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UAV SfM技术在活动构造研究中的应用——以青藏高原西北部龙木错断裂为例

江晨轶 潘家伟 张丽军 李海兵 孙知明 Marie-Luce Chevalier 刘富财 苏强

江晨轶, 潘家伟, 张丽军, 等, 2024. UAV SfM技术在活动构造研究中的应用——以青藏高原西北部龙木错断裂为例. 地质力学学报, 30 (2): 332-347. DOI: 10.12090/j.issn.1006-6616.2023192
引用本文: 江晨轶, 潘家伟, 张丽军, 等, 2024. UAV SfM技术在活动构造研究中的应用——以青藏高原西北部龙木错断裂为例. 地质力学学报, 30 (2): 332-347. DOI: 10.12090/j.issn.1006-6616.2023192
JIANG Chenyi, PAN Jiawei, ZHANG Lijun, et al., 2024. Application of UAV SfM technology in active tectonic research: A case study of the Longmu Co Fault, Northwestern Qinghai-Tibet Plateau. Journal of Geomechanics, 30 (2): 332-347. DOI: 10.12090/j.issn.1006-6616.2023192
Citation: JIANG Chenyi, PAN Jiawei, ZHANG Lijun, et al., 2024. Application of UAV SfM technology in active tectonic research: A case study of the Longmu Co Fault, Northwestern Qinghai-Tibet Plateau. Journal of Geomechanics, 30 (2): 332-347. DOI: 10.12090/j.issn.1006-6616.2023192

UAV SfM技术在活动构造研究中的应用——以青藏高原西北部龙木错断裂为例

doi: 10.12090/j.issn.1006-6616.2023192
基金项目: 

中国地质调查局地质调查项目 DD20221630

国家自然科学基金项目 42372274

科技部科技基础资源调查专项 2021FY100101

详细信息
    作者简介:

    江晨轶(1999—),男,在读硕士,构造地质学专业,从事活动构造与无人机摄影测量研究。Email: jcycoix@163.com

    通讯作者:

    潘家伟(1983—),男,博士,副研究员,从事青藏高原活动构造、地震地质研究。Email: jiawei-pan@foxmail.com

  • 中图分类号: P231;P65

Application of UAV SfM technology in active tectonic research: A case study of the Longmu Co Fault, Northwestern Qinghai-Tibet Plateau

Funds: 

the Geological Survey Project of the China Geological Survey DD20221630

the National Natural Science Foundation of China 42372274

the National Science and Technology Basic Resources Investigation Program of China 2021FY100101

  • 摘要: 为探讨搭载与未搭载实时动态差分技术/动态后处理技术(RTK/PPK)模块的无人机平台通过运动恢复结构(SfM)方法处理获得的数字高程模型(DEM)数据质量差异,以及建立不同无人机平台野外数据采集和室内数据处理过程的快速流程,利用大疆经纬M 300 RTK无人机(搭载禅思L1激光雷达(LiDAR)和测绘相机)与大疆精灵4 Pro无人机(搭载可见光相机)分别对青藏高原西北部龙木错断裂上1处位错阶地进行了数据采集,获得了该处高分辨率、高精度DEM数据和数字正射影像图(DOM)数据。对比结果显示,M 300 RTK无人机平台L1负载系统获得的LiDAR和SfM地形数据精度接近,两者在约100 m的飞行高度获得的DEM数据在水平和垂直方向的均方差分别为0.135 m、0.111 m和0.201 m、0.180 m;无RTK模块的精灵4 Pro无人机获取的DEM数据虽然绝对精度较差(水平和垂直方向均方差分别为1.707 m和249.280 m),但其反映的相对地形与实际地形接近,经过地面控制点校正后精度可以达到分米级。研究表明,RTK SfM技术克服了使用地面控制点的局限性,为活动构造研究领域微地貌测量提供了更高精度、更高效率的解决方案。当对测区的绝对三维坐标要求不高,仅需相对的地形起伏时,未搭载RTK模块的无人机也能够在无地面控制点约束的情况下满足地貌位错测量基本需求。

     

  • 图  1  野外工作点及邻区活动断裂分布图(据Li et al., 2021修改)

    Figure  1.  Fieldwork locations and distribution of active faults in neighboring areas (modified from Li et al., 2021)

    图  2  野外工作点河流阶地左行位错地貌卫星影像解译图(据Chevalier et al., 2017修改)

    红色线指示活动断层迹线;黄色方框为无人机航测范围;黄色圆圈及对应的数字表示地面控制点位置及其编号

    Figure  2.  Interpretation of satellite imagery of left-lateral faulted landforms in river terraces at fieldwork locations (modified from Chevalier et al., 2017)

    The red lines indicate the traces of active faults, the yellow boxes represent the coverage area of the UAV aerial survey, and the yellow circles with corresponding numbers indicate the positions of ground control points and their identifiers.

    图  3  应用UAV SfM方法开展活动构造研究数据采集、数据处理、位错测量流程图

    Figure  3.  Flowchart of data collection, data processing, and fault displacement measurement process for active tectonics research using UAV SfM method

    图  4  无人机数据采集系统及地面控制点的布置与测量

    a—大疆经纬M 300 RTK无人机及禅思L1负载;b—大疆精灵4 Pro无人机;c—地面控制点标靶;d—地面控制点坐标RTK测量

    Figure  4.  UAV data acquisition system and deployment and measurement of ground control points

    (a) DJI Matrice M300 RTK UAV equipped with Zenmuse L1 payload; (b) DJI Phantom 4 Pro UAV; (c) Ground control point target; (d) Ground control point coordinates measured by RTK

    图  5  测区UAV SfM及LiDAR成果图

    a—M 300 RTK无人机航测照片重叠度示意图;b—M 300 RTK SfM方法处理获得的测区DOM图像;c—M 300 RTK SfM方法处理获得的测区DEM图像;d—禅思L1 LiDAR获得的测区DEM图像;e—未使用地面控制点校正的精灵4 Pro SfM方法获得的测区DEM图像;f—经地面控制点校正过后的精灵4 Pro SfM方法获得的测区DEM图像

    Figure  5.  Result Maps of UAV SfM and LiDAR

    (a) Overlap of aerial survey photos taken by the M300 RTK UAV; (b) Digital Orthophoto Map (DOM) of the surveyed area obtained by M300 RTK SfM method; (c) Digital Elevation Model (DEM) of the surveyed area obtained by M300 RTK SfM method; (d) DEM of the surveyed area obtained by Zenith L1 LiDAR; (e) DEM of the surveyed area obtained by DJI Phantom 4 Pro using SfM method without ground control point correction; (f) DEM of the surveyed area obtained by DJI Phantom 4 Pro using SfM method after ground control point correction

    图  6  M 300 RTK UAV SfM方法获得的工作点DOM、DEM综合解译的河流阶地位错地貌图

    红色线指示活动断层迹线;黄色线段AB指示图 9中地形剖面位置;灰色细线及对应的数字为等高线及其对应海拔高度;黑色虚线指示T3阶地被断裂错开的标志线,黑色双向箭头及中间数字指示T3阶地位错量

    Figure  6.  River terrace faulted landform interpreted from the integrated analysis of DOM and DEM obtained by M300 RTK UAV SfM method

    The red lines indicate the traces of active faults, the yellow line segment AB indicates the position of the topographic profile in Fig. 9, the gray lines and corresponding numbers represent contour lines and their corresponding elevations, the black dashed lines indicate the marker lines where the T3 terrace is displaced by the fault, and the black bidirectional arrow with the middle numbers indicate the amount of displacement of the T3 terrace.

    图  7  M 300 RTK无人机平台SfM数据、LiDAR数据、未经地面控制点校正的精灵4 Pro SfM数据及地面控制点校正后的精灵4 Pro SfM数据与地面检查点差分GPS实测坐标在3个方向上的对比

    a—X方向;b—Y方向;c、d—Z方向

    Figure  7.  Comparison of M300 RTK UAV SfM data, LiDAR data, Phantom 4 Pro SfM data before GCP correction, and Phantom 4 Pro SfM data after GCP correction, with checkpoint coordinates measured by differential GPS, in the X, Y, and Z axes

    (a) X direction; (b) Y direction; (c, d) Z direction

    图  8  LaDiCaoz软件位错提取过程及结果

    a—确定断层迹线、断错地貌标志(阶地陡坎)以及穿过标志物剖面的位置;b—位错恢复后的研究点地貌图;c、d—断裂两侧剖面线地貌标志体拟合前后的相对位置

    Figure  8.  Process and results of fault displacement extraction using LaDiCaoz software

    (a) Locating fault traces, terrace risers, and profile locations; (b) Geomorphological map of the study points after displacement restoration (color legends in Fig 8a and 8b represent changes in terrain elevation); (c, d) Relative positions before and after fitting of geomorphic markers on both sides of the fault

    图  9  M 300 RTK无人机平台SfM、LiDAR、精灵4 Pro SfM DEM数据地形剖面对比(剖面位置见图 6)

    T0—河漫滩;T1—一级阶地;T2—二级阶地;T2′—次二级阶地;T2″—次次二级阶地;T3—三级阶地

    Figure  9.  Comparison of topographic profiles of DEM data derived from M 300 RTK UAV SfM, LiDAR, and Phantom 4 Pro SfM (Profile location is shown in Fig. 6)

    T0-floodplain; T1-Terrace 1; T2-Terrace 2; T2′-Sub-terrace on Terrace 2; T2″-High sub-terrace on Terrace 2; T3-Terrace 3

    表  1  不同类型数据与地面检查点RTK GNSS测量结果对比(单位:m)

    Table  1.   Comparison of different types of data with ground checkpoints measured with RTK GNSS (unit: m; * denotes points used as GCP during Phatom 4Pro data SfM processing)

    地面检查点 经纬M300 SfM 禅思L1 LiDAR 地面控制点校正前精灵4 Pro SfM数据 地面控制点校正后精灵4 Pro SfM数据
    DX DY DZ DX DY DZ DX DY DZ DX DY DZ
    GCP1 -0.078 -0.078 -0.148 0.043 0.043 0.043 -1.082 -0.239 249.443 -0.057 -0.006 1.075
    GCP2* -0.035 -0.058 -0.082 0.023 0.023 0.023 -0.940 -0.518 249.408 -0.046 -0.025 0.017
    GCP3* 0.258 0.028 -0.141 0.106 0.106 0.106 -0.253 -0.990 248.971 -0.004 -0.076 0.018
    GCP4* 0.350 0.058 -0.334 0.166 0.166 0.166 -0.336 -1.944 248.603 -0.002 -0.059 -0.314
    GCP5* 0.197 0.169 -0.226 0.008 0.008 0.008 -0.766 -2.731 248.457 0.058 -0.023 0.256
    GCP6* -0.026 0.071 -0.149 0.125 0.125 0.125 -1.536 -2.415 249.319 0.057 0.021 -0.185
    GCP7* -0.185 -0.040 -0.233 0.016 0.016 0.016 -1.849 -1.721 249.819 0.017 0.072 0.262
    GCP8 -0.109 -0.091 -0.125 -0.087 -0.087 -0.087 -1.419 -1.228 249.693 0.104 0.026 -1.060
    GCP9 0.189 0.028 -0.100 0.135 0.135 0.135 -0.730 -1.612 249.086 0.049 -0.007 -0.592
    GCP10 -0.015 -0.069 -0.070 0.064 0.064 0.064 -0.833 -0.229 249.390 0.017 0.103 0.422
    GCP11* -0.081 -0.191 -0.184 -0.054 -0.054 -0.054 -1.113 0.533 249.263 -0.037 0.113 -0.317
    GCP12* 0.103 -0.125 -0.104 0.124 0.124 0.124 -0.524 0.280 249.127 -0.032 -0.035 0.517
    GCP13* 0.224 -0.027 -0.187 0.183 0.183 0.183 -0.081 -0.250 248.951 -0.002 -0.101 -0.058
    GCP14* -0.236 -0.119 -0.251 -0.018 -0.018 -0.018 -1.704 -0.796 249.809 0.032 0.009 0.066
    GCP15 -0.167 -0.107 -0.199 0.031 0.031 0.031 -1.313 -0.179 249.523 0.040 0.023 -0.619
    GCP16 -0.157 -0.121 -0.122 -0.122 -0.122 -0.122 -1.340 -0.486 249.608 0.050 -0.029 -0.785
    注:*为精灵4 Pro数据SfM处理过程中的地面控制点
    下载: 导出CSV

    表  2  不同类型数据在XYZ轴方向的平均误差与均方根误差(单位:m)

    Table  2.   Mean error and root mean square error (RMSE) in X, Y, and Z axes for different types of data (unit: m)

    X轴平均误差 Y轴平均误差 Z轴平均误差 RMSEX RMSEY RMSEZ RMSEH RMSE3D
    经纬M300 SfM 0.151 0.086 0.166 0.176 0.099 0.180 0.201 0.270
    禅思L1 LiDAR 0.096 0.051 0.103 0.112 0.076 0.111 0.135 0.174
    校正前精灵4 Pro SfM 0.989 1.009 249.279 1.112 1.295 249.280 1.707 249.286
    校正后精灵4 Pro SfM 0.038 0.046 0.410 0.046 0.058 0.527 0.073 0.532
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-12-01
  • 修回日期:  2024-03-18
  • 录用日期:  2024-03-25
  • 预出版日期:  2024-04-09
  • 刊出日期:  2024-04-28

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