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滑坡多源监测技术及预警模型研究进展与展望

朱淳 龚逸非 宋盛渊 李海波 何满潮

朱淳, 龚逸非, 宋盛渊, 李海波, 何满潮. 滑坡多源监测技术及预警模型研究进展与展望[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230547
引用本文: 朱淳, 龚逸非, 宋盛渊, 李海波, 何满潮. 滑坡多源监测技术及预警模型研究进展与展望[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230547
ZHU Chun, GONG Yifei, SONG Shengyuan, LI Haibo, HE Manchao. Progress and Prospects of Landslide Multi-Source Monitoring Technology and Early Warning Model[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230547
Citation: ZHU Chun, GONG Yifei, SONG Shengyuan, LI Haibo, HE Manchao. Progress and Prospects of Landslide Multi-Source Monitoring Technology and Early Warning Model[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230547

滑坡多源监测技术及预警模型研究进展与展望

doi: 10.3969/j.issn.0258-2724.20230547
基金项目: 国家重点研发计划(2022YFC3080100);国家自然科学基金项目(52104125、42177139)
详细信息
    作者简介:

    朱淳(1993—),男,教授,研究方向为大型岩质边坡变形破坏机制等,E-mail:zhu.chun@hhu.edu.cn

    通讯作者:

    龚逸非(1995—),男,助理研究员,研究方向为大型滑坡监测预警等,E-mail:gongyifei@hhu.edu.cn

  • 中图分类号: X935

Progress and Prospects of Landslide Multi-Source Monitoring Technology and Early Warning Model

  • 摘要:

    我国地质灾害频发,滑坡灾害因其种类多、察觉难、分布广、危害大等特点,造成的人员伤亡和财产损失位于各类地质灾害之首. 多源监测技术在滑坡预警、防灾减灾过程中起着至关重要的作用. 简要回顾多种滑坡监测技术的产生及发展历程;系统梳理近年来从滑坡的表观长期安全评估、深部牛顿力监测到微震信号感知的多源数据融合监测方法应用等一系列重要进展;概述了卫星监测智能识别技术、空天地一体化复合光纤滑坡监测技术以及NPR (negative poisson’s ratio anchor)深部牛顿力实时监测技术在滑坡识别解译、长期监测、应急响应等方面的应用研究;总结学者们在滑坡预警模型的最新成果和主要研究方向,对其评估方法及主要结论进行分类评述;分析讨论以现有滑坡监测数据为驱动,融合各类深度学习方法来预测滑坡的优势和主要存在的问题. 前沿的深度学习算法与滑坡灾变多参量高精度演化特征信息的深度融合,将引领智能化滑坡预警模型的研究迈向新的高度,成为未来探索的核心焦点.

     

  • 图 1  降雨型边坡雨量装置及监测点布置[24]

    Figure 1.  Rainfall-type slope rainfall device and monitoring point layout[24]

    图 2  基于微震监测的滑坡风险预警

    Figure 2.  Landslide risk warning based on micro-seismic monitoring

    图 3  边坡声发射监测及布置示意[57]

    Figure 3.  Slope acoustic emission monitoring and layout[57]

    图 4  NPR大变形缆索和边坡岩体相互作用及监测过程[59]

    Figure 4.  Interaction and monitoring process of NPR large deformation cable and slope rock mass[59]

    图 5  监测加固一体化系统及监测中心效果示意[59]

    Figure 5.  Monitoring and reinforcement integrated system and monitoring center effect[59]

    图 6  空-天-地-体多源数据滑坡监测预警平台

    Figure 6.  Space-air-ground-body multi-source data landslide monitoring and early warning platform

    图 7  滑坡多源数据监测常用技术示意[60]

    Figure 7.  Common technologies of landslide multi-source data monitoring[60]

    图 8  陕北黄土滑坡[71]

    Figure 8.  Loess landslide in northern Shaanxi[71]

    图 9  我国的边坡四级预警机制与预警等级划分示意

    Figure 9.  China’s slope four-level early warning mechanism and early warning level division

    图 10  可解释性机器学习在滑坡地质灾害中的研究框架[91]

    Figure 10.  Research framework of interpretable machine learning in landslide disasters[91]

    表  1  昆明铁路局威红线附近区段边坡降雨警戒值[26]

    Table  1.   Rainfall warning value of slopes near Weihong Line of Kunming Railway Bureau [16]

    警戒值 K 采取措施
    3 d 连续累计
    降雨量/mm
    降雨强度/
    (mm•h−1)
    38 11 1.15≤K<1.20 加强巡守
    56 16 1.10≤K<1.15 固定看守
    89 25 1.05≤K<1.10 固定看守(限速)
    113 32 K<1.05 固定看守(限速)
    下载: 导出CSV

    表  2  地表变形监测技术及其特点

    Table  2.   Surface deformation-based monitoring technology and characteristics

    监测技术 测量精度 适用条件 优缺点
    天基 GNSS[29] mm~cm 级 适用于较大区域滑坡的长期观测  受飞行高度、地形、植被和大气延迟误差等影响,数据后处理较复杂
    InSAR[28] mm~cm 级  大范围、长周期观测包括坡表沉降、裂缝等
    空基 航空摄影[38] mm~m 级 小区域的三维快速测量 受到复杂地形条件影响
    机载LiDAR[39] mm~m 级 小区域精确测量 受植被影响小,测量精准
    地基 GB-InSAR[52] 亚 mm 级 全天候、实时监测滑坡区域的变形 受环境对监测结果的影响较大
    自动全站仪[53] 0.5~5 mm  适于较小范围内处于加速变形阶段前的滑坡 受通视条件、大气条件和植被影响
    裂缝计[54]  适于较小范围内处于加速变形阶段的滑坡 埋深过程较为繁琐
    分布式光纤[22] 0.01 mm 级  适于长距离、大范围滑坡体宏观实时监测 连续监测,灵敏度高,成本较高
    三维激光
    扫描[45]
    mm 级  适于滑坡不同变形阶段 地表三维空间位移与沉降等地貌变形监测 成本高,数据处理复杂
    地震仪微震
    监测[54]
    cm~m 级 区域性大范围岩质边坡局部破裂  信号分析较为繁琐,地震事件和滑坡变形的关系有待明确
    下载: 导出CSV

    表  3  滑坡深部变形监测技术及其特点

    Table  3.   Deep deformation-based landslide monitoring technology and characteristics

    监测技术 测量精度 适用条件 优缺点
    TDR[55] mm~cm 级,精度随线缆长度衰减  适于滑面倾角较小的滑坡滑面位置与深部位移监测,以及滑坡治理效果评价  监测成本低、滑面定位准确,无法确定滑动方向和倾斜状态,对均匀变形的敏感度较差
    多点位移计[56]  适于滑面倾角较小的滑坡 量程较小,难以精准确定滑动方向
    钻孔测斜 高,0.1 mm 级  适于滑坡滑体初始变形和等速变形阶段下,滑坡不同深度的变形监测  成本高,当滑坡变形较大时,易造成测管毁坏
    声发射[57] 高,0.01 mm 级  监测滑坡的极缓慢变形. 适用于岩质滑坡监测 连续监测、成本低、灵敏度高
    下载: 导出CSV

    表  4  常用滑坡预测解译机器学习模型

    Table  4.   Common machine learning models for landslide prediction and interpretation

    常用滑坡解译模型 优点 缺点
    逻辑回归 学习成本较低、易于理解和实现  发生欠拟合现象、分类精度可能不高
    决策树 适合评价离散小规模样本  评价大量连续变量和多类别样本效果欠佳
    人工神经网络 准确度高、学习能力强  需要大量参数,学习时间过长,评价结果不稳定
    支持向量机 结果易解释  崩塌滑坡易发性,存在运行时间较长、存在过拟合的问题
    集成算法(bagging、随机森林、boosting、stacking) 对样本数量要求较低  在某些噪音值较大的样本来进行危险性评价时可能会发生过拟合现象
    深度学习 学习能力强、覆盖范围广  准确性受样本数量的影响较大
    下载: 导出CSV
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  • 收稿日期:  2023-10-16
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