ISSN 1009-6248CN 61-1149/P 双月刊

主管单位:中国地质调查局

主办单位:中国地质调查局西安地质调查中心
中国地质学会

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    基于时序InSAR的黄土滑坡隐患早期识别以白鹿塬西南区为例

    张林梵

    张林梵. 基于时序InSAR的黄土滑坡隐患早期识别—以白鹿塬西南区为例[J]. 西北地质, 2023, 56(3): 250-257. DOI: 10.12401/j.nwg.2023086
    引用本文: 张林梵. 基于时序InSAR的黄土滑坡隐患早期识别—以白鹿塬西南区为例[J]. 西北地质, 2023, 56(3): 250-257. DOI: 10.12401/j.nwg.2023086
    ZHANG Linfan. Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan[J]. Northwestern Geology, 2023, 56(3): 250-257. DOI: 10.12401/j.nwg.2023086
    Citation: ZHANG Linfan. Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan[J]. Northwestern Geology, 2023, 56(3): 250-257. DOI: 10.12401/j.nwg.2023086

    基于时序InSAR的黄土滑坡隐患早期识别—以白鹿塬西南区为例

    基金项目: 西安市地质灾害综合防治体系建设“白鹿塬西南坡风险调查评价”(ZLC-SX-2021014)资助。
    详细信息
      作者简介:

      张林梵(1996−),男,硕士,助理工程师,主要从事工程勘察与灾害防治工作。E−mail:274768033@qq.com

    • 中图分类号: P694

    Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan

    • 摘要:

      中国黄土滑坡灾害频发且分布广泛,传统的地质灾害调查对于地处高位、形变特征不明显和隐蔽型的滑坡隐患难以有效识别,是滑坡灾害监测预警成功率低的主要原因之一。如何有效超前判识别地质灾害隐患是地质灾害防治工作的前提和基础,时序InSAR技术在此领域具有良好的应用潜力,但如何更好地将InSAR技术融入到滑坡灾害相关研究中仍处于探索阶段。笔者以西安市白鹿塬西南区为研究区,在高精度三维倾斜摄影、ALOS-2雷达影像集等数据基础上,以时序InSAR技术反演得到104处地表形变明显区域;结合黄土滑坡易发指数、航拍影像和野外核查,快速识别黄土滑坡及隐患23处,其中包括新识别的滑坡隐患20处和在册的滑坡灾害3处,通过与传统地灾调查数据比对和实地调查核实验证了时序InSAR方法探测结果的优势和有效性,并构建了基于高精度InSAR和DEM数据的黄土滑坡隐患早期识别方法。

      Abstract:

      Loess landslide disasters occur frequently and are widely distributed in China. Traditional geological hazard surveys are difficult to effectively identify hidden landslide hazards that are located at high altitudes, have unclear deformation characteristics, and are hidden. This is also one of the main reasons for the low success rate of landslide hazard monitoring and warning. How to effectively identify geological hazard hazards beyond prejudgment is the premise and foundation of geological hazard prevention and control work. Time series InSAR technology has good application potential in this field, but how to better integrate InSAR technology into landslide disaster related research is still in the exploratory stage. The author takes the southwest area of Bailuyuan in Xi’an City as the research area, and on the basis of high−precision 3D oblique photography, ALOS-2 radar image set, and other data, uses time-series InSAR technology to invert 104 areas with obvious surface deformation. By combining the susceptibility index of loess landslides, aerial images, and field verification, 23 loess landslides and hidden dangers were quickly identified, including 20 newly identified landslide hazards and 3 registered landslide disasters. The advantages and effectiveness of the time−series InSAR method detection results were verified through comparison with traditional geological disaster investigation data and on−site investigation verification. A high−precision InSAR and DEM data based early identification method for loess landslide hazards was constructed.

    • 图  1   白鹿塬西南区地理位置图

      Figure  1.   Geographical location map of the southwest district of Bailuyuan

      图  2   白鹿塬地质剖面示意图(李宝田等,2021)

      Figure  2.   Geological profile of Bailuyuan

      图  3   影像采集时间分布图

      Figure  3.   Time distribution of image acquisition

      图  4   研究区最陡坡向地表形变速率图

      Figure  4.   Topographic deformation rate of the steepest slope in the study area

      图  5   研究区地表形变核密度热点图

      Figure  5.   Hot spot map of surface deformation nucleus density in the study area

      图  6   斜坡特征统计及历史滑坡在其特征分布比率图

      a. 斜坡在坡度区间分布的比率;b. 滑坡在坡度区间分布的比率;c. 斜坡在坡高区间分布的比率;d. 滑坡在坡高区间分布的比率

      Figure  6.   Statistics of slope characteristics and distribution ratio of historical landslides in their characteristics

      图  7   易发性指数等级图

      Figure  7.   Vulnerability index grade chart

      图  8   黄土滑坡隐患早期识别结果与历史滑坡点空间分布图

      Figure  8.   Early identification results of hidden dangers of loess landslide and spatial distribution of historical landslide points

      表  1   ALOS-2数据参数表

      Table  1   ALOS-2 data parameters

      影像采集时间影像数量雷达波长轨道方向空间分辨率视角垂直基线分布范围极化方式
      2020-01-18-2021-09-1119景23 cm升轨10 m32°91 mHH+HV
      下载: 导出CSV

      表  2   新识别滑坡隐患和历史滑坡灾害活动性分类表

      Table  2   Classification of newly identified landslide hazards and activities of historical landslide hazards

      类型划分标准活动性统计
      历史滑坡灾害(点位信息) 年变形量<10 mm/yr 稳定 HP1~HP7、HP8~HP24、HP26~HP28(共26个)
      年变形量>10 mm/yr 复活/活动 HP8、HP25、HP29(共3个)
      滑坡隐患(识别范围) 年变形量>10 mm/yr 活动 X1~X2、X4~X8、X10~X13、X15~X23(共20处)
      复活/活动 X3、X9、X14(共3处)
      下载: 导出CSV
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    出版历程
    • 收稿日期:  2022-11-30
    • 修回日期:  2023-04-11
    • 网络出版日期:  2023-05-11
    • 刊出日期:  2023-06-19

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