation experiment[J]. Journal of Engineering Geology, 23(3): 401-407. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201503005
    He C Y, Xu Q, Ju N P, et al. 2018. Real-time early warning technology of debris flow based on automatic identification of rainfall process[J]. Journal of Engineering Geology, 26(3): 703-710. http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201803018
    Huang G W, Huang G W, Du Y, et al. 2018. A lowcost real-time monitoring system for landslide deformation with Beidou cloud[J]. Journal of Engineering Geology, 26(4): 1008-1016. http://en.cnki.com.cn/Article_en/CJFDTotal-GCDZ201804021.htm
    Huang J, Huang R Q, Ju N P, et al. 2015.3D WebGIS-based platform for debris flow early warning: A case study[J]. Engineering Geology, 197 : 57-66. doi: 10.1016/j.enggeo.2015.08.013
    Huang R Q, Huang J, Ju N P, et al. 2013. WebGIS-based information management system for landslides triggered by Wenchuan earthquake[J]. Natural Hazards, 65(3): 1507-1517. doi: 10.1007/s11069-012-0424-x
    Intrieri E, Gigli G, Mugnai F, et al. 2012. Design and implementation of a landslide early warning system[J]. Engineering Geology, 147-148 : 124-136. doi: 10.1016/j.enggeo.2012.07.017
    Liu C Z. 2019. Analysis methods on the risk identification of landslide disasters[J]. Journal of Engineering Geology, 27(1): 88-97. http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201901010
    Peng D L, Xu Q, Liu F Z, et al. 2018. Distribution and failure modes of the landslides in Heitai terrace, China[J]. Engineering Geology, 236 : 97-110. doi: 10.1016/j.enggeo.2017.09.016
    Peng D L, Xu Q, Zhang X L, et al. 2019. Hydrological response of loess slopes with reference to widespread landslide events in the Heifangtai terrace, NW China[J]. Journal of Asian Earth Sciences, 171 : 259-276. doi: 10.1016/j.jseaes.2018.12.003
    Peng J B, Lin H Z, Wang Q Y, et al. 2014. The critical issues and creative concepts in mitigation research of loess geological hazards[J]. Journal of Engineering Geology, 22(4): 684-691. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201404018
    Qi X. 2017. Sudden loess landslide monitoring and early warning research-a case study of Gansu landslide in Heifangtai loess[D]. Chengdu: Chengdu University of Technology.
    Wu F Q, Sha P. 2019. Achievements of engineering geology in China and the mission in the new era-A review on 2018 Annual Symposiun of Engineering Geology of China[J]. Journal of Engineering Geology, 27(1): 184-194. http://en.cnki.com.cn/Article_en/CJFDTotal-GCDZ201901020.htm
    Xu Q, Dong X J, Li W L. 2019. Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 44(7): 957-966. http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201907003
    Xu Q, Huang R Q, Li X Z. 2004. Research progress in time forecast and prediction of landslides[J]. Advance in Earth Sciences, 19(3): 478-483. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dqkxjz200403021
    Xu Q, Peng D L, Li W L, et al. 2016. Study on formation mechanism of diffuse failure landslide[J]. Journal of Southwest Jiaotong University, 51(5): 995-1004. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xnjtdxxb201605024
    Xu Q, Tang M G, Huang R Q. 2015. Monitoring, early warning and emergency disposition for large-scale landslides[M]. Beijing: Science Press.
    Xu Q, Tang M G, Xu K X, et al. 2008. Research on space-time evolution laws and early warning-prediction of landslides[J]. Chinese Journal of Rock Mechanics and Engineering, 27(6): 1104-1112. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb200806003
    Xu Q, Yuan Y, Zeng Y P, et al. 2011. Some new pre-warning criteria for creep slope failure[J]. Science China Technological Sciences, 54 (S1): 210-220. doi: 10.1007/s11431-011-4640-5
    Xu Q, Zeng Y P, Qian J P, et al. 2009. Study on a improved tangential angle and the corresponding landslide pre-warning criteria[J]. Geological Bulletin of China, 28(4): 501-505. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgqydz200904011
    Xu Q, Zheng G, Li W L, et al. 2018. Study on successive landslide damming events of Jiasha River in Baige Village on October 11 and November 3, 2018[J]. Journal of Engineering Geology, 26(6): 1534-1551.
    Xu Q. 2012. Theoretical studies on prediction of landslides using slope deformation process data[J]. Journal of Engineering Geology, 20(2): 145-151. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201202001
    Yin Y P, Wang H D, Gao Y L, et al. 2010. Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China[J]. Journal of Southeast Asian Applied Geology, 2(3): 170-184. doi: 10.1007%2Fs10346-010-0220-1
    Zhu X, Xu Q, Qi X, et al. 2017. A self-adaptive data acquisition technique and its application in landslide monitoring[C]//Miko M, Arbanas, Yin Y, et al. 2017. Advancing Culture of Living with Landslides. WLF2017. Springer, Cham.
    董文文, 朱鸿鹄, 孙义杰, 等. 2016.边坡变形监测技术现状及新进展[J].工程地质学报, 24(6): 1088-1095. doi: 10.13544/j.cnki.jeg.2016.06.007
    董秀军, 许强, 唐川, 等. 2015.滑坡位移-时间曲线特征的物理模拟试验研究[J].工程地质学报, 23(3): 401-407. doi: 10.13544/j.cnki.jeg.2015.03.005
    何朝阳, 许强, 巨能攀, 等. 2018.基于降雨过程自动识别的泥石流实时预警技术[J].工程地质学报, 26(3): 703-710. doi: 10.13544/j.cnki.jeg.2017-189
    黄观文, 黄观武, 杜源, 等. 2018.一种基于北斗云的低成本滑坡实时监测系统[J].工程地质学报, 26(4): 1008-1016. doi: 10.13544/j.cnki.jeg.2017-394
    刘传正. 2019.崩塌滑坡灾害风险识别方法初步研究[J].工程地质学报, 27(1): 88-97. doi: 10.13544/j.cnki.jeg.2019-009
    彭建兵, 林鸿州, 王启耀, 等. 2014.黄土地质灾害研究中的关键问题与创新思路[J].工程地质学报, 22(4): 684-691. doi: 10.13544/j.cnki.jeg.2014.04.014
    亓星. 2017.突发型黄土滑坡监测预警研究-以甘肃黑方台黄土滑坡为例[D].成都: 成都理工大学.
    伍法权, 沙鹏. 2019.中国工程地质学科成就与新时期任务-2018年全国工程地质年会学术总结[J].工程地质学报, 27(1): 184-194. doi: 10.13544/j.cnki.jeg.2018-407
    许强, 董秀军, 李为乐. 2019.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), 44(7): 957-966. http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201907003
    许强, 黄润秋, 李秀珍. 2004.滑坡时间预测预报研究进展[J].地球科学进展, 19(3): 478-483. doi: 10.3321/j.issn:1001-8166.2004.03.021
    许强, 彭大雷, 李为乐, 等. 2016.溃散性滑坡成因机理初探[J].西南交通大学学报, 51(5): 995-1004. doi: 10.3969/j.issn.0258-2724.2016.05.024
    许强, 汤明高, 黄润秋. 2015.大型滑坡监测预警与应急处置[M].北京:科学出版社.
    许强, 汤明高, 徐开祥, 等. 2008.滑坡时空演化规律及预警预报研究[J].岩石力学与工程学报, 27(6): 1104-1112. doi: 10.3321/j.issn:1000-6915.2008.06.003
    许强, 曾裕平, 钱江澎, 等. 2009.一种改进的切线角及对应的滑坡预警判据[J].地质通报, 28(4): 501-505. doi: 10.3969/j.issn.1671-2552.2009.04.011
    许强, 郑光, 李为乐, 等. 2018.2018年10月和11月金沙江白格两次滑坡-堰塞堵江事件分析研究[J].工程地质学报, 26(6): 1534-1551. doi: 10.13544/j.cnki.jeg.2018-406
    许强. 2012.滑坡的变形破坏行为与内在机理[J].工程地质学报, 20(2): 145-151. doi: 10.3969/j.issn.1004-9665.2012.02.001
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出版历程
  • 收稿日期:  2019-01-21
  • 修回日期:  2019-10-29
  • 刊出日期:  2020-02-25

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