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独库高速公路克扎依—巩乃斯段雪崩易发性评价

程秋连, 刘杰, 杨治纬, 张天意, 王斌

程秋连,刘杰,杨治纬,等. 独库高速公路克扎依—巩乃斯段雪崩易发性评价[J]. 中国地质灾害与防治学报,2024,35(1): 60-71. DOI: 10.16031/j.cnki.issn.1003-8035.202302009
引用本文: 程秋连,刘杰,杨治纬,等. 独库高速公路克扎依—巩乃斯段雪崩易发性评价[J]. 中国地质灾害与防治学报,2024,35(1): 60-71. DOI: 10.16031/j.cnki.issn.1003-8035.202302009
CHENG Qiulian,LIU Jie,YANG Zhiwei,et al. Avalanche susceptibility evaluation of the Kezhayi to Gongnaisi section of the Duku expressway[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 60-71. DOI: 10.16031/j.cnki.issn.1003-8035.202302009
Citation: CHENG Qiulian,LIU Jie,YANG Zhiwei,et al. Avalanche susceptibility evaluation of the Kezhayi to Gongnaisi section of the Duku expressway[J]. The Chinese Journal of Geological Hazard and Control,2024,35(1): 60-71. DOI: 10.16031/j.cnki.issn.1003-8035.202302009

独库高速公路克扎依—巩乃斯段雪崩易发性评价

基金项目: 交通运输行业重点科技项目(2022-ZD6-090);新疆交通运输科技项目(2022-ZD-006);新疆交投集团2021年度“揭榜挂帅”科技项目(ZKXFWCG2022060004);新疆交通设计院科技研发项目(KY2022021501)
详细信息
    作者简介:

    程秋连(1998—),女,硕士研究生,主要从事公路冰雪灾害防治方面的工作。E-mail:cql878583@163.com

    通讯作者:

    刘 杰(1986—),男,博士,高级工程师,主要从事公路冰雪灾害防治方面的工作。E-mail:hfutliujie@163.com

  • 中图分类号: P642.21

Avalanche susceptibility evaluation of the Kezhayi to Gongnaisi section of the Duku expressway

  • 摘要:

    独库高速公路克扎依—巩乃斯段以高山地貌为主,地形切割剧烈,为雪崩发育提供了有利的地形条件,对该区域进行雪崩易发性评价是独库高速公路安全建设及运行的重要前提。通过遥感解译和现场调查等手段获取149个雪崩点的因子数据,通过对因子进行相关性检测,筛选出10个评价因子,构成雪崩评价因子体系。在此基础上,运用K均值聚类法和随机法提取出非雪崩点和原始雪崩点构成样本集,通过机器学习中的多层感知器、支持向量机算法对研究区域开展雪崩易发性评价。研究结果表明,随机法和K均值聚类法提取出的样本集分别带入算法中训练,R-SVM、R-MLP、K-SVM、K-MLP四种模型的Kappa系数均大于0.6,4组模型对验证数据集的预测结果与实际值存在高度的一致性。经多层感知器训练的AUC值由0.762提高至0.983,经支持向量机训练的AUC值由0.724提高至0.951。基于本研究预测性能最佳的K-MLP模型分区显示该研究区雪崩发育对拟建线路影响较小,但对于隧道洞口可能会造成威胁。本研究可为独库高速公路建设、运营以及雪崩灾害防治工作提供理论支撑和方法参考。

    Abstract:

    The Kezhayi to Gongnaisi section of the Duku expressway is predominantly characterized by alpine landforms, with steep terrain cutting that provides conducive conditions for avalanche development. The study on the evaluation of snow avalanche susceptibility in this area is a crucial prerequisite for the safety construction and operation of the Duku expressway. The 149 snow avalanche points were collected by employing remote sensing interpretation and field investigations. Through correlation analysis of these factors, 10 evaluation factors were selected, forming the avalanche evaluation factor system. Subsequently, the non-avalanche points and original avalanche points were extracted using the K-means clustering method and random method to create a sample set. Machine learning techniques, including multilayer perceptron (MLP) and support vector machine (SVM) algorithms, were utilized to assess avalanche susceptibility in the study area. The results show that the sample datasets extracted by the random and K-means clustering methods were used for training, the Kappa coefficient of the R-SVM, R-MLP, K-SVM, and K-MLP models were greater than 0.6. These four sets of models exhibited a high degree of consistency between the predicted results and actual values of the validation dataset. The AUC (area under curve) value trained by MLP increased from 0.762 to 0.983, while the AUC value trained by SVM increased from 0.724 to 0.951. Based on the K-MLP model partition with the highest evaluation accuracy, the snow avalanche development in the research area has a relatively minor impact on the proposed route but may pose a threat to tunnel entrances. This study provides theoretical support and methodological references for the construction, operation and mitigation of sonw avalanche disasters for the Duku expressway.

  • 河南某金矿属全国16个重点成矿带中的豫西成矿带。已探明矿种钼、钨、铅、金、铁等40多种,矿产种类多,开采价值大,矿业活动带来巨大经济价值,也引发矿渣型泥石流等地质灾害问题[1-7],2010年7月24日当地普降暴雨,境内共发生矿渣型泥石流29次,死亡68人,失踪21人,经济损失19.8亿,教训非常惨重。

    目前对矿渣型泥石流的研究主要体现在成灾模式、启动机理、危险评价等方面,邓龙胜等[8]通过计算洪峰流量,评价了矿渣型泥石流的泥沙携带力、冲击力以及揭底深度;李荣等[9]、陈媛儿[10]、谢鉴衡[11]、秦荣昱[12]、彭润译等[13]从沙粒启动的水动力条件入手得出非均匀沙的起动流速公式,与实际情况吻合;林玫玲等[14]采用PFC2D仿真软件,揭示矿渣颗粒转化为泥石流时的内部力学特征与降雨强度的大小关系;李建林等[15]通过研究矿渣泥石流的沟谷形态得出沟道比降、汇水面积和沟道长度三个因素中汇水面积对其发育和行成的影响最为显著;唐亚明等[16]模拟了特定雨力下,泥石流的冲击范围,并引入泥石流危险因子做了危险程度的分区评价,提出了在渣堆处修建挡墙等工程治理措施;杨敏等[17]、徐友宁等[18]对潼关金矿区矿渣堆数目、体积、稳定性进行实地调研,并提出对废渣堆进行资源化利用等防治措施。前人研究成果均提到了矿渣泥石流是由废弃渣堆引起,并提出治理渣堆的必要性,但并没有对渣堆危险性高低进行分类评价,也没有提出精准合理的防治措施。

    文中在以豫西某金矿区大南沟、后木寺沟16个渣堆为研究对象进行分析阐述,和前人相同之处是借鉴了启动流速(Uc[9-13]以及《桥涵水文》第五版[19]中洪峰流量(Q)的计算公式,不同之处在于①考虑渣堆阻塞行洪通道等因素,进一步计算出渣堆断面处的泄洪流速(Us),并结合启动流速(Uc)计算稳定性大小即Fs=Uc/Us;②考虑渣堆之间的相互影响,分析不同重现期雨力条件下,单个计算渣堆失稳转化成泥石流时的危险系数;③将渣堆的危险高低进行精细计算,科学归类。以期达到精准分类,科学防治、经济节约等目的。

    豫西某金矿地处秦岭山脉东段,熊耳山西南部,伏牛山西段北部(图1),气候属暖温带半湿润大陆性季风气候,降雨量大且集中。海拨最高1671.4 m,最低1000 m,坡度较陡,区内地形切割强烈,沟谷呈“V”字型;植被覆盖度高,草木茂盛,基岩裸露较差。

    图  1  豫西某金矿区域位置图
    Figure  1.  The gold mine location in the western Henan

    该区域出露岩性主要为安山岩、流纹斑岩、片麻岩、冲洪积物。马超营断裂发育演化,共经历6期次地质活动,7次构造事件[19],其间热液侵入成矿,该金矿床位于马超营区域性断裂带与北东向上宫—星星印断裂带的交汇部位,从1979年建矿开采至今已有40余年历史。开采规模25×104 t/a,地下开采,开采规模大,废石渣、矿渣多且堆放不合理,严重阻塞沟道,在降雨条件下极易失稳形成泥石流。

    因马超营断裂(图1)6期次的构造活动(嵩阳发展—中远古形成—后期改造)[19],在强烈复杂的构造活动过程中形成有利于沟谷型泥石流发育的“哑铃状”特殊地形(图2):即两头(形成区、堆积区)呈“喇叭状”,中间(流通区)狭窄,该区域西高东低,相对高差670 m,沟谷总长度14 km,物源区平均纵坡降170‰,最大纵坡降377‰。

    图  2  豫西某金矿泥石流形态特征图
    Figure  2.  Morphological characteristic map of the debris flow in western Henan

    豫西某金矿矿区岩石力学性质分别为安山岩、流纹斑岩抗压强度64~97 MPa,片麻岩抗压强度659 MPa,为坚硬块状岩体,不易风化,岩层层面、贯通的断裂结构面倾向与坡面反向,不具备发生大规模崩塌滑坡的可能性,且在现场调查过程中山坡的风化层较薄,仅在山麓、沟谷中下游可见坡积物、冲洪积物,未见大范围的崩积物,因此自然条件下发生泥石流的可能性较小。现场测量图2中的1-1′剖面,得出剖面图如图3所示。

    图  3  豫西某金矿工程地质剖面图
    1—安山岩;2—冲积物;3—流纹斑岩;4—破碎带;5—金矿脉;6—正长斑岩;7—断层;8—砂岩; 9—片麻岩;10—不整合接触
    Figure  3.  Engineering geological plan of the gold mine in western Henan

    豫西某金矿常期以民采为主,大量的围岩因不具加工价值而沿坡面、沟道随意堆弃,这些堆积物自身稳定性差,在降雨等条件下容易失稳。据现场调查统计了渣堆16处,总计体积12.05×104 m3,均有可能失稳致灾。各渣堆的分布位置及其他参数如图24表1所示。

    图  4  豫西某金矿渣堆分布图
    Figure  4.  Distribution map of the gold slag heaps in western Henan
    表  1  豫西某金矿渣堆体积及压占沟谷比例统计表
    Table  1.  Statistical table of volume and proportion of the slag in a gold slag pile in western Henan
    矿渣ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    体积/(104 m31.20.090.120.070.140.50.051.4
    压占沟谷比例/%5043836972717448
    矿渣ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    体积/(104 m322.51.20.290.40.30.251.54
    压占沟谷比例/%7455777160674988
    下载: 导出CSV 
    | 显示表格

    豫西某金矿区渣堆厚度一般在2~7 m,平均厚度4.3 m,少数可达12 m,渣堆均不同程度堵塞沟道,有的在沿山坡呈阶梯状堆积,部分位于沟谷左侧,部分位于沟谷右侧,密实度差,渣堆顶部颗粒较细,底部颗粒较粗,分选差,棱角明显。渣堆不同程度堵塞沟道,有的在沿山坡呈阶梯状堆积,在沟谷底部部分占压行洪通道(图5),有的沿沟谷底部堆积,几乎全部占压行洪通道(图6);现场量测各渣堆体积以及压占沟谷比例结果见表1

    图  5  后木寺ZD2渣堆堵塞沟道示意图
    Figure  5.  Schematic diagram of Houmusi ZD2 slag pile blocking the channel
    图  6  大南沟渣堆ZD16堵塞沟道示意图
    Figure  6.  Schematic diagram of Danangouslag ZD16 pile blocking the channel

    豫西某金矿区降雨多集中在7—9月,年降水量最高1386.6 mm,最少403.3 mm,月最大降水量423.4 mm,24 h最大降水量159.2 mm。根据当地气象局实际观测近10a最大降水量49 mm/h,查阅资料《桥涵水文》[19]可知当地100 a一遇降雨量90 mm/h、50 a一遇降雨量为80 mm/h,25 a一遇降雨量为60 mm/h,充沛的降雨为泥石流的启动提供了水动力条件,历年7—9月实测降雨量变化曲线见图7

    图  7  豫西某金矿历年降雨量
    Figure  7.  Rainfall in western Henan gold mine over the years

    文中先不考虑渣堆之间的相互影响,计算分析16处渣堆的稳定性大小,然后在根据计算出的稳定性大小分析其相互影响关系,对渣堆稳定性进行修正,最终计算出考虑相互影响后的稳定性大小。

    豫西某金矿的主要诱发条件为短时强降雨、所以降雨引发洪峰流量可按下式计算[20]

    $$ Q = 0.278\left(\frac{{{S _{\rm{P}}}}}{{{\tau ^n}}} - \mu \right)F $$ (1)
    $$ \tau = {{{K}}_{\text{3}}}{\left(\frac{L}{{\sqrt I }}\right)^{\alpha _1^{}}} $$ (2)
    $$ \mu ={K}_{\text{1}}({S} _{{\rm{P}}})^{{\beta }_{1}} $$ (3)

    式中:$ Q $——洪峰流量/(m3·s−1);

    Sp——雨力/(mm·h−1);

    τ——汇流时间/s;

    n——暴雨递减指数,取0.45;

    µ——损失参数,取15.85 mm/h;

    F——汇流面积/km2

    K3——地区参数,取0.63;

    L——主河道长度/km;

    I——主河道平均比降/‰;

    α1——汇流参数,取0.15;

    $ {K}_{1} $——地区参数,取1;

    β1——指数,取−1。

    根据现场测量结果行洪宽度以及水深,因为沟谷呈“V”字形,所以设计平均宽度取测量渣堆顶端处长度的一半,则泄洪流速($U_{\rm{s}} $)计算公式如下:

    $$ U_{\rm{s}} = \frac{Q}{{h b}} \text{;} $$ (4)

    式中:$ U_{\rm{s}}$——泄洪流速/(m·s−1);

    Q——洪峰流量/(m3·s−1);

    h——设计水深/m;

    b——设计行洪宽度/m,其余参数同前文一致。

    根据泥沙启动临界流速公式[9-13]

    $$ {U_{\rm{c}}} = 3.91{d^{\tfrac{1}{3}}}{h^{\tfrac{1}{6}}}\sqrt {\sqrt {\frac{{{m^2} + m_0^2{{\cos }^2}\theta }}{{1 + {m^2}}}} - \frac{{{m_0}\sin \theta }}{{\sqrt {1 + {m^2}} }}} \text{;} $$ (5)

    式中:${U_{\rm{c}}} $——启动流速/(m·s−1);

    d——粒径/m;

    h——设计水深/m;

    α——斜坡倾角/(°),m=cotα

    φ——渣堆摩擦角/(°),m0=tanφ

    θ—流向与沙粒所在坡脚水平线的交角/(°)。取 θ=90°。

    文中将某个渣堆断面处的泄洪流速及启动流速理论计算值作为计算稳定性的依据,计算公式如下:

    $$ F_{\rm{s}} = \frac{{U_{\rm{c}}}}{{U_{\rm{s}}}} \text{;} $$ (6)

    式中:$U_{\rm{c}}$$U_{\rm{s}}$——与前文意义一致。

    因为目前对于渣堆在洪水冲击下的稳定性判定没有权威的标准,所以文中引入《建筑边坡工程规范》的判定标准,即假设稳定性系数$F_{\rm{s}} $<1为高危险(失稳),1≤$F_{\rm{s}} $≤1.15为中危险(临界),$F_{\rm{s}} $>1.15为低危险(稳定)。

    通过实地调查测量每个渣堆所对应的对应的汇水面积(F)、沟谷长度(L),纵坡降(I),设计水深(h),行洪宽度(b)等参数作为计算Us的依据,参数值如表2所示。

    表  2  渣堆泄洪流速Us计算参数测量结果表
    Table  2.  The measurement result of calculation parameters of flood discharge velocity of the slag pile
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    F/km20.1760.1760.270.270.4690.4690.6160.176
    L/km0.470.470.610.610.720.790.791.01
    I/‰462462418418387373373314
    h/m222.52.52.5222
    b/m1.53.52.53.353.051111.51.5
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    F/km20.851.681.821.820.2320.2830.4310.511
    L/km1.521.521.660.520.660.840.971.13
    I/‰283283269514456400374332
    h/m2.52222.52.522
    b/m2.79.526.518.54.3524.54
    下载: 导出CSV 
    | 显示表格

    现场调查各渣堆的摩擦角($\varphi $)、其底部沟谷的坡度($\alpha $),并通过筛分试验,得到渣堆的平均粒径(d50=0.0123 m)等参数作为计算$U_{\rm{c}} $的依据,坡度($\alpha $)及摩擦角($\varphi$)测量值如表3所示。

    表  3  渣堆启动流速(Uc)计算参数测量结果表
    Table  3.  The measurement result of calculation parameters of startup flow rate of the slag pile
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    α/(°)1414910.8414.1610.456.766
    φ/(°)3432202241231833
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    α/(°)74412.8412.958.3811.146
    φ/(°)3034293033241729
    下载: 导出CSV 
    | 显示表格

    分别带入Sp=49 mm/h,Sp=60 mm/h,Sp=80 mm/h,Sp=90 mm/h,计算4种雨力条件下的稳定性系数其计算过程如图8所示,结果如表4所示。

    图  8  渣堆稳定性系数计算过程图
    Figure  8.  Calculation process diagram of stability coefficient of slag heap

    通过计算可知UsSpFLIhb决定,FLI均由渣堆所处沟谷的地形地貌决定,对于堆积形态、堆积位置已定的渣堆,其值是定值,对于确定的渣堆断面,hb也是定值,只有Sp是变量,因此Us也只与Sp有关。因此只要给定Sp就可计算出Us

    表  4  不同雨力工况下渣堆稳定性计算结果表
    Table  4.  The calculation result of slag pile stability under different rain conditions
    近10 a最大值计算
    结果(49 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.902.111.401.850.902.762.260.51
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.721.881.332.641.011.250.950.68
    25 a一遇计算
    结果(60 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.721.701.121.490.722.221.810.41
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.581.511.072.120.811.000.760.54
    50 a一遇计算
    结果(80 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.531.250.831.090.531.631.330.30
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.421.110.791.560.600.740.560.40
    100 a一遇计算
    结果(90 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.471.100.730.960.471.441.180.26
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.370.980.691.380.530.650.490.35
    下载: 导出CSV 
    | 显示表格

    通过计算可知Uc由渣堆堆积形态以及渣堆的粒径级配所决定,与Sp大小无关。对于堆积形态确定的渣堆,其Uc是定值,不随Sp的变化而改变。经过计算得到以上4种雨力条件下的稳定性系数后,采用3.1.5的判定标准,对其稳定性高低进行判断,结果如表4所示。

    通过分析表5可知高危渣堆在雨力Sp=49 mm/h、Sp=60 mm/h、Sp=80 mm/h、Sp=90 mm/h条件下占比分别为38%、44%、63%、75%,中低危渣堆分别为6%、19%、12%、6%,低危渣堆占比56%、38%、25%、19%,随着雨力不断增大,高危渣堆占比不断增大,低危渣堆不断减少;不考虑渣堆相互影响的情况下,各种雨力大小工况下,各渣堆危险高低排序不变。不考虑渣堆相互影响的各雨力条件下渣堆的危险程度分布如图9所示。

    表  5  不同雨力下渣堆危险性以及稳定性系数
    Table  5.  Ranking table of slag pile stability under different rain conditions
    渣堆
    编号
    ZD
    8
    ZD
    16
    ZD
    9
    ZD
    1
    ZD
    5
    ZD
    15
    ZD
    13
    ZD
    14
    ZD
    11
    ZD
    3
    ZD
    4
    ZD
    10
    ZD
    2
    ZD
    7
    ZD
    12
    ZD
    6
    49 mm/hFs0.510.680.720.900.900.951.011.251.331.401.851.882.112.262.642.76
    60 mm/hFs0.410.540.580.720.720.760.811.001.071.121.491.511.701.812.122.22
    80 mm/hFs0.300.400.420.530.530.560.600.740.790.831.091.111.251.331.561.63
    90 mm/hFs0.260.350.370.470.470.490.530.650.690.730.960.981.101.181.381.44
    下载: 导出CSV 
    | 显示表格
    图  9  不同雨强工况下渣堆危险程度图
    Figure  9.  Dangerous degree map of slag heap under different rain intensities

    结合表5图9可以看出这4种雨力计算过程中均存在同一条沟上游渣堆失稳后会对下游渣堆稳定性造成影响,例如图9(a)中ZD1在Sp=49 mm/h时首先失稳汇入主沟,会对ZD2以及下游渣堆产生影响,因此要在不考虑渣堆相互影响的计算基础上对渣堆的稳定性系数做出修正。

    为了分析渣堆之间的相互影响,考虑到同一条沟上游渣堆失稳后主要是增加洪水重度,增大洪峰流量,进而增加下游泄洪流速,降低了下游的渣堆的稳定性,因此采用《中国泥石流》[21]中式(7)以及《工程地质手册》[22]式(8)进行修正。

    $$ {\gamma _{\rm{c}}} = \tan J + {k_0} \cdot {k_r} \cdot {k_1} \cdot {A^{0.11}} \text{;} $$ (7)

    式中:γc——泥石流容重/(kN·m−3);

    J——物源区平均坡度;

    k0——补给系数;

    kr——岩性系数;

    k1——稀释系数;

    A——物源区储备体积与汇水面积比。

    (按照文献[21]k0取1,kr取1,k1取0.9)。

    $$ {Q_{\rm{c}}} = Q\left(1 + \frac{{{\gamma _{\rm{c}}} - 1}}{{{\gamma _{\rm{s}}} - {\gamma _{\rm{c}}}}}\right) $$ (8)

    式中:Qc——修正后洪峰流量/(m3·s−1);

    γs——沙粒的密度/(kg·m−3),取2.72 kg·m−3;其余参数同前文一致。

    考虑渣堆相互影响后的修正过程如图10所示。

    图  10  渣堆稳定性系数修正过程图
    Figure  10.  Correction process diagram of slag pile stability coefficient

    用3.3.2的过程,将4种雨力的稳定性系数进行修正后,其计算结果见表6

    表  6  不同雨力工况下渣堆稳定性修正计算结果表
    Table  6.  The calculation result of slag pile stability correction under different rain conditions
    近10年最大观测雨强
    修正结果(49 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.901.411.011.330.702.261.800.43
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.621.481.122.641.011.250.950.62
    25年一遇雨强修正
    结果(60 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.721.130.811.060.561.701.440.34
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.471.220.872.120.810.840.630.45
    50年一遇雨强修正
    结果(80 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.530.830.590.780.411.241.050.25
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.350.880.621.560.600.620.460.33
    100年一遇雨强修正
    结果(90 mm/h)
    渣堆编号ZD1ZD2ZD3ZD4ZD5ZD6ZD7ZD8
    Fs0.470.730.520.680.361.100.930.22
    渣堆编号ZD9ZD10ZD11ZD12ZD13ZD14ZD15ZD16
    Fs0.310.780.551.380.530.540.410.29
    下载: 导出CSV 
    | 显示表格

    采用3.1.5条的判定方法,即假设稳定性系数Fs<1为高危险(失稳),1≤Fs≤1.15为中危险(临界),Fs>1.15为低危险(稳定)。

    通过表7计算结果可知高危渣堆在雨力Sp=49 mm/h、Sp=60 mm/h、Sp=80 mm/h、Sp=90 mm/h条件下占比分别为38%、63%、81%、88%,中危渣堆分别为19%、12%、6%、6%,低危渣堆占比44%、25%、13%、6%,对比表5计算结果,可知考虑渣堆相互影响后,相同雨力条件下,高位渣堆在增加,低危渣堆在减少,这是由于上游渣堆失稳后增大了下有渣堆的致灾风险,不同雨力条件下,各渣堆危险高低排序不同。这是考虑了相似沟道渣堆相互影响的结果,说明考虑渣堆相互影响更符合实际。考虑渣堆相互影响后各雨力条件下的渣堆危险性分布如图11所示。

    表  7  修正后不同雨力下渣堆危险性以及稳定性系数
    Table  7.  Ranking table of slag pile stability under different rain conditions after correction
    49 mm/h渣堆编号ZD
    8
    ZD
    9
    ZD
    16
    ZD
    5
    ZD
    1
    ZD
    15
    ZD
    3
    ZD
    13
    ZD
    11
    ZD
    14
    ZD
    4
    ZD
    2
    ZD
    10
    ZD
    7
    ZD
    6
    ZD
    12
    Fs0.430.620.620.700.900.951.011.011.121.251.331.411.481.802.262.64
    60 mm/h渣堆编号ZD
    8
    ZD
    16
    ZD
    9
    ZD
    5
    ZD
    15
    ZD
    1
    ZD
    3
    ZD
    13
    ZD
    14
    ZD
    11
    ZD
    4
    ZD
    2
    ZD
    10
    ZD
    7
    ZD
    6
    ZD
    12
    Fs0.340.450.470.560.630.720.810.810.840.871.061.131.221.441.702.12
    80 mm/h渣堆编号ZD
    8
    ZD
    16
    ZD
    9
    ZD
    5
    ZD
    15
    ZD
    1
    ZD
    3
    ZD
    13
    ZD
    11
    ZD
    14
    ZD
    4
    ZD
    2
    ZD
    10
    ZD
    7
    ZD
    6
    ZD
    12
    Fs0.250.330.350.410.460.530.590.600.620.620.780.830.881.051.241.56
    90 mm/h渣堆编号ZD
    8
    ZD
    16
    ZD
    9
    ZD
    5
    ZD
    15
    ZD
    1
    ZD
    3
    ZD
    13
    ZD
    14
    ZD
    11
    ZD
    4
    ZD
    2
    ZD
    10
    ZD
    7
    ZD
    6
    ZD
    12
    Fs0.220.290.310.360.410.470.520.530.540.550.680.730.780.931.101.38
    下载: 导出CSV 
    | 显示表格
    图  11  修正后不同雨强工况下渣堆危险程度图
    Figure  11.  Dangerous degree map of slag heap under different rain intensities

    (1)该区泥石流隐患是人为原因,虽然马超营断裂演化形成有利于泥石流发生的地形条件,但废弃矿渣压占行洪通道才是主因。

    (2)渣堆泄洪流速(Us)计算需SpFLIhb等6个参数,启动流速(Uc)需αφd等3个参数;同一雨力条件下,渣堆失稳转化为泥石流的风险大小不同,取决于UsUc的比值;不同雨力条件下,对于特定堆弃场地、特定堆积形态,Us仅随Sp赋值而改变,而Uc是定值,稳定性系数(Fs)与Sp赋值有关。

    (3)渣堆的稳定性可通过不考虑相互影响算出初步结果,在分析相互影响关系进行修正等两个步骤进行;随着雨力增强,失稳渣堆增多,泥石流危害程度增大。

    结合金矿区降水及矿渣堆放现状,防灾的关键在于防渣,结合文中分析提出建议如下:

    (1)废渣堆放场地要提前规划,做好选址,避免因挤压行洪通道而增加泄洪流速,增大致灾风险。

    (2)渣堆防治要根据雨力大小,危险性高低做到分类防治、科学精准、经济节约。

    致谢:该项研究得到长安大学曹琰波副教授,中国地质调查局西安地质调查中心徐友宁研究员、朱立峰高工的悉心指导和栾川县自然资源局、栾川县金兴矿业有限责任公司的大力支持,在此一并表示感谢。

  • 图  1   研究区域雪崩遥感解译与分布图

    Figure  1.   Remote sensing interpretation and distribution of snow avalanches in the study area

    图  2   K均值聚类算法流程图

    Figure  2.   Flowchart of the k-means clustering algorithm

    图  3   MLP结构示意图

    Figure  3.   Schematic diagram of the MLP structure

    图  4   雪崩易发性评价流程图

    Figure  4.   Flowchart of sonw avalanche susceptibility evaluation

    图  5   雪崩评价因子图

    Figure  5.   Snow avalanche evaluation factors map

    图  6   雪崩易发性指数图

    Figure  6.   Snow avalanche susceptibility index map

    图  7   评价因子重要性统计图

    Figure  7.   Importance statistics of evaluation factors

    图  8   ROC曲线

    Figure  8.   ROC curves

    图  9   基于K-MLP模型雪崩易发性分区图

    Figure  9.   Snow avalanche susceptibility partition map based on K-MLP model

    表  1   评价因子数据源

    Table  1   Data sources for evaluates factors

    分类 评价因子 数据源
    地形条件 高程、坡度、坡向、地表粗糙度、
    地表起伏度、高程变异系数
    地理空间数据云DEM
    气候条件 1月平均温度、最大风速、
    年平均降雨量
    研究区及周边各站点
    的气象数据
    积雪条件 年平均降雪量、
    最大积雪厚度
    研究区及周边各站点
    的气象数据
    下载: 导出CSV

    表  2   雪崩评价因子分级量化结果

    Table  2   Quantitative results of snow avalanche evaluation factor grading

    评价因子 二级属性 $ {S _{ij}} $ $ {N_{ij}} $ $ {X_{ij}} $ $ {C_{ij}} $ 评价因子 二级属性 $ {S _{ij}} $ $ {N_{ij}} $ $ {X_{ij}} $ $ {C_{ij}} $
    高程/m 1873~2295 43244 5 0.241 0.000 地面粗糙度 1~1.1 149984 37 0.514 0.228
    >2295~2619 82136 15 0.381 0.051 >1.1~1.2 83112 48 1.206 0.536
    >2619~2927 59328 30 1.056 0.297 >1.2~1.4 51767 38 1.533 0.681
    >2927~3262 51439 31 1.258 0.370 >1.4~1.7 19965 21 2.196 0.976
    >3262~3627 43467 23 1.105 0.315 >1.7~2.4 4640 5 2.250 1.000
    >3627~4459 31463 45 2.986 1.000 >2.4~7.2 709 0 0.000 0.000
    坡度/(°) 0~10 62301 5 0.167 0.000 地形起伏度
    /m
    0~194 49218 1 0.042 0.000
    >10~19 56267 18 0.668 0.253 >194~332 55415 11 0.414 0.213
    >19~28 59776 28 0.978 0.410 >332~457 68499 31 0.945 0.516
    >28~37 62891 40 1.328 0.587 >457~588 67127 48 1.493 0.829
    >37~47 49479 38 1.603 0.726 >588~754 53605 46 1.792 1.000
    >47~82 19463 20 2.145 1.000 >754~1263 17213 12 1.455 0.808
    坡向 67060 31 0.962 0.373 1月平均气温
    / °C
    −14~−11 39756 15 0.788 0.045
    东北 23094 7 0.633 0.000 >−11~−9 201195 71 0.737 0.000
    22559 15 1.388 0.855 >−9~−7 70126 63 1.876 1.000
    东南 38180 21 1.148 0.583 年平均降雨量
    /mm
    43~45 65586 27 0.859 0.319
    48476 21 0.904 0.307 >45~47 127682 31 0.507 0.000
    西南 43794 17 0.810 0.201 >47~49 117809 91 1.613 1.000
    西 31664 23 1.517 1.000 最大风速
    /(m·s−1
    9~12 32130 18 1.170 0.760
    西北 35250 14 0.829 0.222 >12~15 169196 63 0.777 0.000
    高程
    变异系数
    0~0.016 41667 4 0.200 0.000 >15~19 109751 68 1.294 1.000
    >0.016~0.028 72137 27 0.781 0.540 年平均降雪量
    /mm
    11~15 22538 7 0.648 0.000
    >0.028~0.040 80064 48 1.252 0.977 >15~18 219917 77 0.731 0.062
    >0.040~0.051 63506 38 1.249 0.974 >18~21 68622 65 1.978 1.000
    >0.051~0.065 45784 28 1.277 1.000 地表切割度
    /m
    0~88 74711 2 0.056 0.000
    >0.065~0.107 7919 4 1.055 0.794 >88~161 70384 18 0.534 0.210
    最大积雪深度
    /mm
    55~61 121679 51 0.875 0.628 >161~232 62245 23 0.771 0.315
    >61~68 153748 95 1.290 1.000 >232~309 55587 62 2.329 1.000
    >68~78 35650 3 0.176 0.000 >309~401 39623 36 1.897 0.810
    >401~682 8527 8 1.959 0.837
    下载: 导出CSV

    表  3   雪崩评价因子相关性矩阵

    Table  3   Correlation matrix of snow avalanche evaluation factors

    高程 坡度 坡向 高程
    变异系数
    地表
    切割度
    地面
    粗糙度
    地形
    起伏度
    1月平均
    气温
    年平均
    降雨量
    最大
    风速
    年平均
    降雪量
    最大
    积雪深度
    高程 1
    坡度 0.12 1
    坡向 0.03 0.01 1
    高程变异系数 0.17 0.28 0.03 1
    地表切割度 0.21 0.11 0.05 0.11 1
    地面粗糙度 0.34 0.26 0.01 0.25 0.28 1
    地形起伏度 0.44 0.6 0.05 0.92 0.93 0.47 1
    1月平均气温 0.03 0.11 0.02 0.15 0.13 0.06 0.12 1
    年平均降雨量 0.01 0.07 0 0.04 0.06 −0.01 0.04 0.16 1
    最大风速 0.51 0.01 −0.01 −0.02 −0.13 −0.11 −0.17 0.61 0.07 1
    年平均降雪量 0.28 0.22 0.02 0.19 0.24 0.14 0.25 0.23 0.23 0.08 1
    最大积雪深度 −0.54 −0.19 −0.02 −0.13 −0.22 −0.19 −0.27 0.14 0.18 0.64 0.18 1
    下载: 导出CSV

    表  4   K均值聚类法分析结果

    Table  4   Results of K-means clustering algorithm method

    聚类结果栅格数量/个雪崩个数/个相对雪崩比
    113484467.122
    28556150.122
    364712471.516
    43304170.442
    5114280440.804
    下载: 导出CSV

    表  5   基于K-MLP模型雪崩易发性分区结果统计

    Table  5   Statistical results of snow avalanche susceptibility partition based on K-MLP model

    易发性等级 栅格
    数量/个
    面积
    /km2
    雪崩
    数/个
    分区
    比例/%
    雪崩比 雪崩密度
    /(个·km−2
    低易发区 79284 71.35 9 25.59 0.06 0.12
    中易发区 86287 77.66 24 27.74 0.16 0.31
    高易发区 83594 75.23 51 26.87 0.34 0.68
    极高易发区 61912 55.72 64 19.90 0.44 1.17
    下载: 导出CSV
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  • 收稿日期:  2023-02-08
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