多时相数字孪生滑坡变形监测方法与应用研究

杨云建, 周学铖, 何中海, 李宗亮, 蔡嘉伦, 裴鹏, 刘锦涛, 匡伟. 多时相数字孪生滑坡变形监测方法与应用研究——以金沙江白格滑坡为例[J]. 水文地质工程地质, 2024, 51(2): 132-143. doi: 10.16030/j.cnki.issn.1000-3665.202210053
引用本文: 杨云建, 周学铖, 何中海, 李宗亮, 蔡嘉伦, 裴鹏, 刘锦涛, 匡伟. 多时相数字孪生滑坡变形监测方法与应用研究——以金沙江白格滑坡为例[J]. 水文地质工程地质, 2024, 51(2): 132-143. doi: 10.16030/j.cnki.issn.1000-3665.202210053
YANG Yunjian, ZHOU Xuecheng, HE Zhonghai, LI Zongliang, CAI Jialun, PEI Peng, LIU Jintao, KUANG Wei. Multi-temporal digital twin method and application of landslide deformation monitoring: A case study on Baige landslide in Jinsha River[J]. Hydrogeology & Engineering Geology, 2024, 51(2): 132-143. doi: 10.16030/j.cnki.issn.1000-3665.202210053
Citation: YANG Yunjian, ZHOU Xuecheng, HE Zhonghai, LI Zongliang, CAI Jialun, PEI Peng, LIU Jintao, KUANG Wei. Multi-temporal digital twin method and application of landslide deformation monitoring: A case study on Baige landslide in Jinsha River[J]. Hydrogeology & Engineering Geology, 2024, 51(2): 132-143. doi: 10.16030/j.cnki.issn.1000-3665.202210053

多时相数字孪生滑坡变形监测方法与应用研究

  • 基金项目: 国家自然科学基金项目(42171355);四川省测绘地理信息学会科技开放基金(CCX202205);西南科技大学博士基金项目(22ZX7171)
详细信息
    作者简介: 杨云建(1990—),男,工程师,主要从事无人机航空摄影测量与遥感、地灾应急监测与响应工作。E-mail:787135238@qq.com
    通讯作者: 周学铖(1989—),男,高级工程师,主要从事遥感地质灾害监测与风险评估防控研究。E-mail:zhouxuecheng_rs@qq.com
  • 中图分类号: P237;P642.2

Multi-temporal digital twin method and application of landslide deformation monitoring: A case study on Baige landslide in Jinsha River

More Information
  • 高位隐蔽滑坡因为难到达、难识别、难监测,致使成灾表现具有极强的突发性和破坏性。针对传统人工地面调查和地面布设监测设备存在危险系数高、工作效率低、设备易损坏和离线误报率高等问题,提出基于无人机倾斜摄影测量技术构建高位隐蔽滑坡数字孪生体的方法,通过信息化、数字化手段对地质灾害变形特征及时空演化规律进行监测分析。以西藏金沙江白格滑坡为研究对象,利用无人机倾斜摄影测量技术获取2019年4月—2021年9月共计10期次航测数据,融合多源数据构建了多时相数字孪生滑坡体,通过多期孪生滑坡体实现对白格滑坡整体滑移、局部微变形、滑塌体积等多维要素的高精度定量分析,并及时应用于白格滑坡时空演化分析和监测预警中。研究表明:白格滑坡在2019—2021年监测期内存在持续变形迹象,强变形主要位于滑坡两侧及后缘,渐有扩大趋势,存在垮塌堵江风险。运用多时相数字孪生滑坡变形监测手段实现对地质灾害定性-定量化特征描述与风险评估,具有快速灵活、覆盖全面、不受复杂艰险地形条件限制等优势,可为高位隐蔽滑坡等斜坡灾害大梯度形变监测提供工程实践参考。

  • 加载中
  • 图 1  白格滑坡泄洪全貌图

    Figure 1. 

    图 2  多时相数字孪生滑坡变形监测方法

    Figure 2. 

    图 3  白格滑坡监测分区(2021年9月28日正射影像)

    Figure 3. 

    图 4  滑坡体长时序变形图(2019年4月26日—2021年9月28日)

    Figure 4. 

    图 5  裂缝发育分布图

    Figure 5. 

    图 6  裂缝变化图

    Figure 6. 

    图 7  裂缝变形及趋势图

    Figure 7. 

    图 8  变形区量化分析图

    Figure 8. 

    图 9  滑坡体地表三维变形图

    Figure 9. 

    图 10  滑坡体已滑变形分析图

    Figure 10. 

    表 1  无人机倾斜摄影测量日期及精度简表

    Table 1.  Date and accuracy of UAV oblique photogrammetry

    期次 拍摄日期 航片数量
    /张
    航测面积
    /km2
    检查点中误差
    平面/m 高程/m
    一期 2019-04-26 8980 6.5 0.1850 0.1766
    二期 2019-06-29 2850 14.6 0.1545 0.3010
    三期 2019-08-03 7310 5.5 0.1849 0.1232
    四期 2019-09-25 9570 5.0 0.2101 0.2629
    五期 2020-01-16 1956 9.1 0.3377 0.2789
    六期 2020-04-25 16335 7.9 0.2136 0.0794
    七期 2020-07-26 3124 12.1 0.1860 0.1400
    八期 2020-10-12 14225 7.9 0.2115 0.0964
    九期 2021-04-26 8939 12.7 0.1396 0.0842
    十期 2021-09-28 16055 7.9 0.1139 0.0896
    下载: 导出CSV

    表 2  欠稳定区体积估算表

    Table 2.  Volume estimation of unstable zone

    欠稳定区亚区面积/km2亚区体积/
    (104 m3
    体积小计/
    (104 m3
    K2K2-1
    右滑壁
    0.13169413
    K2-20.0390
    K2-3a
    K2-3b
    0.14154
    K3K3-1
    左滑壁
    0.09117306
    K3-20.05125
    K3-3
    K1-3
    后滑壁
    0.0464
    合计0.48719719
    下载: 导出CSV
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出版历程
收稿日期:  2022-10-21
修回日期:  2023-04-25
录用日期:  2023-05-17
刊出日期:  2024-03-15

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