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大渡河瀑布沟水库红岩子滑坡变形特征与机理分析

李忠文, 李俊峰, 张小琼, 杨宇驰, 周平根, 韩冰

李忠文,李俊峰,张小琼,等. 大渡河瀑布沟水库红岩子滑坡变形特征与机理分析[J]. 中国地质灾害与防治学报,2023,34(4): 1-10. DOI: 10.16031/j.cnki.issn.1003-8035.202212015
引用本文: 李忠文,李俊峰,张小琼,等. 大渡河瀑布沟水库红岩子滑坡变形特征与机理分析[J]. 中国地质灾害与防治学报,2023,34(4): 1-10. DOI: 10.16031/j.cnki.issn.1003-8035.202212015
LI Zhongwen,LI Junfeng,ZHANG Xiaoqiong,et al. Deformation characteristics and reactivation mechanism of Hongyanzi landslide in Pubugou reservoir area of the Dadu River[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4): 1-10. DOI: 10.16031/j.cnki.issn.1003-8035.202212015
Citation: LI Zhongwen,LI Junfeng,ZHANG Xiaoqiong,et al. Deformation characteristics and reactivation mechanism of Hongyanzi landslide in Pubugou reservoir area of the Dadu River[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4): 1-10. DOI: 10.16031/j.cnki.issn.1003-8035.202212015

大渡河瀑布沟水库红岩子滑坡变形特征与机理分析

基金项目: 国家重点研发计划项目“复杂山区地质灾害监测预警北斗集成系统研发”(2021YFC3000500);国家级地质环境监测与预报项目(121201014000150003);自然资源部科技人才项目“智能传感与风险预警关键技术赋能全国地质灾害动态监测网络构建战略研究”(121106000000180039-2201)
详细信息
    作者简介:

    李忠文(1998-),男,江西赣州人,硕士研究生,主要从事地质灾害方面的研究。E-mail:2943371193@qq.com

    通讯作者:

    韩 冰(1981-),男,河北邯郸人,博士,正高级工程师,主要从事地质灾害监测预警方面的研究。E-mail:hanbing@mail.cgs.gov.cn

  • 中图分类号: P642.22

Deformation characteristics and reactivation mechanism of Hongyanzi landslide in Pubugou reservoir area of the Dadu River

  • 摘要: 库水涨落常诱发库岸滑坡变形破坏。为了研究库岸滑坡的变形特征及变形机理,以大渡河瀑布沟水电站红岩子滑坡为对象,通过详细的地表宏观变形调查和对监测数据的深入分析,结合GeoStudio数值模拟,深入研究了该滑坡的变形特征、渗流场、稳定性及库水对滑坡的作用机理。结果表明:红岩子滑坡地表宏观变形显著,累计位移曲线呈“阶跃”式特征,库水下降是滑坡变形的主要诱发因素;库水位由850 m高水位集中下降至830 m以下时,位移阶跃启动,“阶跃”段的累计变形量占全年总变形量的90%以上,当库水位下降速率大于0.5 m/d时,滑坡加速变形;滑坡变形模式为蠕滑-拉裂,库水升降导致滑体内部渗透力的变化,对滑坡稳定性影响很大,引发滑坡“阶跃”变形。
    Abstract: Reservoir water fluctuation can often trigger deformation and failure of reservoir bank landslides. This study focuses on the Hongyanzi landslide in the Pubugou hydropower Station of the Dadu River to investigate the deformation characteristics and deformation mechanisms of the reservoir bank landslides. Through detailed surface macro deformation surveys, monitoring data analysis, and GeoStudio numerical simulation, the deformation characteristics, seepage field, stability and the influence of reservoir water on landslide were studied in depth. The research revealed significant macro deformation of the Hongyanzi landslide surface, with a cumulative displacement curve exhibiting a “step-like” characteristic. The primary inducing factor of landslide deformation was found to be the decrease in the reservoir water level. When the reservoir water level dropped from 850 m to below 830 m, the displacement step was triggered, and the cumulative deformation of the “step” segment accounted for over 90% of the total annual deformation. Accelerated deformation of the landslide occurred when the rate of the reservoir water level decline was greater than 0.5 m/d. The landslide deformation mode was identified as creep slip-tensile cracking, with the rise and fall of the reservoir water level significantly impacting the internal permeability of the sliding body and causing a large impact on the landslide’s stability, leading to the “step-like” deformation of the landslide.
  • 强降雨多发生在每年7—8月,降雨因素为该时间段地质灾害发生的主要影响因素。据历史数据统计,中国约60%的突发地质灾害的发生与降雨强度及累计降雨量密切相关,突发地质灾害集中发生在每年暴雨多发的汛期[1]

    降雨型滑坡预测预报研究一直以来受到国内外学者的关注,研究方向可分为滑坡破坏机理分析和预警预报研究两方面。降雨型滑坡预警预报研究方法包括试验分析法、数值模拟法和数学统计法。黄润秋等[2]通过室内模型试验揭示了降雨型滑坡随着降雨量的增大,滑坡岩土体孔隙水压力逐渐升高,最终形成滑坡,揭示了降雨型滑坡存在降雨阈值的根本原因。Guzzetti等[3]、Sharir等[4]认为降雨型滑坡的发生通常与临界降雨量有关,若超过此雨量界限,可能会发生滑坡。

    根据分析对象不同,数学统计法又分为滑坡位移与降雨相关性分析法[5]、滑坡结构与降雨相关性分析法[6]、经验统计模型法[7]。甘肃省地质构造复杂,崩塌、滑坡、泥石流等地质灾害非常发育。按年度统计,降雨引发的地质灾害占比约60%以上,最高可达99%(图1)。甘肃省以区域降雨统计模型、滑坡24 h趋势预警模型、泥石流预警模型均属于以数学统计为主的第一代预警模型,在地质灾害气象风险预警中发挥了重要作用[810]

    图  1  2013—2023年甘肃省降雨引发的地质灾害占年度地质灾害的比例图
    Figure  1.  Proportion of geological disasters caused by rainfall in annual geological disasters from 2013 to 2023 in Gansu Province

    目前,甘肃省的地质灾害气象风险预警模型研究仍处于第一代隐式统计模型向第二代显示统计模型的过渡阶段[11],预警模型研究及应用相对滞后,精度有待提高。近年来,甘肃省地质灾害气象风险预警模型研究主要集中在地质灾害高发、频发的白龙江流域,且以泥石流灾害预警模型研究为主。比如王高峰等[12]选取泥石流危险性评价因子:泥石流沟的规模、主沟纵比降、沟谷发育密度、物源区沟道纵比降,通过综合分析研究,得出了自然沟谷发生泥石流灾害的定量评价模型,为中小型泥石流预警预报提供思路,而针对斜坡类灾害的预警模型研究相对较少。

    本文以白龙江流域降雨型滑坡为研究对象,针对不同岩性特征的滑坡,建立了不同概率等级下的滑坡发生时事件降雨量(event rainfall)与降雨历时(duration of rainfall)之间的关系模型,以下简称ED模型,为不同岩性类型的斜坡在降雨作用下发生滑坡的阈值研究提供新思路。

    白龙江流域甘肃段包含5个县(区),位于长江上游,区内植被发育,受新生代印度—亚洲板块挤压作用影响,断裂构造变形明显[13]。区内山大沟深,地形地貌复杂、岩土体类型多样、新构造活动强烈、生态环境脆弱,加之地震活动频繁、降雨集中、暴雨频发,建设用地紧张、发展与环境保护矛盾突出。滑坡、泥石流灾害的暴发,不仅严重威胁当地人民的生命财产,也严重制约社会经济发展。

    研究区是中国滑坡、泥石流四大高易发区之一,开展区内降雨引发滑坡灾害预警模型研究意义重大。据统计,截至2023年底,研究区共发育地质灾害隐患点5035处,占当年全省隐患点总数的22.97%,隐患点密度为0.24处/km2;按类型划分,滑坡2776处,崩塌910处,泥石流1341处,地裂缝1处,地面塌陷7处;按行政区划分,宕昌380处,武都2251处,文县1530处,迭部401处,舟曲473处。

    本研究以2000—2019年研究区发生的滑坡灾害数据为基础。结合自然资源部门地质灾害隐患点台账、县(区)地质灾害区划报告资料、地质灾害调查报告、县志等资料,同时采用多期遥感数据对比分析、室内解译、野外调查、访问、取样、测试等手段,修正补充已有的滑坡灾害台账数据,结合前期降雨事件分析比对,最终整理形成128个因降雨引发滑坡的记录,资料详细记录了滑坡事件发生的地点、时间、类型、成因等,数据较为可靠,成为本次研究的对象。经分析,该类滑坡主要分布在白龙江两岸的山坡地带,在6—9月多发,占比为74%,7月下旬及8月上旬,滑坡发生数量达到峰值,其余月份滑坡发生的数量较少,滑坡的发生数量与降雨量和降雨强度基本吻合[14],与当地灾害发生的规律相符(图23)。

    图  2  累计月降雨量与滑坡数量的相关关系
    Figure  2.  Correlation between cumulative monthly average rainfall and number of landslides
    图  3  累计月降雨量与滑坡数量的相关性分析图
    Figure  3.  Correlation analysis diagram between cumulative monthly rainfall and number of landslides

    按照滑坡岩性特征将滑坡分为较硬、极软、软硬相间三种类型。对于每种类型的滑坡,采用频数法,分别计算不同降雨事件下滑坡发生的概率,即不同岩性类型的滑坡发生前的事件雨量和降雨历时关系,基于频率法分别获得不同概率条件下ED降雨阈值曲线[1516],建成滑坡发生概率预警模型。

    2010年Brunetti等[15]发表的论文中指出基于频率法的ED降雨阈值符合幂律法则:

    E=(α±Δα)D(γ±Δγ)

    式中:E——事件雨量/mm;

    D——降雨持续时间/d;

    α——截距,Δα为与α相关的变量;

    γ——指数,Δγ为与γ相关的变量。

    假设选择一组滑坡数据,以滑坡发生的概率为5%、20%和50% 3种情况下,获得对应的截距和指数,绘制3条曲线,将滑坡事件分为4个区间。即,位于5%概率线以下的点表示该降雨事件下的雨量及降雨历时引发滑坡的概率小于5%。如图4所示,通过对滑坡降雨事件数据统计分析,分别获取滑坡发生概率为5%、20%和50%的ED关系曲线,其中,RLs为诱发滑坡的降雨事件,NRLs为未诱发滑坡的降雨事件。根据样本数据中滑坡发生概率,采用数据拟合方法,计算获得截距和指数γ±Δγ取0.64±0.09,概率为5%的直线关系(T5)为E=(5.01±0.06)·D(0.64±0.09),概率为20%的直线关系为(T20)E=(7.08±0.67)·D(0.64±0.09),概率为50%的直线关系(T50)为E=(15.14±1.15)·D(0.64±0.09)ɑ取值介于2.07~16.29,D取值介于1~40 d,随着滑坡事件概率的增大,相对不确定性增加,ED之间的关系趋于离散[17]。通过对本次研究中数据的分析,在概率为50%的直线关系中,其相对不确定度为7.6%;在概率为20%的直线关系中,其相对不确定度为9.5%;在概率为5%的直线关系中,其相对不确定度为1.2%。从图2中可以看出,概率为50%的曲线关系中,其相对不确定度较低,说明数据较为集中[18]

    图  4  概率为T5、T20、T50的阈值曲线
    Figure  4.  Threshold curves for T5,T20,and T50 obtained by frequency method

    根据地层年代、岩体工程性质特征等因素综合考虑,将区域内地层岩性按照软弱程度进行分类,分类标准详见表1。通过岩性分类结果与128处滑坡样本空间分布进行对比,得出松散物质、软硬相间、极软三种类型的岩性中滑坡灾害多发,其中,有72起滑坡分布在松散层内,岩土体类型主要为第四系残坡积碎石土、粉质黏土、强风化千枚岩、砾石,占比约56.25%;有37起滑坡分布在软硬相间的岩性中,岩体类型主要有板岩、千枚岩、浅变质砂岩、砂岩与千枚岩互层岩体,占比约28.91%;有12起滑坡分布极软的岩组中,岩性主要是新近系砾岩、页岩、泥质砂岩等,占比9.38%[1920]。而坚硬、较软、较硬三种岩性类型中滑坡分布数量为7起,数量较少,滑坡降雨阈值曲线的拟合效果差,因此此处不做分析(图5)。

    表  1  岩性类型的划分标准
    Table  1.  Classification Standards for Lithological Types
    软硬类型 主要岩性类型
    极软 层状碎屑岩:古近系砾岩;新近系砾岩、页岩、泥质砂岩
    坚硬 块状岩浆岩:花岗岩、辉绿岩、辉长岩、闪长岩、
    闪长玢岩、闪斜煌斑岩等
    较软 层状碎屑岩:白垩系砾岩、砂岩、泥岩
    较硬 层状碳酸盐岩:三叠系、二叠系灰岩、砂岩、页岩等
    泥盆系板岩、砂岩、页岩、灰岩等
    软硬相间 ①层状变质岩:二叠系砂岩、砂质板岩、凝灰岩、千枚岩;
    志留系砂岩、石灰岩、千枚岩、板岩
    ②层状碳酸盐岩:泥盆系板岩、千枚岩、灰岩
    ③层状碎屑岩:侏罗系砂岩、泥岩、砾岩、页岩
    松散物质 第四系残坡积碎石土、粉质黏土、强风化千枚岩、砾石
    下载: 导出CSV 
    | 显示表格
    图  5  降雨型滑坡岩土类型分类图
    Figure  5.  Classification diagram of rainfall-induced landslides in different rock and soil types

    采用频数法对不同岩体类型的滑坡进行分析,得到概率分别为15%(低)、25%(中)、40%(高)、60%(极高)时,降雨ED曲线(图6),以曲线为下限,将曲线上部4个区间自下而上依次对应定义为低风险区、中风险区、高风险区、极高风险区4个预警等级,即降雨事件雨量与降雨历时所对应的点落入4个区间中的某一个,即判定该滑坡的风险等级为该区间的风险等级。

    图  6  不同岩性类型滑坡不同预警等级的降雨阈值曲线
    Figure  6.  Rainfall threshold curves for different warning levels of landslides with different lithological types

    3种岩性类型的滑坡不同预警等级下限的降雨阈值曲线分别如下:

    松散物质:E=6.43D0.72P=15%,蓝色预警)、E=7.94D0.72P=25%,黄色预警)、E=10.91D0.72P=40%,橙色预警)、E=18.79D0.72P=60%,红色预警)。

    极软岩类:E=9.25D0.54P=15%,蓝色预警)、E=12.30D0.54P=25%,黄色预警)、E=18.88D0.54P=40%,橙色预警)、E=31.48D0.54P=60%,红色预警)。

    软硬相间:E=9.79D0.46P=15%,蓝色预警)、E=11.16D0.54P=25%,黄色预警)、E=15.00D0.46P=40%,橙色预警)、E=21.09D0.46P=60%,红色预警)。

    图6中可知,松散层滑坡降雨阈值曲线的间距较小,不同预警等级临界累计降雨量差值最小,降雨量对松散层滑坡作用较快。极软岩类滑坡降雨阈值曲线的间距较大,不同预警等级临界累计降雨量差值较大,降雨量对滑坡发生反映慢。软硬相间岩类滑坡降雨阈值曲线的间距中等,不同预警等级临界累计降雨量差值中等,降雨量对滑坡发生反映中等。按照12 d降雨历时,计算得不同岩性类型斜坡分别在4种预警等级下的下限临界累计雨量值(表2)。

    表  2  不同岩性类型的斜坡在各预警等级下发生滑坡前不同降雨历时下的累计雨量
    Table  2.  Duration and cumulative rainfall before landslides occur on slopes of different rock types at different warning levels /mm
    滑坡类型 预警等级 降雨历时/d
    1 2 3 4 5 6 7 8 9 10 11 12
    松散物质 低(P=15%) 6.43 10.59 14.18 17.45 20.49 23.36 26.10 28.74 31.28 33.75 36.14 38.48
    中(P=25%) 7.94 13.08 17.51 21.54 25.30 28.85 32.23 35.49 38.63 41.67 44.63 47.52
    高(P=40%) 10.91 17.97 24.06 29.60 34.76 39.64 44.29 48.76 53.07 57.26 61.32 65.29
    极高(P=60%) 18.79 30.95 41.44 50.98 59.87 68.26 76.28 83.98 91.41 98.61 105.62 112.44
    极软岩类 低(P=15%) 9.25 13.45 16.74 19.55 22.06 24.34 26.45 28.43 30.30 32.07 33.77 35.39
    中(P=25%) 12.30 17.88 22.26 26.00 29.33 32.37 35.18 37.81 40.29 42.65 44.90 47.06
    高(P=40%) 18.88 27.45 34.17 39.91 45.02 49.68 54.00 58.03 61.84 65.46 68.92 72.24
    极高(P=60%) 31.48 45.77 56.97 66.55 75.07 82.84 90.03 96.76 103.12 109.15 114.92 120.45
    软硬相间 低(P=15%) 9.79 13.47 16.23 18.52 20.53 22.32 23.96 25.48 26.90 28.23 29.50 30.70
    中(P=25%) 11.61 15.97 19.24 21.97 24.34 26.47 28.42 30.22 31.90 33.48 34.98 36.41
    高(P=40%) 15.00 20.63 24.86 28.38 31.45 34.20 36.71 39.04 41.21 43.26 45.20 47.05
    极高(P=60%) 21.09 29.01 34.96 39.90 44.22 48.09 51.62 54.89 57.95 60.82 63.55 66.15
    下载: 导出CSV 
    | 显示表格

    本文收集了2020年陇南“8•17”暴洪灾害期间59起滑坡信息及前期降雨资料,其中,滑坡发生与8月11—17日,降雨数据为滑坡附近雨量站点8月5—18日逐日累计降雨数据,共计372条。按照3种岩性类型,分别与上述不同预警等级的降雨阈值曲线对比分析,检验模型的准确性。

    根据滑坡事件分析,滑坡多在降雨持续6 d后集中暴发。松散物质类滑坡共计38起,其中,25起滑坡发生前降雨事件位于极高风险区,占比约65.79%;10起位于高风险区(40%≤P<60%),占比约7.89%;3起位于中风险区(25%≤P<40%),占比约0。极软岩类滑坡事件共计9起,其中,8起滑坡时降雨事件位于极高风险区(P≥60%),占比约88.89%;1起位于高风险区(40%≤P<60%),占比约11.11%;中、低风险区(P<40%)无滑坡发生。软硬相间滑坡事件共计12起,其中,10起滑坡降雨事件位于极高风险区(P≥60%),占比约83.33%;1起位于高风险区(40%≤P<60%),占比约8.33%;1起位于低风险区(40%≤P<60%),占比约8.33%;中风险区无滑坡发生(表3图7)。

    表  3  不同岩性类型滑坡事件对应的预警等级
    Table  3.  Warning levels corresponding to landslide events of various lithologic types
    滑坡类型 预警等级 滑坡/处 事件比例/%
    松散物质 低(P<25%) 0 0
    中(25%≤P<40%) 3 7.89
    高(40%≤P<60%) 10 26.32
    极高(P≥60%) 25 65.79
    极软岩类 低(P<25%) 0 0
    中(25%≤P<40%) 0 0
    高(40%≤P<60%) 1 11.11
    极高(P≥60%) 8 88.89
    软硬相间 低(P<25%) 1 8.33
    中(25%≤P<40%) 0 0
    高(40%≤P<60%) 1 8.33
    极高(P≥60%) 10 83.33
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    综上所述,位于极高风险预区的降雨事件,比例最低的为松散物质类滑坡,占比65.79%,其次为软硬相间滑坡,占比83.33%,最高为极软岩类滑坡,占比88.89%,但都大于60%,因此,极高风险阈值曲线基本准确。

    图  7  滑坡发生前降雨事件与降雨阈值曲线对应关系
    Figure  7.  Correspondence between rainfall events and rainfall threshold curves before landslides

    (1)白龙江流域甘肃段地质灾害数量多,严重威胁当地群众的生命财产安全,制约社会经济发展,针对降雨引发斜坡类灾害研究较少,本文为开展该区地质灾害预警预报模型研究提供了新思路。

    (2)基于频率法建立了白龙江流域不同岩性特征的滑坡降雨阈值ED模型,并给出了累计雨量下限阈值,对白龙江流域斜坡类灾害预警预报具有指导意义。

    (3)通过2020年陇南“8•17”暴洪灾害期间,降雨引发的59起滑坡事件前期降雨量分析对比,引发滑坡的降雨事件约65.79%以上均位于极高风险预警区,与极高风险(P>60%)阈值曲线一致。

    (4)本文获取的滑坡下限降雨预警曲线,只能通过已发生的滑坡灾害结合前期降雨事件来验证模型准确性,对滑坡发生前的预警曲线校验存在困难,下一步研究中应考虑滑坡发生前不同风险等级预警模型或阈值,为斜坡类地质灾害降雨预警预报提供依据。

  • 图  1   滑坡工程地质平面图及监测布置图

    Figure  1.   Geological contour map and monitoring layout of the landslide

    图  2   滑坡工程地质剖面图

    Figure  2.   Geological cross-section profile of the landslide

    图  3   2013—2015年监测点水平-垂直累计位移图

    Figure  3.   Total horizontal and vertical cumulative displacement map of monitoring points from 2013—2015

    图  4   2013—2015年各监测点水平位移方向曲线图

    Figure  4.   Horizontal displacement curve of monitoring points from 2013—2015

    图  5   监测点累计位移-库水位-降雨图

    Figure  5.   Cumulative displacement - reservoir water level - rainfall relationship diagram of monitoring points

    图  6   滑坡变形速率与库水位变化速率曲线图

    Figure  6.   Curve of landslide deformation rate and reservoir water level change rate

    图  8   滑坡二维模型

    Figure  8.   Two-dimensional model of the landslide

    图  7   滑坡2日平均库水变化值与2日平均位移增量关系图

    Figure  7.   The relationship between the variation value of the two-day average reservoir water and the increment of the two-day average displacement on the slope

    图  9   降雨条件下滑坡渗流图

    Figure  9.   Seepage diagram of the landslide under rainfall conditions

    图  10   降雨条件下J04、J05孔隙水压

    Figure  10.   Pore water pressure distribution at monitoring points J04 and J05 under rainfall conditions

    图  11   不同时期滑坡渗流场及孔隙水压力(单位:kPa)

    Figure  11.   Landslide seepage field and pore water pressure distribution map at different periods(unit:kPa)

    图  12   库水作用下监测点J01、J02、J03孔隙水压

    Figure  12.   Pore water pressure distribution at monitoring points J01, J02 and J03 under the action of reservoir water

    图  13   滑坡稳定系数变化曲线图

    Figure  13.   Change curve of landslide stability coefficient

    表  1   滑坡变形加速阶段库水下降速率均值

    Table  1   Summary table of mean decline rate of reservoir water during landslide deformation acceleration phase

    年份日期库水下降速率/(m·d−1
    20133月1日—3月22日0.76
    3月27日—4月12日0.72
    4月15日—4月17日0.87
    20143月2日—3月9日0.59
    4月4日—4月10日0.67
    4月15日—4月25日0.91
    20181月23日—2月10日0.85
    3月27日—3月31日0.56
    20192月22日—3月2日0.72
    3月22日—4月2日0.89
    4月26日—4月29日0.70
    下载: 导出CSV

    表  2   滑坡物理力学参数

    Table  2   Physical and mechanical parameters of the landslide

    岩性重度
    /(kN·m−3
    黏聚力
    /kPa
    内摩擦角
    /(°)
    渗透系数
    /(m·d−1
    堆积层2020150.450
    基岩263200440.001
    下载: 导出CSV

    表  3   滑坡计算参数

    Table  3   Landslide calculation parameters

    编号模拟工况荷载组合
    I降雨自重+2013年真实降雨
    II降雨+库水自重+2013年真实降雨+2013年库水位
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出版历程
  • 收稿日期:  2022-12-26
  • 修回日期:  2023-04-10
  • 录用日期:  2023-05-03
  • 网络出版日期:  2023-05-10
  • 刊出日期:  2023-08-21

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