Application of combined space, arial and ground based multiple technologies in deformation monitoring of mining areas
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摘要: 河北滦平县张百湾镇周台子村由于多年矿山开采遗留下大量的采空区，部分采空区未做任何处理存在塌陷隐患，严重制约当地的经济发展和社会稳定。对该地区采空区形变调查和实地监测十分有必要。文中综合应用合成孔径雷达干涉测量技术、无人机摄影测量技术、三维激光扫描技术对采空区的空间分布进行划分确定和形变监测。首先应用小基线集技术对采空区进行地表形变解算。然后应用无人机数据构建研究区的三维模型，并通过多期无人机航飞数据，计算2次航飞间地表变化，佐证InSAR技术的结果。最后应用三维激光扫描技术，对部分重点区域进行三维激光扫描，建立采空区精细化模型。研究结果表明，三种技术的联合监测结果表现出高度的一致性，其中InSAR技术探测出研究区最大形变速率−25 mm/a，结合2期无人机正射模型DEM与三维激光扫描数据差分结果确定出采空区17处的高风险区域，部分区域对居民区和道路有影响。基于“空天地”一体化技术具有较高的可靠性，可应用于矿区采空区形变调查和地面沉降监测。
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关键词:
- 采空区 /
- InSAR技术 /
- 无人机摄影测量 /
- SBAS-InSAR /
- 三维激光扫描
Abstract: There are a large number of iron mine gobs due to many years of mining in Zhoutaizi village, Zhangbaiwan Town, Luanping County. And some gobs have potential safety hazards of collapse, which seriously restrict the local economic development and social stability. So it is necessary to strengthen deformation investigation and field monitoring of existing gobs in this area. In this paper, the spatial distribution of gobs in the study area is determined and deformation of gobs is monitored by using Interferometric Synthetic Aperture Radar (InSAR), UAV photogrammetry and 3D laser scanning technology. Firstly, Small Baseline Subset InSAR (SBAS-InSAR) technology is used to retrieve the surface deformation of gobs. Then, a three-dimensional model of the study area is constructed by using UAV data, and the ground surface changes between two flights are calculated by using multi-period UAV flight data to support the results of InSAR technology. Finally, 3D laser scanning technology is applied to some areas, and the fine model of gobs is established. The results show that the joint monitoring results of the three technologies show a high consistency, the maximum deformation rate (−25 mm/a) of the study area are detected by InSAR method. Combined with the difference results of the DEM and 3D laser scanning data of the two periods of UAV, 17 high-risk areas of gobs are identified. The high-risk areas are distributed in various mining areas, and some areas have an impact on residential areas and roads. The based on Space, Sky and Ground multi-technology indicating that this method has high reliability and can be well applied to deformation investigation of goaf in mining area and the surface deformation monitoring during inadequate mining.-
Key words:
- gobs /
- InSAR technology /
- UAV photogrammetry /
- SBAS-InSAR /
- 3D laser scanning
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图 2 研究区全区正射影像图及细节展示
Figure 2. Orthophoto map and detail display of the whole study area
图 9 无人机DTM差分地表形变图及实景模型
Figure 9. UAV DTM differential surface deformation diagram and real scene model
图 12 InSAR、无人机多角度对比综合分析图
Figure 12. Multi angle comparison and comprehensive analysis of InSAR and UAV
图 13 区块1多结果叠合高风险采空区区域图
Figure 13. Regional map of high-risk goaf with multi result superposition in block 1
图 14 区块2多结果叠合高风险采空区区域图
Figure 14. Regional map of high-risk goaf with multi result superposition in block 2
图 15 区块3多结果叠合高风险采空区区域图
Figure 15. Regional map of high-risk goaf with multi result superposition in block 3
图 16 区块8多结果叠合高风险采空区区域图
Figure 16. Regional map of high-risk goaf with multi result superposition in block 8
表 1 研究区Sentinel-1数据
Table 1. Sentinel-1 data in the research area
Sentinel-1A升轨影像数据集 编号 成像日期 垂直基线/m 时间基线/d 多普勒频差/Hz 高程模糊度/m 0 2018-11-06 0.00 0 0.00 0.00 1 2018-12-12 57.96 36 −4.61 262.75 2 2019-01-05 −8.46 60 7.27 1799.97 3 2019-02-10 95.92 96 3.33 158.75 4 2019-03-06 113.21 120 5.57 134.52 5 2019-04-11 125.46 156 −3.34 121.38 6 2019-11-01 128.02 360 −5.10 118.96 7 2019-12-07 74.24 396 −3.10 205.13 8 2020-01-12 61.75 432 0.86 246.60 9 2020-02-05 62.86 456 0.54 242.27 10 2020-03-12 141.33 492 2.36 107.75 11 2020-04-05 97.92 516 3.22 155.52 Sentinel-1A降轨影像数据集 编号 成像时间 垂直基线/m 时间基线/d 多普勒频差/Hz 高程模糊度/m 0 2018-11-05 0.00 0 0.00 0.00 1 2018-12-11 32.79 36 1.49 518.88 2 2019-01-04 −30.31 60 3.69 561.38 3 2019-02-09 17.13 96 1.14 993.06 4 2019-03-05 −39.30 120 −7.84 432.96 5 2019-04-10 61.11 156 −14.1 278.41 6 2019-11-12 −31.83 372 −2.95 534.49 7 2019-12-06 47.26 396 4.70 360.03 8 2020-01-11 −58.43 432 −3.82 291.19 9 2020-02-04 −9.43 456 1.16 1805.11 10 2020-03-11 −5.50 492 −7.30 3095.02 
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