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基于时序InSAR与多时序编录的甘肃黑方台滑坡易发性动态评价

凌晴, 屈杰博, 党星海, 郭琪, 任峻广, 魏玉明

凌晴,屈杰博,党星海,等. 基于时序InSAR与多时序编录的甘肃黑方台滑坡易发性动态评价[J]. 中国地质灾害与防治学报,2025,36(4): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202406018
引用本文: 凌晴,屈杰博,党星海,等. 基于时序InSAR与多时序编录的甘肃黑方台滑坡易发性动态评价[J]. 中国地质灾害与防治学报,2025,36(4): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202406018
LING Qing,QU Jiebo,DANG Xinghai,et al. Dynamic evaluation of landslide susceptibility in Heifangtai, Gansu based on time-series InSAR and multi-temporal cataloguing[J]. The Chinese Journal of Geological Hazard and Control,2025,36(4): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202406018
Citation: LING Qing,QU Jiebo,DANG Xinghai,et al. Dynamic evaluation of landslide susceptibility in Heifangtai, Gansu based on time-series InSAR and multi-temporal cataloguing[J]. The Chinese Journal of Geological Hazard and Control,2025,36(4): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202406018

基于时序InSAR与多时序编录的甘肃黑方台滑坡易发性动态评价

详细信息
    作者简介:

    凌 晴(1983—),女,安徽宿州人, 博士,副教授,主要从事地质灾害防灾减灾方面的研究工作。E-mail:lingqing@chd.edu.cn

    通讯作者:

    屈杰博(1997—),男,甘肃天水人,硕士,主要从事地质灾害防灾减灾方面的研究工作。E-mail:2107024334@qq.com

  • 中图分类号: P642.22

Dynamic evaluation of landslide susceptibility in Heifangtai, Gansu based on time-series InSAR and multi-temporal cataloguing

  • 摘要:

    我国西部山区滑坡灾害频发,准确划分滑坡灾害风险等级对地质灾害至关重要。数据驱动模型已在滑坡易发性评价中取得显著进展,然而在大比例尺小区域尺度下仍面临缺乏动态特征数据和过度依赖样本纯度的挑战。针对此,本文提出了一种基于融合时序InSAR形变与数据驱动模型的综合评价新方法。该方法首先引入时序InSAR形变监测数据作为动态因子,基于多重共线性分析筛选因子,构建滑坡易发性动态评价体系。随后,利用多种机器学习方法构建滑坡易发性评价模型,获取大比例尺小区域滑坡最优易发性预测模型,并以此为基础进行顾及InSAR地表形变和多时序编录的滑坡易发性评价。文章选取甘肃黑方台为研究对象,结果表明:大比例尺小区域尺度下Maxent模型精度表现最优。此外,相对于未融入时序InSAR形变特征,文章融入时序InSAR形变特征的滑坡动态易发性图有效提高识别效果,模型预测精度提升约0.36%,在极高易发区和高易发区中,滑坡占比提升了4.29%。以8 a时间滑坡编录数据为正样本,模型精度为99.26%,表明基于长时序高质量正样本能够显著提高区域易发性预测的精度和可靠性。研究通过对动态评价体系和不同时间维度滑坡数据对滑坡易发性的探索,有助于提高大比例尺(小区域)滑坡风险评价的精确度。

    Abstract:

    Landslide disasters occur frequently in the mountainous regions of western China, and accurate classification of landslide hazard risk levels is crucial for effective geohazard mitigation. Data-driven models have achieved notable progress in landslide susceptibility evaluation; however, challenges persist at large-scale, small-area levels due to limited dynamic features and excessive dependence on sample purity. To address this, this paper proposes a new integrated evaluation method based on the fusion of time-series InSAR deformation data with machine learning models. In this approach, InSAR-derived deformation is introduced as a dynamic factor, and multicollinearity analysis is employed to screen and optimize evaluation indicators. Subsequently, various machine learning methods are used to construct a landslide susceptibility evaluation model, obtain the optimal susceptibility prediction model for large-scale and small-area landslides, and then carry out the landslide susceptibility evaluation while accounting for InSAR-based surface deformation and multi-temporal landslide catalogues. In this paper, Heifangtai in Gansu Province is selected as the study area, and the results show that the Maxent model has the best accuracy in the large-scale, small-area conditions. In addition, compared with models that exclude InSAR data, the incorporation of time-series deformation features improves the model’s prediction accuracy by approximately 0.36%, and increases the proportion of landslides identified within very high and high susceptibility zones by 4.29%. Using an eight-year time landslide catalogued data as positive samples, the model achieves an accuracy of 99.26%, highlighting the effectiveness of long time-series, high-quality data in improving the accuracy and reliability of regional susceptibility assessments. This study contributes a dynamic evaluation framework that enhances the accuracy of landslide susceptibility mapping and offers valuable insights for risk assessment in large-scale, small-area geohazard-prone regions.

  • 我国山区高填方机场具有平整范围大、跨越的地质单元多、周边限制因素多、地形地貌及地质条件复杂、施工环境恶劣、土石方量巨大、挖填高度大、填料性质复杂且可选余地小等特点[12],因此高填方是我国山区机场建设的重要特点。高填方边坡是机场的临空面,其稳定性是高填方机场成败的关键[3],是山区机场建设最为重要的核心技术问题之一。

    重力式挡墙依靠墙体自重来抵抗土体侧压力,可采用浆砌块石或混凝土结构,具有就地取材、施工简单、经济性好、耐久性好、可靠性高、能显著节约土石方及征地面积等优势[45],是最为常用的边坡支挡结构[4, 6],但对于填方边坡其高度一般不超过10 m[7]。随着我国经济社会的发展,超高重力式挡墙在市政、水利、港口等行业中逐渐得到应用,例如贵阳某市政道路采用22 m高的重力式路肩墙[8],深圳市检察院培训基地采用了22 m高的扶壁式钢筋混凝土路肩墙[9],涪陵货运港采用了高约30 m的重力式路肩墙[10],宜兴抽水蓄能电站采用了92.3 m高堆石边坡加45.9 m高钢筋混凝土挡墙相结合的混合坝型[1113]。然而,超高重力式挡墙在岩溶发育场地高填方工程中尚无应用案例报道。

    岩溶发育场地工程地质及水文地质条件复杂,溶沟、溶槽、落水洞、地下溶洞、溶蚀裂隙等喀斯特地貌发育,岩体较为破碎,基覆界面起伏大,岩溶充填物及覆盖层物质组成及力学性质极不均匀,是典型的特殊不良地基[1416],对各类建构筑物及场地稳定性造成较大影响[1519]。山区机场高填方边坡高度高、填筑面积大,填方荷载大且作用复杂,对支挡结构强度及稳定性要求高[3, 2021]。超高混凝土挡墙强度高,能承受较大的土压力,但其自重大、重心高、对沉降敏感,对地基强度及均匀性、墙身材料强度等要求高[1213]。因此,在岩溶发育场地采用超高重力式挡墙进行高填方边坡支挡,可能存在地基承载力不足、不均匀沉降量大、墙身强度不足、边坡深部抗滑稳定性及挡墙稳定性问题突出等技术难题,技术难度大、风险高,限制了其工程应用。

    重庆武隆仙女山机场南端西侧高填方区地形陡峻、岩体较破碎、地下水较丰富,覆盖型岩溶广泛发育,岩溶面积大、深度深,是典型的岩溶发育场地。受坡脚天然气管道限制,项目采用了最大高度为49.5 m(含岩溶混凝土换填高度)的超高重力式路堤墙方案,挡墙高度在国内外尚未见报道。为了解决深厚岩溶对高挡墙及高边坡稳定性的影响问题,通过物探、钻探及施工地质调查等方式详细查明了岩溶发育情况,通过数值模拟分析了不同岩溶换填深度下边坡破坏模式、边坡及挡墙稳定性、挡墙应力及变形等,确定了合理的岩溶换填深度。目前武隆机场已通航3年多,高挡墙及高边坡运行状态良好。理论及实践表明,采用局部换填方案改善了岩溶地基不均匀性,降低了挡墙应力集中效应,大幅提高了挡墙及边坡稳定性,解决了超高重力式挡墙在岩溶发育场地中的应用难点。研究成果对于高填方工程项目规划、高挡墙设计及施工、岩溶发育场地地基处理具有较大的参考价值。

    武隆机场飞行区等级为4C,跑道长2800 m,机场标高为1743.69 m。机场土石方填方量约21.51×106 m3,挖方量约19.40×106 m3,最大垂直填方高度约65 m,填方边坡最大高度约107 m,是典型的高填方机场。

    机场位于大娄山期二级剥夷面上,总体地势南高北低,东高西低。南端和西侧紧靠台地边缘,为中等起伏台地地貌。受流水深切割的影响,沟谷两侧坡度较大,地形陡峻。研究区位于机场跑道南端西侧,为一条“V”字型冲沟上(图1),纵向平均坡度约25°,两侧沟壁坡度40°~75°,地形条件复杂。边坡坡顶垂直填方高度最大约57 m,在距离坡顶约127 m处有一条大致与跑道平行的天然气管线,边坡不具备放坡和反压条件,高填方边坡稳定性问题非常突出。经综合比选,采用超高重力式挡墙加高路堤方案。

    图  1  研究区地形地貌全景图
    Figure  1.  Panoramic view of the study area's topography

    重力式挡墙地面以上最大净高为41.2 m,总高度最大49.5 m(含岩溶换填),墙身呈折线形,见图2(a),顶宽2.0 m,南侧墙体长58.8 m,北侧长73.3 m,采用C25混凝土结构。挡墙结构与衡重式挡墙类似,但中下部因地制宜,根据各处地形、基岩及岩溶情况不断变化,结构形式较为复杂。由于挡墙高度大,对地基承载力及均匀性要求高,对岩溶充填物采用开挖一定深度后回填混凝土,并与墙身整体浇筑的地基处理方案。挡墙中部设一道排水廊道和一排泄水孔。

    图  2  工程总平面图及典型工程地质剖面图
    Figure  2.  General plan and typical geological profile of the engineering

    挡墙后高填方边坡最高为65.71 m,按1∶1.4的坡比自然放坡,每15 m高设置2 m宽马道,见图2(b)。路堤范围内清除覆盖层至强风化基岩,再开挖抗滑台阶提高基覆界面强度。填料采用中风化灰岩或硅质岩破碎料。填筑区周边及墙趾处设截排水沟,马道上设一道混凝土种植槽兼做截水沟。

    武隆机场区域上位于仙女山背斜北西翼,岩层呈单斜状,走向北东-南西,缓倾北西,倾向260°~300°,倾角5°~12°。场区基岩为二叠系上统吴家坪组,以灰岩和硅质岩为主,薄−中厚层,发育N35°E和N55°W两组陡倾节理,倾角多在70°以上。根据勘察资料及现场调查,强风化灰岩和硅质岩体结构破碎,中风化岩体结构为破碎−较破碎。

    研究区基岩以灰岩和硅质岩为主,局部可见黏土岩夹层,产状为290°∠8°,薄−中厚层,强风化厚约5 m,岩体破碎,岩块强度高。硅质岩和黏土岩等相对隔水层出露地带发育股状流水,流量随季节变化较大,有利于岩溶发育[16, 22]。场区覆盖层厚0~11.3 m,包括耕植土、粉质黏土、碎石土以及天然气管线施工形成的素填土,性质差,见图2(b)。

    挡墙基础范围发现有5处岩溶,其中1号和2号岩溶体积很小,基础开挖即可清除,对工程无影响。3—5号岩溶位于挡墙基础中部,上部被第四系土覆盖,为覆盖型岩溶。岩溶整体形态呈椭圆形,尺寸分别为42 m×15 m、19 m×15 m、54 m×21 m,投影面积分别为463 m2、171 m2、802 m2,岩溶占挡墙基础面积的45%以上,见图2(a)。

    为了查明岩溶形态及深度,对3—5号开展了钻探和物探,钻孔及物探典型剖面位置见图2(a)。3号岩溶区钻孔深17 m,上部9.1 m为岩溶充填物,其下为破碎灰岩;4号岩溶区钻孔深27.5 m,表层3.2 m为充填物,3.2~9.1 m为破碎灰岩,9.1~27.5 m为充填物,钻探未探明岩溶基底;5号岩溶区设置2个钻孔,30 m深钻孔全为充填物,未探明岩溶基底,32 m深钻孔上部27.6 m为充填物,其下为基岩;各岩溶充填物均为浅黄色含砂碎石土,含水量高,强度较低。物探结果综合分析表明(图3),上述3个岩溶形状不规则,底部埋深预计超过35 m,深部有岩溶通道互相连接。

    图  3  高密度电法剖面图
    Figure  3.  Profiles determined by high-density electrical technique

    现场施工中开挖发现(图4),岩溶发育区覆盖层较厚,基岩埋深5~9 m。岩溶类型主要为溶槽、溶沟及溶蚀裂隙,呈长条形展布,各岩溶通过溶蚀裂隙、溶洞等通道连接。溶沟、溶槽最大深度超过20 m,侧壁近直立,溶腔尺寸随深度增加呈现逐渐减小趋势。岩溶沟槽长轴大致呈N30°E方向,与场区陡倾节理方向基本一致,岩溶受岩体结构面控制。溶蚀沟槽内填充含水量高的浅黄色含砂碎石土,岩溶之间基岩顶部结构破碎。

    图  4  挡墙基础岩溶开挖图
    Figure  4.  Excavation pictures of the karst for foundation of the retaining wall

    本工程地质结构复杂,挡墙结构与普通重力式挡墙有明显不同,填料、基岩及其结构面、挡墙结构及材料、岩溶、不同材料界面特性等都对边坡及挡墙稳定性产生影响,传统的极限平衡法难以准确确定破坏模式及稳定状态,需要开展数值模拟。

    数值模拟采用Optum G2软件,是一款集极限分析和有限元分析于一体的岩土分析软件,具有操作简单、网格自动化程度高、支持有限元极限分析、收敛性强等特点,在复杂地质条件及复杂支挡结构破坏模式分析、可靠度计算等方面具有优势[23]。Optum G2可考虑基岩层面、不同材料接触面(包括挡墙与基岩、岩溶充填土、填料,基岩与填料等)力学性质,是本工程理想的分析软件。LI等[24]、YANG等[25]分别分析了武隆机场填料、填料与基岩界面力学特性,相关参数参照选取;基岩及结构面参数按经验参考《工程岩体分级标准》[26]选取,岩溶充填物参数选自勘察报告;挡墙与不同材料接触面参数参照GEO软件帮助文档选取;各计算参数见表1

    表  1  数值模拟计算参数
    Table  1.  Summary of simulation model parameters
    岩土性质 本构模型 容重/(kN·m−3 黏聚力/kPa 内摩擦角/(°) 弹性模量/MPa 泊松比
    填料 摩尔库伦 22.5 50 35 60 0.30
    高挡墙 线弹性 24.0 28000 0.20
    岩溶充填物 摩尔库伦 18.2 20 13 10 0.32
    灰岩 摩尔库伦 26.5 200 40 10000 0.25
    灰岩层面 摩尔库伦 60 25
    节理面 摩尔库伦 20 35
    挡墙-灰岩接触面 摩尔库伦 0 35
    挡墙-填料接触面 摩尔库伦 0 30
    挡墙-岩溶充填物接触面 摩尔库伦 0 15
    填料-灰岩接触面 摩尔库伦 45 35
    下载: 导出CSV 
    | 显示表格

    根据工程实际情况,选取岩溶发育范围大、宽度宽(总宽约40 m)、岩溶深度深(最深约35 m)、挡墙总高度最高(含岩溶换填的总高度为49.5 m)、溶沟溶槽发育(共计4条)的剖面进行计算,详见图2(b)及图5。填筑体底部覆盖层需全部清除,因此按照第四系土清除后的原地面进行建模。模型设置位移边界条件,即底部为完全固定边,两侧为半固定边(水平方向固定、竖向可自由变形)。

    图  5  挡墙结构及岩溶处理示意图(单位:m)
    Figure  5.  Diagram of retaining wall structure and karst treatment (unit: m)

    数值模拟内容包括不同岩溶换填深度(图5)下边坡破坏模式、边坡整体稳定性、应力及变形,并提取挡墙墙背土压力计算挡墙稳定性。需要特别注意的是,填土为粗粒土,沉降大部分在施工过程中完成;采用的岩土本构模型属于理想弹塑性模型,不能考虑蠕变效应,因此填筑体变形计算结果仅供参考。

    当岩溶换填深度较大时,由于换填深度范围内回填的混凝土与挡墙一起整体浇筑,受溶槽间岩桥的阻挡(图5),挡墙不具备沿基底发生滑移的条件(沿填筑体及挡墙底可能发生的深部抗滑稳定性在整体稳定性中考虑),因此不需要验算挡墙抗滑移稳定性。

    挡墙高度高,承受的土压力大,需要验算抗倾覆稳定性。墙背由5个面组成,第i面承担的土压力为Pi图5),Pi到挡墙墙趾的水平、竖向距离为xiyi。挡墙自重为G,其到挡墙墙趾的水平、竖向距离为xGyG,则挡墙抗倾覆稳定性按式(1)计算:

    K=GxG+PiyxiPixyi (1)

    经典塑性力学上下限解可在不引入任何假定的前提下,通过上下限逼近边坡安全系数真实解[27]。通过对模型网格单元细分和基于剪切耗散的自适应加密,可较为准确地确定边坡破坏模式和整体安全系数。针对不同的岩溶换填深度(0~31 m)分别进行了模拟和分析。

    当岩溶换填深度较浅时,边坡潜在破坏面由圆弧和多段折线组成,破坏模式较为复杂。圆弧面位于填筑体内部,折线面由基岩主动破裂面、基岩层面、挡墙底边(即挡墙与岩溶充填物之间界面)以及挡墙前被动破裂面组成,墙前被动区、挡墙底边界、基岩层面相对较为薄弱见图6(a)和图6(c)。此外,墙背下卸荷台边缘处存在第二破裂面,与已有研究一致[28]

    图  6  不同换填深度下边坡破坏模式及整体稳定性
    Figure  6.  Slope failure modes and overall stability under different replacement depths

    当岩溶换填深度大时,边坡潜在破坏面为填筑体内部的圆弧面见图6(b),破坏模式较为简单。岩溶换填深度的增加可有效消除基岩层面、岩溶等薄弱带存在的安全隐患,有利于边坡稳定性。经计算,当换填深度为15 m时,下限解对应的破坏模式为圆弧和多段折线组成,上限解为圆弧面,换填深度大于15 m时上下限解对应的破坏面均为沿着填筑体内的圆弧面。

    有限元上下限解计算表明,当换填深度低于15 m时,随着岩溶换填深度的增加,边坡整体安全系数大致呈线性增加;换填深度大于15 m时,安全系数不再变化,见图6(d)。上述结果与破坏模式分析结果一致,即换填深度大于15 m时,边坡最危险滑面为墙后填筑体内部的圆弧面,与挡墙无关,因此安全系数不会变化。

    当换填深度不小于7 m时,边坡安全系数上下限解均大于1.35,可满足民航规范要求[29],因此岩溶换填深度不应小于7 m。

    采用弹塑性模型对边坡及挡墙应力进行了有限元分析(应力以拉为正、压为负),典型换填深度下的应力及塑性应变结果见图7所示,关键点(位置见图5)应力随换填深度的变化规律见图8(a)

    图  7  挡墙应力及塑性应变等值线图(应力单位:kPa)
    Figure  7.  Contour maps of the retaining wall stresses and plastic strains (stress unit: kPa)
    图  8  换填深度与关键点应力及变形关系曲线
    Figure  8.  Relationship curves between replacement depths and stresses/deformations of key points

    面坡坡脚处(A3点)是挡墙压应力的主要集中点,见图7(a)左、图7(b)左,随着换填深度的增加最大压应力逐渐减小。换填深度小于10 m时,压应力降低迅速;大于10 m时呈缓慢下降趋势。换填深度大于5 m时,最大压应力小于20 MPa,混凝土强度满足要求。

    换填深度浅时,拉应力的主要集中点在挡墙基底溶槽之间的基岩接触区域见图7(a)中。由于岩溶充填物强度低,能承受的荷载很小,挡墙基底受力特点类似于多点竖向固定的简支梁,简支点需承受较大的弯拉应力。从体积塑性应变图也可看出,岩溶之间的基岩顶部出现大片塑性变形区域,与挡墙受力特点一致,见图7(a)右。当换填深度较大时,挡墙基础埋深大、与基岩接触面积大,应力通过基岩扩散后,挡墙底附加应力小,其受力与普通衡重式挡墙类似,因此拉应力集中点出现在卸荷台转角处,见图7(b)中。

    挡墙换填深度与上、下卸荷台(A1、A2处)小主应力关系曲线可知,见图8(a),随换填深度增加卸荷台拉应力呈现增加趋势。这是由于随换填深度增加挡墙自重加大,限制了土体变形,土压力逐渐增加所致。经计算,换填深度大于7 m后,上卸荷台拉应力趋于稳定,稳定值约1.86 MPa,C25混凝土抗拉强度满足要求。

    岩溶之间的基岩(A4)应力分析表明,随换填深度增加其应力逐渐减小,见图7图8(a),有利于地基稳定性。

    体积塑性应变计算表明,岩溶换填深度浅时,地基发生大面积塑性应变,见图7(a)右,地基稳定性差。换填深度加大后,仅在局部尖角处出现塑性变形,见图7(b)右。

    挡墙水平位移、边坡坡顶变形与换填深度的关系见图8(b)。随换填深度增加,挡墙水平位移、坡顶变形均逐渐减小,与经验及应力分析结果一致。当换填深度大于7 m时,挡墙及坡顶水平位移、坡顶竖向位移趋于稳定,稳定值分别约为6 mm、20 cm、50 cm。

    挡墙及地质条件复杂,采用有限元计算挡墙土压力。由于换填深度增加导致挡墙自重增加,加之埋深加大后地基水平抗力增加,挡墙水平位移逐渐减小,限制了土体变形,因此墙背与填土接触面的土压力整体呈现增加趋势,见图9(a)。当换填深度不小于7 m时,随换填深度增加,挡墙位移趋于稳定,见图8(b),因此土压力也趋于稳定,见图9(a)。

    图  9  换填深度与挡墙受力及挡墙稳定性关系曲线
    Figure  9.  Relationship curves between replacement depths and retaining wall forces and stability

    P5为墙背与基岩接触面压力,当不换填时挡墙水平位移很大(约36 cm),主要受力点位于挡墙中前缘,岩石压力较小;当换填深度较小时,挡墙水平位移大幅降低,由于岩溶充填物能承受荷载小,P5所在面作为支撑面承担较大的压力,因此P5呈现增加趋势;随着换填深度进一步增加,岩溶侧壁支撑作用逐渐加大,岩石压力逐渐减小,因此P5随后呈减小趋势。

    根据土压力计算成果,对挡墙进行了抗倾覆稳定性计算,见图9(b)。由图可知,挡墙稳定性随换填深度增加呈现先减少后增加趋势。岩溶不换填时,挡墙水平位移大,土压力小,因此抗倾覆稳定性较大;换填深度小幅增加后,挡墙水平位移大幅降低,土压力增加较快,因此挡墙稳定性降低;换填深度进一步增加后,挡墙水平位移及土压力趋于稳定,但挡墙自重逐步增加,因此抗倾覆稳定性逐渐增加。经计算,挡墙抗倾覆稳定性均大于1.5,满足规范要求[7],抗倾覆稳定性不是控制性因素。

    此外,随换填深度的增加,挡墙基础埋深加大,重心高度(图5yG)逐渐降低,降低幅度呈先快后慢的趋势,见图9(b)。降低重心高度可进一步提高挡墙基础受力的均匀性,提高挡墙稳定性,降低偏心荷载对挡墙的不利影响。

    根据理论计算,岩溶换填深度不小于7 m时,挡墙变形较小,边坡整体安全系数、挡墙抗倾覆稳定性、挡墙及地基强度均能满足规范要求,因此岩溶换填深度不宜小于7 m。

    考虑到岩溶地基的不确定性,按照换填深度不低于10 m进行控制。当岩溶深度低于10 m时开挖至岩溶槽底,大于10 m时开挖10~20 m,岩溶上下尺寸变化小时开挖深度取大值。开挖至设计标高后,若底部仍存在充填物应灌浆处理以提高承载力。考虑到基岩岩体较破碎,对挡墙基础固结灌浆、岩溶边壁及基础设短锚钉的构造加强措施,锚钉与墙身连为一体。岩溶区域底部设置3 m厚底板,配双层钢筋网,进一步增加基础整体性。考虑到挡墙转角处存在明显的应力集中,特别是墙背卸荷台存在拉应力集中,在墙面附近配置钢筋,并在转角处对配筋适当加强。

    项目从2018年1月初开始施工,2019年5月中旬挡墙基础开挖与岩溶处理(含岩溶混凝土回填)基本完成。挡墙上部结构于2019年6月13日开始施工,2020年6月12日完成全部混凝土浇筑。墙身混凝土浇筑过程中,墙后土石方也逐步回填,2020年6月29日,墙后高填方边坡回填完成。

    挡墙修建完成后,在墙顶布置了12个变形监测点,在填方边坡坡面布置了3个监测剖面共计8个监测点。此处选取边坡高度最高的BW04—BW06剖面及挡墙变形较大的BDW07点作为代表进行分析,各点位置如图10(a)所示。边坡监测从2020年6月16日开始,至2020年9月9日结束;高挡墙有三个监测点于2020年6月16日开始监测,其余开始于2020年7月5日,至2020年9月9日结束;各点变形监测成果如图10(b)所示。

    图  10  监测点位置图及其变形时程曲线
    Figure  10.  Map of monitoring point locations and their deformation time-history curves

    监测结果表明:(1)在高填方边坡施工过程中,边坡变形增长较快;填筑体施工完成后,边坡变形较小,变形曲线很快趋于收敛,表明填筑体固结在填筑完成后很快完成。(2)填筑体最大水平位移约19.4 mm,最大沉降量约12.7 mm,填筑体施工完成后水平及竖向位移最大值均不大于4 mm,变形量及变形速率很小,边坡稳定性良好。(3)填筑体施工完成后,BDW07水平位移最大值约为3.3 mm,变形曲线收敛良好,高挡墙稳定性良好;挡墙变形有轻微的上下波动,预计是不同时间温差导致,与已有研究一致[11]。(4)高挡墙所有8个变形监测点数据表明,挡墙最大水平位移为3.3 mm,最大竖向位移为3.9 mm,与数值模拟结果基本吻合。

    目前机场已通航3 a,在此期间武隆机场对高挡墙区域进行了持续的现场巡查,未见任何不良迹象,高边坡及高挡墙状态良好。

    (1)高挡墙范围内广泛发育覆盖型岩溶,面积占挡墙基础的45%以上,以溶槽、溶沟及溶蚀裂隙为主,长轴与场区陡倾结构面方向基本一致。岩溶最大深度大于30 m,全填充,侧壁陡倾,基岩地层顺倾、岩体较破碎,地基极不均匀,高挡墙及高边坡稳定性问题极为突出。根据工程实际采用超高重力式路堤墙及岩溶地基局部混凝土换填方案,可有效解决项目重大工程技术难题。

    (2)岩溶处理深度浅时,边坡潜在破坏面由填筑体内部的圆弧面、岩体主动破裂面、墙底与岩溶充填物的接触面、基岩层面及墙前被动破坏面组成,且墙后出现第二破裂面,破坏模式复杂。处理深度大于15 m时,边坡潜在破坏面为墙后填筑体内的圆弧面,破坏模式简单。

    (3)岩溶处理深度不小于7 m时,随换填深度的增加,墙背土压力、挡墙及填土变形、卸荷台拉应力及面墙墙脚压应力均趋于稳定,边坡整体安全系数满足规范要求,挡墙及地基应力不超材料强度,因此建议岩溶换填深度不小于7 m。

    (4)当岩溶换填深度较大时,岩溶换填混凝土、岩溶间基岩与高挡墙形成统一的整体,极大提高了边坡及挡墙稳定性,实现了岩溶地基溶沟溶槽的合理化利用及不良地质的有效防治。

    (5)工程监测显示,填筑体施工完成后边坡及高挡墙水平及竖向位移最大值均小于4 mm,变形曲线迅速收敛。监测及运营实践表明,边坡及挡墙稳定状态良好,岩溶局部换填方案可有效解决超高重力式挡墙在岩溶发育场地中的应用难点。

  • 图  1   研究区地理位置图

    Figure  1.   Location of the study area

    图  2   技术路线图

    Figure  2.   Technology roadmap of the study

    图  3   多源SAR数据集小区域滑坡时序监测流程图

    Figure  3.   Flowchart of small-area landslides time-series monitoring using multi-source SAR datasets

    图  4   滑坡易发性预测模型ROC曲线

    Figure  4.   ROC curves of landslide susceptibility prediction models

    图  5   滑坡易发性评价结果

    注:a—c为2011、2015、2019年滑坡易发性评价未附加形变;d—f为2011、2015、2019年滑坡易发性评价附加形变。

    Figure  5.   Land susceptibility evaluation results

    图  6   顾及时间的滑坡编录数据易发性评价

    注:a为2019年历史滑坡数据易发性预测;b为2015—2019时间区间滑坡编录数据易发性预测;c为2011—2019时间区间滑坡编录数据易发性预测。

    Figure  6.   Time-sensitive assessment of landslide susceptibility based on catalogued data

    图  7   局部重点区域滑坡易发性风险等级对比分析图

    注:a为局部重点地区灾害前后影像;b为局部重点地区2014—2019地表形变;c为局部重点地区2019年滑坡易发性评价未附加形变;d为局部重点地区2019年滑坡易发性评价附加形变。

    Figure  7.   Comparative analysis of landslide susceptibility in key areas

    图  8   不同滑坡易发分区面积比重

    Figure  8.   Area proportion of different landslide susceptibility zones

    图  9   模型中评价因子的贡献率

    Figure  9.   Relative contributions of evaluation factors to the landslide susceptibility model

    表  1   环境因子来源

    Table  1   Sources of environmental factors

    类型 评价因子 数据源 类型 精度
    地形地貌[33] 高程、坡度、坡向、曲率、平面曲率、剖面曲率、地表粗糙度、地形起伏度 无人机高精度DEM tiff 0.5 m
    气象水文[34] 距水系距离、距水渠距离、湿度指标、
    温度指标、干度指标、多年平均降雨
    中科院成都山地灾害与环境研究所 tiff 30 m
    无人机高精度DOM、高精度谷歌影像 shp 1∶5000
    Landsat-8和Landsat-5 tiff 30 m
    地表覆盖 植被覆盖 Landsat-8和Landsat-5 tiff 30 m
    人类活动[35] 距建筑物距离、距道路距离 无人机高精度DOM、高精度谷歌影像 shp 1∶5000
    地表形变[36] 形变速率 多源SAR数据 tiff 30 m
    下载: 导出CSV

    表  2   多重共线性检验

    Table  2   Multicollinearity test of evaluation factors

    评价因子 剔除曲率因子前 剔除曲率因子后
    T VIF T VIF
    距水系距离 0.393 2.545 0.395 2.534
    坡向 0.921 1.086 0.922 1.085
    地形起伏度 0.409 2.445 0.411 2.435
    距道路距离 0.439 2.280 0.442 2.264
    地表粗糙度 0.456 2.191 0.459 2.181
    距建筑距离 0.439 2.279 0.456 2.193
    湿度指标 0.200 4.992 0.201 4.972
    干度指标 0.457 2.187 0.457 2.186
    植被覆盖 0.484 2.067 0.490 2.043
    坡度 0.347 2.883 0.348 2.874
    多年平均降雨 0.479 2.086 0.482 2.075
    高程 0.544 1.839 0.550 1.817
    距水渠距离 0.463 2.158 0.467 2.142
    曲率 0.017 59.564
    剖面曲率 0.047 21.128 0.584 1.713
    形变速率 0.575 1.738 0.576 1.738
    平面曲率 0.053 18.869 0.573 1.744
    温度指标 0.620 1.613 0.620 1.613
    下载: 导出CSV

    表  3   不同正样本集

    Table  3   Characteristics of different positive sample sets

    不同样本 2011年历史
    滑坡数据
    2015年历史
    滑坡数据
    2019年历史
    滑坡数据
    2015—2019年时间区间
    滑坡编录数据
    2011—2019年时间区间
    滑坡编录数据
    滑坡数量/个 28 64 79 15 51
    下载: 导出CSV

    表  4   预测结果与历史滑坡数量占比情况和AUC

    Table  4   Comparison of forecast results, historical landslide proportions, and AUC values

    数据年份 类型 AUC值/% 极低易发区/% 低易发区/% 中等易发区/% 高易发区/% 极高易发区/%
    2011 未附加InSAR形变 98.89 0 0 3.57 35.71 60.71
    附加InSAR形变 99.23 0 0 0 35.71 64.29
    2015 未附加InSAR形变 96.52 0 6.25 12.50 25 56.25
    附加InSAR形变 96.80 0 1.56 15.63 28.13 54.69
    2019 未附加InSAR形变 98.47 0 4.29 21.43 21.43 52.86
    附加InSAR形变 98.83 0 5.71 15.71 25.71 52.86
    下载: 导出CSV

    表  5   2015—2019滑坡编录数据在2015年附加InSAR形变与未附加InSAR形变易发性统计分析

    Table  5   Comparative statistics analysis of 2015—2019 landslide catalogued data with and without InSAR deformation in 2015 susceptibility evaluation

    分区 2015年附加InSAR形变 2015年未附加InSAR形变
    占总面积比例/% 灾害点占比/% 频率比 占总面积比例/% 灾害点占比/% 频率比
    极低易发区 84.47 0 0 84.30 0 0
    低易发区 7.33 0 0 7.17 13.33 1.860
    中易发区 3.76 20 5.318 3.81 26.67 6.993
    高易发区 2.85 40 14.048 2.85 40 14.040
    极高易发区 1.59 40 25.100 1.87 20 10.684
    下载: 导出CSV

    表  6   顾及时间的不同滑坡编录数据的AUC

    Table  6   AUC values of landslide susceptibility models based on different temporal landslide datasets

    滑坡数据 2019年历史滑坡数据 2015—2019年时间区间滑坡编录数据 2011—2019年时间区间滑坡编录数据
    滑坡数/个 79 15 51
    AUC值/% 98.83 93.33 99.26
    下载: 导出CSV
  • [1] 周超. 集成时间序列InSAR技术的滑坡早期识别与预测研究[D]. 武汉:中国地质大学,2018. [ZHOU Chao. Study on early identification and prediction of landslide by integrating time series InSAR technology[D]. Wuhan:China University of Geosciences,2018. (in Chinese with English abstract)]

    ZHOU Chao. Study on early identification and prediction of landslide by integrating time series InSAR technology[D]. Wuhan: China University of Geosciences, 2018. (in Chinese with English abstract)

    [2] 吴宏阳,周超,梁鑫,等. 基于 XGBoost 模型的三峡库区燕山乡滑坡易发性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5):141 − 152. [WU Hongyang,ZHOU Chao,LIANG Xin,et al. Assessment of landslide susceptibility mapping based on XGBoost model:A case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5):141 − 152. (in Chinese with English abstract)]

    WU Hongyang, ZHOU Chao, LIANG Xin, et al. Assessment of landslide susceptibility mapping based on XGBoost model: A case study of Yanshan Township[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(5): 141 − 152. (in Chinese with English abstract)

    [3] 许强,汤明高,徐开祥,等. 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,2008,27(6):1104 − 1112. [XU Qiang,TANG Minggao,XU Kaixiang,et al. Research on space-time evolution laws and early warning-prediction of landslides[J]. Chinese Journal of Rock Mechanics and Engineering,2008,27(6):1104 − 1112. (in Chinese with English abstract)]

    XU Qiang, TANG Minggao, XU Kaixiang, et al. Research on space-time evolution laws and early warning-prediction of landslides[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(6): 1104 − 1112. (in Chinese with English abstract)

    [4] 窦杰,向子林,许强,等. 机器学习在滑坡智能防灾减灾中的应用与发展趋势[J]. 地球科学,2023,48(5):1657 − 1674. [DOU Jie,XIANG Zilin,XU Qiang,et al. Application and development trend of machine learning in landslide intelligent disaster prevention and mitigation[J]. Earth Science,2023,48(5):1657 − 1674. (in Chinese with English abstract)]

    DOU Jie, XIANG Zilin, XU Qiang, et al. Application and development trend of machine learning in landslide intelligent disaster prevention and mitigation[J]. Earth Science, 2023, 48(5): 1657 − 1674. (in Chinese with English abstract)

    [5] 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. [ZHANG Qin,ZHAO Chaoying,CHEN Xuelong. Progress and development trend of multi-source remote sensing technology for early identification of geologic hazards[J]. Journal of Srveying and Mapping,2022,51(6):885 − 896. (in Chinese with English abstract)]

    ZHANG Qin, ZHAO Chaoying, CHEN Xuelong. Progress and development trend of multi-source remote sensing technology for early identification of geologic hazards[J]. Journal of Srveying and Mapping, 2022, 51(6): 885 − 896. (in Chinese with English abstract)

    [6] 王启盛,熊俊楠,程维明,等. 耦合统计方法、机器学习模型和聚类算法的滑坡易发性评价方法[J]. 地球信息科学学报,2024,26(3):620 − 637. [WANG Qisheng,XIONG Junnan,CHENG Weiming,et al. Landslide susceptibility mapping methods coupling with statistical methods,machine learning models and clustering algorithms[J]. Journal of Geo-Information Science,2024,26(3):620 − 637. (in Chinese with English abstract)]

    WANG Qisheng, XIONG Junnan, CHENG Weiming, et al. Landslide susceptibility mapping methods coupling with statistical methods, machine learning models and clustering algorithms[J]. Journal of Geo-Information Science, 2024, 26(3): 620 − 637. (in Chinese with English abstract)

    [7] 朱宇航,黄海峰,殷坤龙,等. 基于滑坡破坏模式分析的易发性评价——以三峡库区首段泄滩河左岸为例[J]. 中国地质灾害与防治学报,2023,34(2):156 − 166. [ZHU Yuhang,HUANG Haifeng,YIN Kunlong,et al. Evaluation of landslide susceptibility based on landslide failure mode analysis:A case study of the left bank of Xietan River in the first section of Three Gorges Reservoir[J]. The Chinese Journal of Geological Hazard and Control,2023,34(2):156 − 166. (in Chinese with English abstract)]

    ZHU Yuhang, HUANG Haifeng, YIN Kunlong, et al. Evaluation of landslide susceptibility based on landslide failure mode analysis: A case study of the left bank of Xietan River in the first section of Three Gorges Reservoir[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(2): 156 − 166. (in Chinese with English abstract)

    [8] 付圣,陈丽霞,黎丰收,等. 鄂西南山区小区域大比例尺滑坡灾害易发性及其精度评价[J]. 山地学报,2017,35(4):517 − 526. [FU Sheng,CHEN Lixia,LI Fengshou,et al. Large scale landslides susceptibility and accuracy assessment in mountainous counties[J]. Mountain Research,2017,35(4):517 − 526. (in Chinese with English abstract)]

    FU Sheng, CHEN Lixia, LI Fengshou, et al. Large scale landslides susceptibility and accuracy assessment in mountainous counties[J]. Mountain Research, 2017, 35(4): 517 − 526. (in Chinese with English abstract)

    [9] 于宪煜. 基于多源数据和多尺度分析的滑坡易发性评价方法研究[D]. 武汉:中国地质大学,2016. [YU Xianyu. Study on landslide susceptibility evaluation method based on multi-source data and multi-scale analysis[D]. Wuhan:China University of Geosciences,2016. (in Chinese with English abstract)]

    YU Xianyu. Study on landslide susceptibility evaluation method based on multi-source data and multi-scale analysis[D]. Wuhan: China University of Geosciences, 2016. (in Chinese with English abstract)

    [10] 周鑫. 金沙江上游茂顶河段滑坡成因机制及敏感性研究[D]. 吉林大学,2019. [ZHOU Xin. Study on the genesis mechanism and sensitivity of landslides in Maoding section of the upper reaches of Jinsha River[D]. Jilin University,2019. (in Chinese with English abstract)]

    ZHOU Xin. Study on the genesis mechanism and sensitivity of landslides in Maoding section of the upper reaches of Jinsha River[D]. Jilin University, 2019. (in Chinese with English abstract)

    [11] 李浩宾. 基于GIS的大比例尺滑坡危险性评价方法研究——以普格县为例[D]. 成都:成都理工大学,2016. [LI Haobin. Study on risk assessment method of large-scale landslide based on GIS:A case study of Puge County[D]. Chengdu:Chengdu University of Technology,2016. (in Chinese with English abstract)]

    LI Haobin. Study on risk assessment method of large-scale landslide based on GIS: A case study of Puge County[D]. Chengdu: Chengdu University of Technology, 2016. (in Chinese with English abstract)

    [12] 曾韬睿,邬礼扬,金必晶,等. 基于stacking集成策略和SBAS-InSAR的滑坡动态易发性制图[J]. 岩石力学与工程学报,2023,42(9):2266 − 2282. [ZENG Taorui,WU Liyang,JIN Bijing,et al. Landslide dynamic susceptibility mapping based on stacking ensemble strategy and SBAS-InSAR[J]. Chinese Journal of Rock Mechanics and Engineering,2023,42(9):2266 − 2282. (in Chinese with English abstract)]

    ZENG Taorui, WU Liyang, JIN Bijing, et al. Landslide dynamic susceptibility mapping based on stacking ensemble strategy and SBAS-InSAR[J]. Chinese Journal of Rock Mechanics and Engineering, 2023, 42(9): 2266 − 2282. (in Chinese with English abstract)

    [13]

    CALVELLO M,PEDUTO D,ARENA L. Combined use of statistical and DInSAR data analyses to define the state of activity of slow-moving landslides[J]. Landslides,2017,14(2):473 − 489. DOI: 10.1007/s10346-016-0722-6

    [14] 高秉海,何毅,张立峰等. 顾及In SAR形变的CNN滑坡易发性动态评估——以刘家峡水库区域为例[J]. 岩石力学与工程学报,2023,42(2):450 − 465. [GAO Binghai,HE Yi,ZHANG Lifeng,et al. Dynamic evaluation of landslide susceptibility by CNN considering InSAR deformation:A case study of Liujiaxia reservoir[J]. Journal of Rock Mechanics and Engineering,2023,42(2):450 − 465. (in Chinese with English abstract)]

    GAO Binghai, HE Yi, ZHANG Lifeng, et al. Dynamic evaluation of landslide susceptibility by CNN consideringInSAR deformation:A case study of Liujiaxia reservoir[J]. Journal of Rock Mechanics and Engineering, 2023, 42(2): 450 − 465. (in Chinese with English abstract)

    [15]

    REICHENBACH P,ROSSI M,MALAMUD B D,et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews,2018,180:60 − 91. DOI: 10.1016/j.earscirev.2018.03.001

    [16]

    GUPTAK S,JHUNJHUNWALLA M,BHARDWAJ A,Shukla D P. Data imbalance in landslide susceptibility zonation:under-sampling for class-imbalance learning[J]. ISPRS - International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2020,51 − 57.

    [17] 付智勇,李典庆,王顺等. 基于多时空滑坡编录和TrAdaBoost迁移学习的滑坡易发性评价[J]. 地球科学,2023,48(5):1935 − 1947. [FU Zhiyong,LI Dianqing,WANG Shun,et al. Landslide susceptibility evaluation based on m-ulti-temporal landslide cataloging and TrAdaBoost migration learning[J]. Earth Science,2023,48(5):1935 − 1947. (in Chinese with English abstract)]

    FU Zhiyong, LI Dianqing, WANG Shun, et al. Landslide susceptibility evaluation based on m-ulti-temporal landslide cataloging and TrAdaBoost migration learning[J]. Earth Science, 2023, 48(5): 1935 − 1947. (in Chinese with English abstract)

    [18] 吕蓓茹,彭玲,李樵民. 顾及样本敏感性的滑坡易发性评价[J]. 测绘通报,2022(11):20 − 25. [LÜ Beiru,PENG Ling,LI Qiaomin. Landslide susceptibility evaluation considering sample sensitivity[J]. Bulletin of Surveying and Mapping,2022(11):20 − 25. (in Chinese with English abstract)]

    LÜ Beiru, PENG Ling, LI Qiaomin. Landslide susceptibility evaluation considering sample sensitivity[J]. Bulletin of Surveying and Mapping, 2022(11): 20 − 25. (in Chinese with English abstract)

    [19] 刘纪平,梁恩婕,徐胜华等. 顾及样本优化选择的多核支持向量机滑坡灾害易发性分析评价[J]. 测绘学报,2022,51(10):2034 − 2045. [LIU Jiping,LIANG Enjie,XU Shenghua,et al. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J]. Journal of Srveying and Mapping,2022,51(10):2034 − 2045. (in Chinese with English abstract)] DOI: 10.11947/j.AGCS.2022.20220326

    LIU Jiping, LIANG Enjie, XU Shenghua, et al. Multi-kernel support vector machine considering sample optimization selection for analysis and evaluation of landslide disaster susceptibility[J]. Journal of Srveying and Mapping, 2022, 51(10): 2034 − 2045. (in Chinese with English abstract) DOI: 10.11947/j.AGCS.2022.20220326

    [20] 吴宏阳, 周超, 梁鑫, 等. 基于样本优化策略的滑坡易发性评价[J]. 武汉大学学报(信息科学版),2024,49(8):1492 − 1502. [WU Hongyang, ZHOU Chao, LIANG Xin, et al. Evaluation of landslide susceptibility based on sample optimization strategy[J]. Geomatics and Information Science of Wuhan University,2024,49(8):1492 − 1502. (in Chinese with English abstract)]

    WU Hongyang, ZHOU Chao, LIANG Xin, et al. Evaluation of landslide susceptibility based on sample optimization strategy[J]. Geomatics and Information Science of Wuhan University, 2024, 49(8): 1492 − 1502. (in Chinese with English abstract)

    [21]

    GUPTA S K,SHUKLA D P. Handling data imbalance in machine learning based landslide susceptibility mapping:A case study of Mandakini River Basin,North-Western Himalayas[J]. Landslides,2023,20(5):933 − 949. DOI: 10.1007/s10346-022-01998-1

    [22] 刘宝生,陈刚,程刚建. 江苏南京地质灾害风险评价[J]. 中国地质灾害与防治学报,2023,34(4):97 − 104. [LIU Baosheng,CHEN Gang,CHENG Gangjian. Risk assessment of geological disasters in Nanjing,Jiangsu Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4):97 − 104. (in Chinese with English abstract)]

    LIU Baosheng, CHEN Gang, CHENG Gangjian. Risk assessment of geological disasters in Nanjing, Jiangsu Province[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(4): 97 − 104. (in Chinese with English abstract)

    [23] 熊小辉,汪长林,白永健,等. 基于不同耦合模型的县域滑坡易发性评价对比分析——以四川普格县为例[J]. 中国地质灾害与防治学报,2022,33(4):114 − 124. [XIONG Xiaohui,WANG Changlin,BAI Yongjian,et al. Comparison of landslide susceptibility assessment based on multiple hybrid models at county level:A case study for Puge County,Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control,2022,33(4):114 − 124. (in Chinese with English abstract)]

    XIONG Xiaohui, WANG Changlin, BAI Yongjian, et al. Comparison of landslide susceptibility assessment based on multiple hybrid models at county level: A case study for Puge County, Sichuan Province[J]. The Chinese Journal of Geological Hazard and Control, 2022, 33(4): 114 − 124. (in Chinese with English abstract)

    [24] 臧烨祺, 郭永刚, 苏立彬, 等. 西藏东南地区滑坡易发性多模型评价方法研究[J]. 中国地质灾害与防治学报,2024,35(6):58 − 69. [ZANG Yeqi, GUO Yonggang, SU Libin, et al. Assessment of landslide susceptibility in southeast Xizang Region based on multiple models[J]. The Chinese Journal of Geological Hazard and Control,2024,35(6):58 − 69. (in Chinese with English abstract)]

    ZANG Yeqi, GUO Yonggang, SU Libin, et al. Assessment of landslide susceptibility in southeast Xizang Region based on multiple models[J]. The Chinese Journal of Geological Hazard and Control, 2024, 35(6): 58 − 69. (in Chinese with English abstract)

    [25] 祁于娜,王磊. 层次分析-熵值定权法应用于山区城镇地质灾害易发性评价[J]. 测绘通报,2021(6):112 − 116. [QI Yuna,WANG Lei. Application of AHP-entropy weight method in hazards susceptibility assessment in mountain town[J]. Bulletin of Surveying and Mapping,2021(6):112 − 116. (in Chinese with English abstract)]

    QI Yuna, WANG Lei. Application of AHP-entropy weight method in hazards susceptibility assessment in mountain town[J]. Bulletin of Surveying and Mapping, 2021(6): 112 − 116. (in Chinese with English abstract)

    [26] 殷跃平. 加强城镇化进程中地质灾害防治工作的思考[J]. 中国地质灾害与防治学报,2013,24(4):5 − 8. [YIN Yueping. Thoughts on strengthening the prevention and control of geological disasters in the process of urbanization[J]. The Chinese Journal of Geological Hazard and Control,2013,24(4):5 − 8. (in Chinese with English abstract)]

    YIN Yueping. Thoughts on strengthening the prevention and control of geological disasters in the process of urbanization[J]. The Chinese Journal of Geological Hazard and Control, 2013, 24(4): 5 − 8. (in Chinese with English abstract)

    [27] 张茂省. 引水灌区黄土地质灾害成因机制与防控技术——以黄河三峡库区甘肃黑方台移民灌区为例[J]. 地质通报,2013,32(6):833 − 839. [ZHANG Maosheng. Formation mechanism as well as prevention and controlling techniques of loess geo-hazards in irrigated areas: A case study of Heifangtai immigration area in the Three Gorges Reservoir of the Yellow River[J]. Geological Bulletin of China,2013,32(6):833 − 839.]

    ZHANG Maosheng. Formation mechanism as well as prevention and controlling techniques of loess geo-hazards in irrigated areas: A case study of Heifangtai immigration area in the Three Gorges Reservoir of the Yellow River[J]. Geological Bulletin of China, 2013, 32(6): 833 − 839.

    [28]

    WANG Siyuan,MENG Xingmin,CHEN Guan,et al. Effects of vegetation on debris flow mitigation:A case study from Gansu province,China[J]. Geomorphology,2017,282:64 − 73. DOI: 10.1016/j.geomorph.2016.12.024

    [29] 赵超英,刘晓杰,张勤,等. 甘肃黑方台黄土滑坡InSAR识别、监测与失稳模式研究[J]. 武汉大学学报(信息科学版),2019,44(7):996 − 1007. [ZHAO Chaoying,LIU Xiaojie,ZHANG Qin,et al. Research on loess landslide identification,monitoring and failure mode with InSAR technique in Heifangtai,Gansu[J]. Geomatics and Information Science of Wuhan University,2019,44(7):996 − 1007. (in Chinese with English abstract)]

    ZHAO Chaoying, LIU Xiaojie, ZHANG Qin, et al. Research on loess landslide identification, monitoring and failure mode with InSAR technique in Heifangtai, Gansu[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 996 − 1007. (in Chinese with English abstract)

    [30]

    PARDESHI S D,AUTADE S E,PARDESHI S S. Landslide hazard assessment:Recent trends and techniques[J]. SpringerPlus,2013,2(1):523. DOI: 10.1186/2193-1801-2-523

    [31] 董英,孙萍萍,张茂省,等. 诱发滑坡的地下水流系统响应历史与趋势——以甘肃黑方台灌区为例[J]. 地质通报,2013,32(6):868 − 874. [DONG Ying,SUN Pingping,ZHANG Maosheng,et al. The response of regional groundwater system to irrigation at Heifangtai terrace,Gansu Province[J]. Geological Bulletin of China,2013,32(6):868 − 874. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1671-2552.2013.06.007

    DONG Ying, SUN Pingping, ZHANG Maosheng, et al. The response of regional groundwater system to irrigation at Heifangtai terrace, Gansu Province[J]. Geological Bulletin of China, 2013, 32(6): 868 − 874. (in Chinese with English abstract) DOI: 10.3969/j.issn.1671-2552.2013.06.007

    [32] 张茂省,李同录. 黄土滑坡诱发因素及其形成机理研究[J]. 工程地质学报,2011,19(4):530 − 540. [ZHANG Maosheng,LI Tonglu. Triggering factors and forming mechanism of loess landslides[J]. Journal of Engineering Geology,2011,19(4):530 − 540. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1004-9665.2011.04.014

    ZHANG Maosheng, LI Tonglu. Triggering factors and forming mechanism of loess landslides[J]. Journal of Engineering Geology, 2011, 19(4): 530 − 540. (in Chinese with English abstract) DOI: 10.3969/j.issn.1004-9665.2011.04.014

    [33] 陈玉波, 徐世光, 陈梦瑞. 以确定性系数法为基础的不同滑坡易发性评价模型对比分析——以云南保山盆地为例[J]. 中国地质灾害与防治学报,2025,36(1):119 − 130. [CHEN Yubo, XU Shiguang, CHEN Mengrui. Comparative analysis of landslide susceptibility evaluation models based on coefficient of determination method: A case study of Baoshan Basin, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2025,36(1):119 − 130. (in Chinese with English abstract)]

    CHEN Yubo, XU Shiguang, CHEN Mengrui. Comparative analysis of landslide susceptibility evaluation models based on coefficient of determination method: A case study of Baoshan Basin, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control, 2025, 36(1): 119 − 130. (in Chinese with English abstract)

    [34] 郭芳芳,杨农,张岳桥,等. 基于GIS的滑坡地质灾害地貌因素分析[J]. 地质力学学报,2008,14(1):87 − 96. [GUO Fangfang,YANG Nong,ZHANG Yueqiao,et al. Gis-based analysis of geomorphological factors for landslide hazards[J]. Journal of Geomechanics,2008,14(1):87 − 96. (in Chinese with English abstract)]

    GUO Fangfang, YANG Nong, ZHANG Yueqiao, et al. Gis-based analysis of geomorphological factors for landslide hazards[J]. Journal of Geomechanics, 2008, 14(1): 87 − 96. (in Chinese with English abstract)

    [35] 温亚楠,张志华,慕号伟,等. 动态多源数据驱动模式下的滑坡灾害空间预测[J]. 自然灾害学报,2021,30(3):83 − 92. [WEN Ya’nan,ZHANG Zhihua,MU Haowei,et al. Landslide disaster spatial prediction under dynamic multi-source data-driven model[J]. Journal of Natural Disasters,2021,30(3):83 − 92. (in Chinese with English abstract)]

    WEN Ya’nan, ZHANG Zhihua, MU Haowei, et al. Landslide disaster spatial prediction under dynamic multi-source data-driven model[J]. Journal of Natural Disasters, 2021, 30(3): 83 − 92. (in Chinese with English abstract)

    [36] 董英,贾俊,张茂省,等. 甘肃永靖黑方台地区灌溉诱发作用与黄土滑坡响应[J]. 地质通报,2013,32(6):893 − 898. [DONG Ying,JIA Jun,ZHANG Maosheng,et al. An analysis of the inducing effects of irrigation and the responses of loess landslides in Heifangtai area[J]. Geological Bulletin of China,2013,32(6):893 − 898. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1671-2552.2013.06.011

    DONG Ying, JIA Jun, ZHANG Maosheng, et al. An analysis of the inducing effects of irrigation and the responses of loess landslides in Heifangtai area[J]. Geological Bulletin of China, 2013, 32(6): 893 − 898. (in Chinese with English abstract) DOI: 10.3969/j.issn.1671-2552.2013.06.011

    [37] 许强,彭大雷,何朝阳,等. 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[J]. 工程地质学报,2020,28(1):111 − 121. [XU Qiang,PENG Dalei,HE Chaoyang,et al. Theory and method of monitoring and early warning for sudden loess landslide:A case study at Heifangtai terrace[J]. Journal of Engineering Geology,2020,28(1):111 − 121. (in Chinese with English abstract)]

    XU Qiang, PENG Dalei, HE Chaoyang, et al. Theory and method of monitoring and early warning for sudden loess landslide: A case study at Heifangtai terrace[J]. Journal of Engineering Geology, 2020, 28(1): 111 − 121. (in Chinese with English abstract)

    [38] 彭大雷. 黄土滑坡潜在隐患早期识别研究——以甘肃黑方台为例[D]. 成都:成都理工大学,2018. [PENG Dalei. Study on early identification of potential hidden dangers of loess landslide:A case study of Heifangtai in Gansu Province[D]. Chengdu:Chengdu University of Technology,2018. (in Chinese with English abstract)]

    PENG Dalei. Study on early identification of potential hidden dangers of loess landslide: A case study of Heifangtai in Gansu Province[D]. Chengdu: Chengdu University of Technology, 2018. (in Chinese with English abstract)

    [39] 王桂杰,谢谟文,邱骋等. 差分干涉合成孔径雷达技术在广域滑坡动态辨识上的实验研究[J]. 北京科技大学学报,2011,33(2):131 − 141. [WANG Guijie,XIE Mowen,QIU Cheng,et al. Experiment research of D-InSAR technique on identifying landslide moving in a wide area[J]. Journal of University of Science and Technology Beijing,2011,33(2):131 − 141. (in Chinese with English abstract)]

    WANG Guijie, XIE Mowen, QIU Cheng, et al. Experiment research of D-InSAR technique on identifying landslide moving in a wide area[J]. Journal of University of Science and Technology Beijing, 2011, 33(2): 131 − 141. (in Chinese with English abstract)

    [40]

    LIU Xiaojie,ZHAO Chaoying,ZHANG Qin,et al. Heifangtai loess landslide type and failure mode analysis with ascending and descending Spot-mode TerraSAR-X datasets[J]. Landslides,2020,17(1):205 − 215. DOI: 10.1007/s10346-019-01265-w

    [41]

    BRENNING A. Statistical geocomputing combining R and SAGA:The example of landslide susceptibility analysis with generalized additive models[J]. Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie,2008,19:23 − 32.

    [42]

    BRENNING A. Statistical geocomputing combining R and SAGA:the example of landslide susceptibility analysis with generalized additive models. In:Böhner,J. ,Blaschke,T. ,Montanarella,L. (Eds. ),SAGA – Second Out,vol. 19. Institut für Geographie der Universität,Hamburg,pp. 23-32.

    [43] 周超,甘露露,王悦,等. 综合非滑坡样本选取指数与异质集成机器学习的区域滑坡易发性建模[J]. 地球信息科学学报,2023,25(8):1570 − 1585. [ZHOU Chao,GAN Lulu,WANG Yue,et al. Regional landslide susceptibility modeling based on non-landslide sample selection index and heterogeneous integrated machine learning[J]. Journal of Geo-Information Science,2023,25(8):1570 − 1585. (in Chinese with English abstract)]

    ZHOU Chao, GAN Lulu, WANG Yue, et al. Regional landslide susceptibility modeling based on non-landslide sample selection index and heterogeneous integrated machine learning[J]. Journal of Geo-Information Science, 2023, 25(8): 1570 − 1585. (in Chinese with English abstract)

    [44]

    HANLEY J A,MCNEIL B J. The meaning and use of the area under a receiver operating characteristic (ROC) curve[J]. Radiology,1982,143(1):29 − 36. DOI: 10.1148/radiology.143.1.7063747

    [45]

    ZWEIG M H,CAMPBELL G. Receiver-operating characteristic (ROC) plots:A fundamental evaluation tool in clinical medicine[J]. Clin Chem,1993,39(4):561 − 577. DOI: 10.1093/clinchem/39.4.561

    [46] 黄发明,陈彬,毛达雄,等. 基于自筛选深度学习的滑坡易发性预测建模及其可解释性. 地球科学,2023.48(5):1696-1710. [HUANG Fanfan,CHEN Bin,MAO Daxiong,et al. Predictive modeling of landslide susceptibility based on self-screening deep learning and its interpretability. Earth Sciences,2023,48(5):1696-1710. (in Chinese with English abstract)]

    HUANG Fanfan, CHEN Bin, MAO Daxiong, et al. Predictive modeling of landslide susceptibility based on self-screening deep learning and its interpretability. Earth Sciences, 2023, 48(5): 1696-1710. (in Chinese with English abstract)

    [47] 沈佳广. 时序InSAR监测黑方台地区典型地质灾害形变及分析[D]. 北京:中国地质大学(北京),2019. [SHEN Jiaguang. Monitoring deformation and analysis of typical geological disasters in Heifangtai area by time series InSAR[D]. Beijing:China University of Geosciences,2019. (in Chinese with English abstract)]

    SHEN Jiaguang. Monitoring deformation and analysis of typical geological disasters in Heifangtai area by time series InSAR[D]. Beijing: China University of Geosciences, 2019. (in Chinese with English abstract)

    [48]

    GUZZETTI F,CARRARA A,CARDINALI M,et al. Landslide hazard evaluation:A review of current techniques and their application in a multi-scale study,Central Italy[J]. Geomorphology,1999,31(1/2/3/4):181 − 216.

    [49] 李璐. 基于机器学习法的黑方台典型滑坡体易发性评价及高精度位移预测分析[D]. 西安:长安大学,2022. [LI LU. High-precision displacement prediction analysis and susceptibility evaluation of Heifangtai typical landslide bodies based on Machine Learning[D]. Xi’an:Chang’an University. (in Chinese with English abstract)]

    LI LU. High-precision displacement prediction analysis and susceptibility evaluation of Heifangtai typical landslide bodies based on Machine Learning[D]. Xi’an: Chang’an University. (in Chinese with English abstract)

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  • 收稿日期:  2024-06-18
  • 修回日期:  2024-09-03
  • 录用日期:  2025-05-25
  • 网络出版日期:  2025-06-04

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