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四川九绵高速平武段物源量对泥石流流体性质与致灾强度影响的差异性分析

李玲, 陈宁生, 杨溢, 钟政, 黄娜

李玲,陈宁生,杨溢,等. 四川九绵高速平武段物源量对泥石流流体性质与致灾强度影响的差异性分析[J]. 中国地质灾害与防治学报,2024,35(5): 90-102. DOI: 10.16031/j.cnki.issn.1003-8035.202312029
引用本文: 李玲,陈宁生,杨溢,等. 四川九绵高速平武段物源量对泥石流流体性质与致灾强度影响的差异性分析[J]. 中国地质灾害与防治学报,2024,35(5): 90-102. DOI: 10.16031/j.cnki.issn.1003-8035.202312029
LI Ling,CHEN Ningsheng,YANG Yi,et al. Differential analysis of sediment volume on fluid properties and debris flow disaster impact in the northwest traffic corridor of Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2024,35(5): 90-102. DOI: 10.16031/j.cnki.issn.1003-8035.202312029
Citation: LI Ling,CHEN Ningsheng,YANG Yi,et al. Differential analysis of sediment volume on fluid properties and debris flow disaster impact in the northwest traffic corridor of Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2024,35(5): 90-102. DOI: 10.16031/j.cnki.issn.1003-8035.202312029

四川九绵高速平武段物源量对泥石流流体性质与致灾强度影响的差异性分析

基金项目: 国家自然科学基金-国际(地区)合作与交流项目(42361144880);青海省科技厅基础研究计划项目(2024-ZJ-904);中国博士后科学基金资助项目(GZC20232571;2024M753153)
详细信息
    作者简介:

    李 玲(1998—),女,四川内江人,安全工程专业,硕士研究生,研究方向为山地灾害评价与预测。E-mail:1694598637@qq.com

    通讯作者:

    杨 溢(1965—),男,云南大理人,地质工程专业,博士、教授,研究方向为灾害启动机理与水土保持。E-mail:2919847230@qq.com

  • 中图分类号: P642.23

Differential analysis of sediment volume on fluid properties and debris flow disaster impact in the northwest traffic corridor of Sichuan Province

  • 摘要:

    九绵高速公路LJ9段沟谷型泥石流灾害频发,时常对附近村落和基础设施造成威胁。为了避免灾害对区域高速公路和G247国道的二次冲击,对九绵高速LJ9段沿线存在泥石流暴发痕迹的6条泥石流沟的物源特征与规模进行研究,认为该地区泥石流属于低频沟谷暴雨型。泥石流容重值在1.647~1.843 g/cm3,流速为3.45~6.54 m/s,流量为29.47~253.45 m3/s,一次过流总量为0.99×104~8.28×104 m3。文章通过对比九绵高速“8•16”泥石流规模特征差异,分析物源量与泥石流的容重、规模的关系,建立了相关性公式。研究结果发现:(1)研究区泥石流属于低频沟谷暴雨型,流体性质与沟道物源量紧密相关,单位面积物源量与泥石流容重呈线性关系;单位面积物源量大于1.65×105 m3/km2和小于1.13×105 m3/km2时,泥石流分别表现为黏性和稀性。(2)在相同地质条件下,泥石流物源量与规模之间呈线性相关,泥石流一次总量随流域单位面积物源量减小而减小。(3)泥石流致灾能力受物源量控制,物源总量超过80×104 m3的沟道易形成黏性泥石流,沟口的堆积扇更大,对应破坏力更强;物源总量小于80×104 m3的沟道更易形成稀性泥石流,冲出沟口的破坏性明显小于黏性泥石流,对沟口的人员与工程建设的威胁更小。该研究结果可为九绵高速公路的规划设计与建设提供一定参考,也为山区基础设施的安全运营、灾害防御工程建设工作提供科学依据。

    Abstract:

    The LJ9 segment of Jiumian Expressway frequently experiences gully-type debris flow disasters, posing threats to nearby villages and infrastructure. Considering the unique characteristics and importance of the highway and G247 national road, this study investigates the material source characteristics and scale of six debris flow gullies with debris flow outbreak traces along the LJ9 section of Jiumian Expressway. The debris flows in this area are classified as low-frequency gully flash flood types. The bulk density of the debris flows ranges from 1.647 to 1.843 g/cm3, with velocities between 3.45 and 6.54 m/s, and flow rate ranging from 29.47 to 253.45 m3/s. The total volume of the debris flow ranges from 0.99×104 to 8.28×104 m3. This paper compares the differences in the scale characteristics of the “8•16” debris flow on the Jiumian Expressway, and analyze the relationship between the volume of material source and the bulk density and scale of debris flow, establishing relevant calculation formulas. The results show that: (1) The fluid properties of debris flow in the study area are classified as low-frequency gully flash flood types, closely related to the volume of material source, with a positive correlation between unit area material source volume and debris flow bulk density. When unit area material source volume exceeds 1.65×105 m3/km2 or falls below 1.13×105 m3/km2, debris flow exhibit viscous or diluted characteristics, respectively. (2) Under similar geological conditions, there is a linear correlation between material source volume and debris flow scale, with total debris flow volume decreasing as unit area material source volume decreases. (3) Debris flow damage potential is controlled by material source volume. Gullies with material source volumes exceeding 800 000 m3 are more likely to produce viscous debris flows, which has greater destructive power and form larger debris flow fans at their mouths. These findings provide insights for the design and construction of the Jiumian Expressway and serve as a scientific basis for the safe operation of infrastructure in mountainous regions and the construction work of geological hazard prevention projects.

  • 云南以红河深大断裂为界,滇西为高山纵谷区,滇东为喀斯特高原[1],滇东北为地质灾害高易发区,滇西地质灾害易发性南相对较弱[2]。罗平县地处云南滇东喀斯特高原地区,是典型的岩溶山区。目前,崩滑易发性制图主要分为定性、定量和机器学习3种方法[34]。定性分析是通过对成因机制的全面认识,基于专家经验和知识确定评价因子权重,定量分析方法通过数学或数值算法估计滑坡易发性[5]。定性分析方法主要有层次分析法[68],定量分析方法主要有频率比法[910]、信息量法[1112],机器学习方法有逻辑回归法、随机森林法、K近邻、支持向量机和神经网络等[1315]。国内外学者进行研究分析,并采用多种研究方法进行对比,吉日伍呷等[15]通过逻辑回归、K近邻、朴素贝叶斯和随机森林算法对鲁甸进行地震滑坡易发性评价,并得出统计建模地更多的是寻找变量之间的可解释关系;樊芷吟等[16]通过信息量法+Logistic回归模型对汶川县进行易发性评价;张晓东[17]通过定量信息量法和确定性系数法分别与Logistic回归耦合对宁夏盐池县进行易发性评价,得出耦合模型结果均优于单一模型评价结果。根据以上研究,易发性评价模型首先选取崩滑评价影响因子,再通过分类分级进行计算,根据不同方法得出的结果基于ArcGIS进行等级划分。信息量法只考虑了评价因子分类分级状态下的权重,其优点在于原理简单,易于实现;缺点在于无法体现所选取的评价因子的权重。因此选取层次分析法和逻辑回归法分别赋予评价因子的权重,其中层次分析法依据崩滑灾害成因分析,通过专家法构建矩阵计算得出评价因子的权重,逻辑回归法是依据样本数据和连接方法,通过两种方法得出评价因子的权重分别与信息量法进行耦合,其耦合模型将评价因子的权重和分类分级的权重叠加得到综合权重,降低了单一评价模型人为主观性因素的影响,论证不同方法评价结果的准确性。

    本文以罗平县作为研究区域,基于野外详实的地质灾害调查成果,综合分析孕灾地质条件和崩滑点分布规律,选取岩土体(工程岩组)、地形地貌(坡度、坡向、高程、起伏度、曲率、地貌类型)、地质构造(距断裂距离)、气象水文(距河流距离)等评价因子。采用信息量法、加权信息量法、信息量-逻辑回归耦合法构建易发性评价模型进行对比分析,并对评价结果进行精度检验分析,选取精度最高模型易发性分布图,可为罗平县今后地质灾害治理提供参考依据,对今后城市的发展和防灾减灾有重要意义,也可为岩溶地区地质灾害易发性评价提供参考。

    研究区(罗平县)位于云南省曲靖市东部。东西最大横距75 km,南北最大纵距99 km。相对高差为1705 m,全县面积3018 km2,山区面积占78%,坝区面积占22%(图1)。研究区西部和北部属于岩溶盆地地貌和岩溶低中山地貌,中部属岩溶断陷湖形盆地,东部和南部受九龙河和南盘江流域侵蚀切割,形成峰林洼地和岩溶中山地貌。区内地层出露主要有古生界泥盆系(D)浅灰、深灰色中厚层状灰岩、泥灰岩、泥质白云岩;石炭系(C)深灰、灰黑色块状灰岩、白云质灰岩、泥质灰岩;古生界二叠系(P)灰、深灰色厚层块状、生物碎屑灰岩,结晶灰岩夹虎斑状灰岩及白云岩;中生界三叠系(T)上统为黄褐色粉砂岩、泥质粉砂岩及细砂岩、中统为深灰色灰岩夹泥质灰岩、中上部为黄色白云岩、下统为紫红色含长石粉细砂夹泥灰岩页岩及含铜页岩;新生界古近系(E)+新近系(N)褐黄紫红色砾岩、细砂岩及粉砂质泥岩、底部砾岩;新生界第四系(Q)细砂、砂砾石及砂质黏土。主要构造体系和构造型式有北东向构造、新华夏系构造、网状构造等。北东向构造为区内主导构造,是研究区内最重要的构造成分之一,主要断裂有:金鸡山断裂、长家湾断裂和腊庄断裂等。其次为新华夏系构造,多发育在褶皱边缘、密集成束、规模大、延伸远、呈舒缓波状,主要分布在西部及南盘江两岸。主要断裂有:洒土革断裂、大水塘断裂、罗格断裂等。

    图  1  研究区概况
    Figure  1.  Overview of the study area

    本研究数据主要包括:(1)12.5 m分辨率数字高程模型(DEM)收集自ASF,用于提取坡度、坡向、起伏度、曲率等评价因子;(2)1∶20万地质图收集自全国地质资料馆,用于提取岩性、断裂等因子;(3)1∶5万地理数据库提取水系;(4)历史崩滑数据:主要来自地矿眉山工程勘察院1∶5万全区调查结果,共154个崩滑灾害点的数据。

    信息量模型是对崩滑历史数据进行统计分析,将影响崩滑的各因子的实测值转化为信息量值,来衡量崩滑的易发性[18]。首先计算各评价因子的信息量值,再对各因子信息量值进行总和,作为崩滑易发性的综合指标[19]。单因子信息量计算公式为:

    I=lnNj/NSj/S (1)

    式中:I——评价因子j下的信息量;

    Nj——评价因子j内发生的崩滑数;

    N——研究区崩滑总数;

    Sj——评价因子j下所占栅格数;

    S——研究区总栅格数。

    将每个评价单元各分类分级进行叠加计算,其地质灾害发生的总信息量计算公式为:

    Ij=i=1nlnNj/NSj/S (2)

    式中:Ij——总信息量,为地质灾害易发性指数;Ij值 越大且为正值则表示该单元内有利于崩滑 发生。

    层次分析模型是一种将决策者定性判断和定量计算有效结合起来的分析方法。通过比较相邻影响因子的重要性[19],根据专家法构建判断矩阵[20]

    A=(aij)=[a11a1nan1ann] (3)

    式中:A——要素判断矩阵;

    aij——因子i和因子j重要性比较的结果,有以下性质:

    aij=1aji,aij0,aii=1,i,j=1,2,,n (4)

    为保证求得的权重的正确性及合理性,还需要进行一致性检验。

    CI=λmaxnn1 (5)
    CR=CI/RI (6)

    式中:CI——一致性指标;

    n——判断矩阵的阶数;

    λmax——判断矩阵的最大特征值;

    CR——随机一致性比;当其<0.1时一致性检验通过;

    RI——随机一致性指标。

    逻辑回归模型是一种研究二分类因变量常用的统计方法[16,21]。通过研究崩滑易发性与评价因子之间的关系,预测崩滑发生的概率。其中自变量为评价因子指标值(x1, x2, ···, xn),是否发生地质灾害作为因变量(分别用1和0代表崩滑点和非崩滑点)。逻辑回归函数如下:

    Z=α+β1x1+β2x2++βnxn (7)
    P(y=1)=11+ez (8)

    式中:α——常数项;

    x1, x2, ···, xn——自变量;

    β1, β2, ···, βn——回归系数;

    Z——崩滑发生的可能性与各评价因子之间的关系;

    P——崩滑灾害发生的概率,范围0~1。

    根据层次分析法得出各评价因子的权重值,结合信息量法各评价因子分类分级的信息量值,两者相乘得出加权信息量值,其计算公式可表示为:

    Ij=i=1nωiIi=i=1nωilnNi/NSi/S (9)

    式中:Ij——加权信息量;

    ωi——每个评价因子的权重;

    Ii——评价因子i的信息量值。

    将信息量模型与逻辑回归模型进行耦合,通过逻辑回归确定评价因子的权重,可降低信息量模型评价因子分级的主观性影响。其原理将信息量模型中评价因子分类分级的信息量值作为逻辑回归模型中的自变量,建立回归方程进行逻辑回归运算,得出各评价因子的回归系数,以此为依据建立信息量-逻辑回归耦合模型。

    本文在罗平县资料收集和野外地质调查的基础上,选取岩土体(工程岩组)、地形地貌(坡度、坡向、高程、起伏度、曲率、地貌类型)、地质构造(距断裂距离)、气象水文(距河流距离)等评价因子进行分析。根据12.5 m×12.5 m栅格单元作为易发性评价的制图单元,通过对研究区评价因子与崩滑点数据进行归纳分析,得出各评价因子的分类分级处理(图2)。

    图  2  崩滑评价因子分类分级图
    Figure  2.  Classification map of landslide susceptibility evaluation factors

    进行逻辑回归时,需确保所选评价因子之间的相互独立,相关性高会出现多重共线性[2223]。采用容忍度(tolerance,TOL)和方差膨胀因子(variance inflation factor,VIF)对自变量进行多重共线性诊断:

    VIF=11R2 (10)

    式中:R2——以xi为因变量时对其他自变量回归的复测 定系数;

    TOLVIF的倒数,当TOL大于0.1且VIF小于10时,则不存在多重共线性。

    根据308个独立属性样本,提取每个样本的各类级信息量值,在SPSS软件中进行多重共线性诊断。结果显示对所选9个评价因子其VIF值在1~1.5(表1)。其VIF<5,表明各因子之间相互独立,不存在共线性。

    表  1  评价因子VIF计算结果表
    Table  1.  Calculation results of VIF for evaluation factors
    评价因子 TOL VIF
    工程岩组 0.818 1.222
    坡度 0.656 1.524
    坡向 0.954 1.048
    高程 0.904 1.107
    地貌类型 0.713 1.402
    起伏度 0.669 1.495
    曲率 0.970 1.031
    距断裂距离 0.945 1.058
    距河流距离 0.717 1.396
    下载: 导出CSV 
    | 显示表格

    崩滑的易发性与评价因子之间存在一定的相关性。为了保证各评价因子间的相互独立性和结果的可靠性,进行因子相关性检验[24]。结果显示各评价因子之间的相关系数均<0.3(表2),评价因子之间的相关性较小,所以9个评价因子均可以进入模型。

    表  2  评价因子之间的相关系数矩阵
    Table  2.  Correlation coefficient matrix of evaluation factors
    评价因子 工程岩组 坡度 坡向 高程 地貌类型 起伏度 曲率 距断裂距离 距河流距离
    工程岩组 1
    坡度 0.07 1
    坡向 −0.09 0.07 1
    高程 0.03 −0.08 0.08 1
    地貌类型 0.02 0.11 0.03 0.01 1
    起伏度 0.11 0.03 0.04 0.00 0.01 1
    曲率 0.07 −0.07 0.08 0.03 0.06 0.04 1
    距断裂距离 0.09 −0.03 −0.04 0.06 0.09 −0.05 0.08 1
    距河流距离 0.01 0.04 0.01 0.01 0.02 0.06 0.06 0.07 1
    下载: 导出CSV 
    | 显示表格

    信息量模型中,崩滑的易发性与因子信息量值有关,信息量值越大且为正值则表示单元内崩滑越容易发生[2528]。根据已有154个地质灾害进行重分类统计,根据公式(1)计算各评价因子分类分级的信息量值(表3)。

    表  3  评价因子分类分级信息量值
    Table  3.  Information value of classification levels for evaluation factors
    评价因子 因子分级 崩滑数量 栅格数量 信息量值 加权信息量值
    工程岩组 软硬相间碳酸盐岩夹碎屑岩岩组 2 205041 0.1347 0.0396
    块状结构坚硬玄武岩岩组 50 1817099 1.1717 0.3433
    坚硬层状碳酸盐岩岩组 97 15380267 −0.3014 −0.0883
    第四系冲洪积松散岩组 5 661729 −0.1206 −0.0353
    坡度
    /(°)
    0~6 7 3162705 −1.3501 −0.2012
    6~12 33 3790902 0.0193 0.0029
    12~18 50 3707425 0.4571 0.0681
    18~24 31 3008341 0.1880 0.0280
    24~30 17 2054943 −0.0316 −0.0047
    30~36 5 1186737 −0.7064 −0.1053
    36~60 11 1091533 0.1657 0.0247
    60~90 0 33429 0 0
    坡向 16 2219489 −0.1692 −0.0129
    东北 15 1920437 −0.0891 −0.0068
    22 2493207 0.0329 0.0025
    东南 31 2704414 0.2946 0.0224
    20 2304895 0.0161 0.0012
    西南 13 1974899 −0.2601 −0.0198
    西 17 2138397 −0.0714 −0.0054
    西北 20 2280277 0.0269 0.0020
    高程/m 715~860 8 350496 0.9848 0.1468
    860~1200 8 1149066 −0.2025 −0.0233
    1200~1350 14 1312775 0.2239 0.4811
    1350~1500 26 3226787 −0.0097 −0.1776
    1500~1650 26 3079467 0.3627 −0.0402
    1650~1800 29 2366839 −0.1401 1.8675
    1800~1950 30 4047954 −0.5067 −0.8614
    1950~2420 13 2530843 −0.5067 −3.6224
    地貌类型 岩溶低中山地貌 22 2824754 −0.0904 −0.0037
    构造侵蚀剥蚀地貌 18 1200919 0.5643 0.0231
    岩溶中山地貌 40 3833292 0.2021 0.0083
    岩溶盆地地貌 0 1948623 0 0
    峰林谷地地貌 0 91609 0 0
    峰丛洼地地貌 16 3752094 −0.6927 −0.0284
    断块上升岩溶地貌 1 177099 −0.4119 −0.0169
    断坳盆地 1 116724 0.0049 0.0002
    石丘(垅岗) 2 390319 −0.5091 −0.0209
    侵蚀谷地地貌 52 2561268 0.8677 0.0356
    构造侵蚀岩溶地貌 2 1167462 −1.6047 −0.0658
    起伏度/m 0~4 13 4183395 −1.0071 −0.0594
    4~8 40 4306129 0.0879 0.0052
    8~15 74 5697380 0.4232 0.0249
    15~23 17 2685735 −0.2956 −0.0174
    23~30 2 750479 −1.1607 −0.0684
    30~38 7 294073 1.0289 0.0607
    38~50 0 128414 0 0
    50~220 1 57698 0.7117 0.0419
    曲率 <0 70 7431512 0.0997 0.0041
    0 20 3330755 −0.3505 −0.0144
    >0 64 7301960 0.0277 0.0011
    距断裂距离/m 0~600 70 6123046 0.2934 0.0194
    600~1200 26 4659019 −0.4237 −0.0279
    1200~1800 20 2742459 −0.1561 −0.0103
    1800~2400 8 1599989 −0.5336 −0.0352
    2400~3000 5 1028561 −0.5617 −0.0371
    >3000 25 1911080 0.4281 0.0282
    距河流距离/m 0~600 57 3404716 0.6748 0.0445
    600~1200 32 2816455 0.2872 0.0189
    1200~1800 21 2280631 0.0771 0.0051
    1800~2400 14 1898512 −0.1451 −0.0096
    2400~3000 6 1564553 −0.7989 −0.0528
    >3000 24 6099326 −0.7731 −0.0511
    下载: 导出CSV 
    | 显示表格

    根据加权信息量法构建模型,对研究区地质灾害及其背景因素和影响因素的相对重要性进行分析,依据所选取的评价因子,按照专家法对选取的评价因子根据式(3)构建判断矩阵计算出各评价因子的权重值,根据式(5)求出每个判断矩阵的一致性指标CI,并通过式(6)进行一致性检验(表4),各评价因子的权重值与各评价因子分类分级的信息量值根据式(9)得出加权信息量值(表3)。

    表  4  评价因子分类分级判断矩阵及其权重
    Table  4.  Judgment matrix and weight of classification levels for evaluation factors
    评价因子 1 2 3 4 5 6 7 8 9 权重 CI/CR
    工程岩组 1 2 4 2 6 8 4 4 6 0.31 0.003
    0.002
    坡度 1/2 1 2 1 3 4 2 2 3 0.155
    坡向 1/4 1/2 1 1/2 2 2 1 1 2 0.083
    高程 1/2 1 2 1 3 4 2 2 3 0.155
    起伏度 1/6 1/3 1/2 1/3 1 1 1/2 1/2 1 0.046
    曲率 1/8 1/4 1/2 1/4 1 1 1/2 1/2 1 0.041
    距断裂距离 1/4 1/2 1 1/2 2 2 1 1 2 0.082
    距河流距离 1/4 1/2 1 1/2 2 2 1 1 2 0.082
    地貌类型 1/6 1/3 1/2 1/3 1 1 1/2 1/2 1 0.046
    下载: 导出CSV 
    | 显示表格

    根据研究区已有的154个崩滑点,并随机选取等量的非崩滑点,共计有308个独立属性样本。将全部样本点依次赋予相应评价因子的信息量值,导入SPSS 25软件进行二项逻辑回归分析(表5),各评价因子的信息量值作为自变量,是否发生地质灾害作为因变量(1和0代表崩滑点和非崩滑点)。

    表  5  逻辑回归分析结果
    Table  5.  Results of logistic regression analysis
    评价因子 B S.E Wals df sig
    工程岩组 0.698 0.261 7.142 1 0.002
    坡度 1.331 0.513 6.721 1 0.000
    坡向 0.761 0.862 0.780 1 0.007
    高程 0.309 0.246 1.570 1 0.002
    地貌类型 0.171 0.421 0.165 1 0.006
    起伏度 0.641 0.304 4.455 1 0.005
    曲率 1.523 0.907 2.820 1 0.003
    距断裂距离 0.528 0.365 2.090 1 0.004
    距河流距离 0.458 0.264 3.001 1 0.000
    常量 −0.165 0.142 1.336 1 0.005
      注:B为回归系数,S.E为标准误,wals为卡方值,df为自由度,sig为显著性。
    下载: 导出CSV 
    | 显示表格

    在逻辑回归分析结果中,sig值越小,代表评价因子的显著性越高,表(3)中sig小于0.05,说明9个因子均有统计意义。基于模型分析结果中的各因子系数值根据公式(7)得逻辑回归公式如下:

    {Z=0.165+0.698x1+0.031x2+0.761x3+0.309x4+0.171x5+0.641x6+1.523x7+0.528x8+0.458x9P=11+ez (11)

    式中:x1x9分别为地层岩组、坡度、坡向、高程、地貌类型、起伏度、曲率、距断裂距离、距河流距离的信息量值;运用ArcGIS的栅格计算器功能将z值代入式(8)得到崩滑灾害发生的概率p

    信息量模型根据信息量法求出各评价因子分类分级的信息量,然后进行叠加分析,加权信息量模型根据表(4)得出的各评价因子的权重值,结合公式(9)求出各分类分级的加权信息量值(表3)。信息量-逻辑回归耦合模型根据公式(11)所求出的概率值构建模型,将结果进行重分类处理,并利用自然断点法将3种评价模型结果划分为非、低、中和高4个等级(图3)。

    图  3  崩滑易发性评价结果
    Figure  3.  Landslide susceptibility evaluation results

    为进一步验证3种评价模型分区结果的精度,本文采用ROC(receiver operating characteristic)曲线进行精度检验。ROC曲线又称接收者工作特征曲线,其横轴特异性代表易发性面积百分比累积量,纵轴敏感度代表崩滑地质灾害点数百分比累积量。ROC曲线与坐标轴围成的面积用AUC值来表示,其线下的面积大小表示预测成功率,值越大准确率越高,模型的预测效果越好[28]。3种评价模型ROC曲线中AUC值分别为0.757,0.723,0.852(图4),加权信息量模型的精度最低,其原因是在采用层次分析法得出评价因子权重时,依据专家打分法构建判断矩阵时主观性因素较大,导致权重综合时降低了准确性,信息量-逻辑回归耦合模型的精度最高,其模型构建主要依据样本点与信息量法中分类分级信息量值进行连接,其精度与所构建的样本点存在紧密的联系,样本点统计规律越明显预测效果越好。

    图  4  ROC曲线
    Figure  4.  ROC curve

    根据所得出的评价结果,利用ArcGIS自然断点法将其划分为非、低、中和高4个等级,并将各易发性等级之间的面积(分级比)进行统计(表6),根据3种模型精度评价结果,信息量-逻辑回归耦合模型精度最高,其非-高易发区崩滑面积(分级比)分别为771.1 km2(25.55%)、836.6 km2(27.73%)、864.36 km2(28.64%)和545.94 km2(18.08%)。

    表  6  崩滑易发性等级分布预测结果
    Table  6.  Prediction results of landslide susceptibility grade distribution
    易发性等级信息量模型加权信息量模型信息量-逻辑回归耦合模型
    分级比/%崩滑比/%分级面积/km2分级比/%崩滑比/%分级面积/km2分级比/%崩滑比/%分级面积/km2
    非易发区17.565.84529.9616.177.14489.0325.556.49771.1
    低易发区28.2714.29853.1831.8015.58959.0227.7320.13836.6
    中易发区32.4631.17979.6433.8235.061020.6828.6425.32864.36
    高易发区21.7148.70655.2218.2042.21549.2718.0848.05545.94
    下载: 导出CSV 
    | 显示表格

    (1)以罗平县为研究对象,选取工程岩组、坡度、坡向、高程、地貌类型、起伏度、曲率、距断裂距离、距河流距离等9个评价因子,进行独立性检验,选取3种评价方法构建易发性评价模型进行对比分析。

    (2)通过对评价因子的分类分级处理,计算信息量值和权重值,值较大的因子类分别是:工程岩组中的层状结构坚硬长石石英砂岩岩组、地貌类型中的岩溶中山地貌和侵蚀谷地地貌、坡度主要分布在6°~30°度之间、高程集中在1350~1950 m、起伏度在23 m以下、距断裂距离和距河流距离1800 m之内,信息量值总体为正,对崩滑发育具有促进作用。

    (3)根据构建的信息量模型、加权信息量模型和信息量-逻辑回归耦合模型进行对比,通过ROC曲线对3种模型的精度检验,其AUC值分别为0.757,0.723,0.852,模型的精度均大于0.7。结合崩滑点分布图,信息量-逻辑回归耦合模型评价与灾点分布情况相符合,可为快速建立评价指标体系和区域崩滑易发性提供参考依据。

  • 图  1   研究区地理位置

    Figure  1.   Geographical location of the study area

    图  2   研究区地质图

    Figure  2.   Geological map of the study area

    图  3   2020年平武县月平均气温、降雨量

    Figure  3.   Monthly average temperature and rainfall in Pingwu County, 2020

    图  4   凹槽土体物源量计算模型

    Figure  4.   Calculation model of the source quantity of groove material sourc

    图  5   颗粒分析粒度曲线

    Figure  5.   Particle size curve from particle analysis

    图  6   研究区泥石流原状物颗粒组成

    Figure  6.   Composition of original debris flow particles in the study area

    图  7   研究区物源分布

    Figure  7.   Material source distribution in the study area

    图  8   阿祖沟凹槽物源分布示意图

    Figure  8.   Schematic diagram of groove material source distribution map of the Azu debris flow gully

    图  9   夺补河5号沟坡面侵蚀物源

    Figure  9.   Material source of slope erosion in No. 5 debris flow gully of Duobo River

    图  10   泥石流沟单位面积物源量与容重的关系

    Figure  10.   The Relationship between material source quantity and bulk density per unit area of debris flow gully

    图  11   单位面积物源量与一次泥石流总量的关系

    Figure  11.   The relationship between the volume of material source per unit area and the total volume of the debris flow

    图  12   泥石流堆积扇示意图

    Figure  12.   Schematic diagram of debris flow fans

    图  13   物源总量与泥石流堆积扇体积的关系

    Figure  13.   The relationship between the total volume of material source and the volume of debris flow fan

    图  14   阿祖沟泥石流沟口航拍图(受灾后)

    Figure  14.   Aerial photography of the Azu debris flow gully exit (post- disaster)

    图  15   杂排沟泥石流沟口堆积扇概况图

    Figure  15.   Overview of the debris flow fan at the Zapai debris flow gully exit

    表  1   泥石流动储量计算公式

    Table  1   Calculation formula for debris flow dynamic reserves

    物源类型 计算公式 说明
    坡面侵蚀物源 V=αAγ V为坡面侵蚀物源体积/104 m3A为坡面侵蚀物源面积/m2
    α为面积-体积修正系数;γ为面积-体积经验指数,不同地区的修正系数与经验指数一般不同
    凹槽土体物源 松散土体面积:
    Δ1cod=12cocd=12hhtanα=
    12h2tan(βθ)
    动储量体积:V01=Δ1codL1
    β为斜坡自然休止角/(°);α为物源土体与自然休止角夹角/(°);co=h为原沟床深度/m;
    V01为下切侵蚀型动储量/m3L1为沟床堆积体长度/m,具体参数位置见图4
    下载: 导出CSV

    表  2   泥石流运动参数式

    Table  2   Calculation formulas table for debris flow motion parameters

    序号 参数 公式 说明
    1 泥石流流速 Vc=(Mc/a)R2/3Ic1/2
    a=(1+φcγs)12
    φc=γcγwγsγc
    Vc为流速/(m·s−1);Mc为泥石流沟床粗糙系数,沟槽情况越复杂取值越大,取值为4.5~7.9(表3);R为泥石流水力半径/m ;Ic为泥石流水力坡度;γs为固体颗粒的容重,取2.65~2.7 g/cm3γc为泥石流容重/(g·cm−3);γw为水的容重,取1.0 g/cm3
    2 峰值流量 QC=(1+φ)QPDC
    QP=0.278ψsτnF
    φ=(γcγw)/(γsγc)
    QC为泥石流峰值流量/(m3·s−1);QP为频率为P的暴雨洪水设计流量/(m3·s−1);φ为泥石流泥沙修正系数,取值在0.8~1.2,见表3DC为堵塞系数,根据实际调查情况,取值见表3ψ为洪峰径流系数;τ为流域汇流时间;n为暴雨指数;s为暴雨雨力;τns依据《四川省中小流域暴雨洪水计算手册》取值,F为流域面积
    3 泥石流一次
    冲出总量
    Wc=0.264TQc T为泥石流历时/s;QC为泥石流峰值流量/(m3·s−1
    4 泥石流冲出
    固体物质总量
    Ws=(γcγw)Wc/(γsγw) Wc为泥石流一次冲出总量/(m3·s−1
    下载: 导出CSV

    表  3   泥石流运动特征参数

    Table  3   Characteristic parameters of debris flow

    沟名 泥石流流速
    /(m·s−1
    泥石流容重
    /(g·cm−3
    泥石流峰值流量
    /(m3·s−1
    泥石流一次冲出总量
    /(104 m3
    固体物质总量
    /(104 m3
    堵塞系数 沟床糙率
    系数
    泥沙修正
    系数
    阿祖沟 4.21 1.843 174.14 8.28 3.73 2.7 4.9 1.15
    麻石扎3号桥沟 6.54 1.647 29.47 0.99 0.34 2.2 7.3 0.83
    夺补河5号桥沟 3.79 1.717 21.67 1.80 0.54 2.4 6.2 0.97
    杂排沟 3.45 1.811 253.45 6.12 3.20 2.6 5.2 0.91
    黄土梁左沟 4.50 1.677 81.38 1.612 0.49 1.9 6.9 0.85
    黄土梁右沟 4.36 1.697 88.79 2.18 0.54 2.1 6.7 0.83
      注:表格中的沟床糙率系数(Mc),泥沙修正系数(φ)与堵塞系数(DC)均为结合研究区先前研究、地形条件与实地勘察数据等因素综合考虑后赋值
    下载: 导出CSV

    表  4   研究区泥石流沟基础资料统计表

    Table  4   Statistical table of basic data of debris flow gullies in the study area

    沟名 流域面积
    /km2
    容重
    /(g·cm−3
    坡面、凹槽物源
    面积S1S2
    /(10−4 km2
    凹槽土体物源量
    /(104 m3
    物源总量
    /(×104m3)
    单位面积物源/
    (104 m3·km−2
    阿祖沟 4.54 1.843 5.80、2.19 26.29 102.89 22.62
    麻石扎3号沟 2.34 1.647 2.25、0 0 2.32 0.99
    夺补河5号沟 1.78 1.717 2.36、0 3.37 18.20 10.11
    杂排沟 5.68 1.811 3.96、2.17 28.16 97.69 17.20
    黄土梁左沟 5.14 1.677 1.01、0 0 10.18 1.98
    黄土梁右沟 5.74 1.697 3.84、0 0 38.73 6.74
    下载: 导出CSV

    表  5   泥石流堆积扇数据

    Table  5   Data of debris flow fans

    沟名 堆积坡度
    /(°)
    堆积厚度
    /m
    面积
    /(104 m2
    堆积体积
    /(104 m3
    阿祖沟 10.0 2.5 6.31 3.98
    麻石扎3号沟 10.1 1.2 0.51 0.34
    夺补河5号沟 8.0 1.2 1.45 1.07
    杂排沟 7.0 2.4 4.12 2.94
    黄土梁左沟、右沟 7.6 1.5 1.68 1.12
    下载: 导出CSV
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  • 收稿日期:  2023-12-28
  • 修回日期:  2024-05-06
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