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ZHANG Junyang,WANG Kun,ZHAO Tongbin,et al. Status and development of UAV remote sensing technology in mining surface subsidence and fracture measuring[J]. Coal Science and Technology,2024,52(S2):435−444. DOI: 10.12438/cst.2023-0438
Citation: ZHANG Junyang,WANG Kun,ZHAO Tongbin,et al. Status and development of UAV remote sensing technology in mining surface subsidence and fracture measuring[J]. Coal Science and Technology,2024,52(S2):435−444. DOI: 10.12438/cst.2023-0438

Status and development of UAV remote sensing technology in mining surface subsidence and fracture measuring

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  • Received Date: September 19, 2024
  • Available Online: October 10, 2024
  • The development and utilization of underground coal resources can cause the mining area surface subsidence and fractures and other hazards, which is not conducive to the protection of ecological environment and the sustainable and stable supply of energy and minerals in mining areas. Comprehensive and efficient measuring of surface subsidence and fractures in mining areas can improve the awareness level of mining damage and scientifically prevent secondary disasters. At present, the mainstream measure methods, such as manual measure of ground observation points and satellite remote sensing, have problems such as high operation intensity and expensive cost, and InSAR is difficult to obtain large-scale deformation due to wavelength limitation. As a new method of geographic information acquisition, Unmanned Aerial Vehicle Remote Sensing (UAVRS) technology has the advantages of flexibility, efficiency, accuracy, repeatability, and comprehensive coverage, and has become a research hotspot in mining area surface subsidence and fractures measuring. Systematic review of domestic and abroad literatures and analysis of frontier progress and development trend are conducive to technological innovation and application in this field. Firstly, the main points of UAVRS and the technical process of measuring surface subsidence and fractures in mining areas are introduced briefly. The UAV is equipped with visible light camera, LiDAR, infrared thermal imaging camera and other sensors, generate remote sensing results such as Digital Elevation Model (DEM) and Digital Orthophoto Map (DOM); Then, in terms of surface subsidence measuring, the research progress, technical difficulties and prospects of terrain acquisition, differential DEM subsidence model, subsidence parameters acquisition and horizontal displacement measuring are analyzed by citing literature cases. In the field of surface fractures measuring, the research progress and problems of image processing, machine learning and infrared thermal imaging are introduced. Finally, the future development direction is forecasted from the advantages of UAVRS technology in subsidence measuring, fractures background noise, fractures prediction and identification accuracy, and data processing speed. Research shows that: (1) UAVRS technology is competent for surface topography acquisition and subsidence measuring in mining areas, and fusion with InSAR data can improve the measuring accuracy of subsidence parameters; (2) Based on DEM acquired by UAVRS, image processing and machine learning methods can realize intelligent recognition of surface fractures, and deep learning is studied to eliminate environmental interference and improve the accuracy of fractures recognition; (3) The research of surface horizontal displacement and subsidence law, the improvement of fracture identification rate and its distribution prediction, the rapid and automatic processing of aerial survey data, and the fusion of multi-source remote sensing data are the main development directions of technology application and research in this field. UAVRS technology has broad prospects in the field of mining surface subsidence and fractures measuring, can drive the transformation of green and intelligent development of mines from the technical level.

  • [1]
    中华人民共和国自然资源部. 中国矿产资源报告 [M]. 北京:地质出版社,2022:14−14.
    [2]
    ZHENG Junliang,YAO Wanqiang,LIN Xiaohu,et al. An accurate digital subsidence model for deformation detection of coal mining areas using a UAV-Based LiDAR[J]. Remote Sensing,2022,14(2):421. doi: 10.3390/rs14020421
    [3]
    张舒,吴侃,王响雷,等. 三维激光扫描技术在沉陷监测中应用问题探讨[J]. 煤炭科学技术,2008,36(11):92−95.

    ZHANG Shu,WU Kan,WANG Xianglei,et al. Discussion on application of 3D laser scanning technology to ground subsidence monitoring[J]. Coal Science and Technology,2008,36(11):92−95.
    [4]
    刘一霖,张勤,黄海军,等. 矿区地表大量级沉陷形变短基线集InSAR监测分析[J]. 国土资源遥感,2017,29(2):144−151.

    LIU Yilin,ZHANG Qin,HUANG Haijun,et al. Monitoring and analyzing large scale land subsidence over the mining area using small baseline subset InSAR[J]. Remote Sensing for Land and Resources,2017,29(2):144−151.
    [5]
    罗伟,王飞. 基于无人机遥感技术的煤矿地表监测与分析 [J]. 煤炭科学技术,2021,49(S2):268−273.

    LUO Wei ,WANG Fei. Coal mine surface monitoring and analysis based on UAV remote sensing technology [J]. Coal Science and Technology,2021,49(S2):268−273.
    [6]
    WATTS A C,AMBROSIA V G,HINKLEY E A. Unmanned aircraft systems in remote sensing and scientific research:classification and considerations of use[J]. Remote Sensing,2012,4(6):1671−1692. doi: 10.3390/rs4061671
    [7]
    李德仁,李明. 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报(信息科学版),2014,39(5):505−513,540.

    LI Deren,LI Ming. Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University,2014,39(5):505−513,540.
    [8]
    LEE S,CHOI Y. Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry[J]. Geosystem Engineering,2016,19(4):197−204. doi: 10.1080/12269328.2016.1162115
    [9]
    王昆,杨鹏,吕文生,等. 无人机遥感在矿业领域应用现状及发展态势[J]. 工程科学学报,2020,42(9):1085−1095.

    WANG Kun,YANG Peng,LYU Wensheng,et al. Current status and development trend of UAV remote sensing applications in the mining industry[J]. Chinese Journal of Engineering,2020,42(9):1085−1095.
    [10]
    BAROŇ I,BEČKOVSKý D,MíČA L. Application of infrared thermography for mapping open fractures in deep-seated rockslides and unstable cliffs[J]. Landslides,2012,11(1):15−27.
    [11]
    SUH J,CHOI Y. Mapping hazardous mining-induced sinkhole subsidence using unmanned aerial vehicle (drone) photogrammetry[J]. Environmental Earth Sciences,2017,76(4):144. doi: 10.1007/s12665-017-6458-3
    [12]
    IGNJATOVIĆ STUPAR D,ROŠER J,VULIĆ M. Investigation of Unmanned Aerial Vehicles-Based photogrammetry for large mine subsidence monitoring[J]. Minerals,2020,10(2):196. doi: 10.3390/min10020196
    [13]
    TAN Hao,YU Xuexiang,ZHU Mingfei,et al. Deformation monitoring and spatiotemporal evolution of mining area with Unmanned Aerial Vehicle and D-InSAR technology [J]. Mobile Information Systems, 2022(1):8075611:1−12.
    [14]
    田帅帅,赵艳玲,李亚龙,等. 高潜水位矿区采煤沉陷地DEM的无人机构建方法[J]. 测绘通报,2018,64(3):98−101.

    TIAN Shuaishuai,ZHAO Yanling,LI Yalong,et al. DEM establishing method of mining subsidence in high underground water mining area with UAV[J]. Bulletin of Surveying and Mapping,2018,64(3):98−101.
    [15]
    WHEATON J M,BRASINGTON J,DARBY S E,et al. Accounting for uncertainty in DEMs from repeat topographic surveys:improved sediment budgets[J]. Earth Surface Processes and Landforms,2009,35:136−156.
    [16]
    LIAN Xuguang,LIU Xiaoyu,GE Linlin,et al. Time-series unmanned aerial vehicle photogrammetry monitoring method without ground control points to measure mining subsidence[J]. Journal of Applied Remote Sensing,2021,15(2):024505.
    [17]
    张永庭,徐友宁,梁伟,等. 基于无人机载LiDAR的采煤沉陷监测技术方法——以宁东煤矿基地马连台煤矿为例[J]. 地质通报,2018,37(12):2270−2277. doi: 10.12097/j.issn.1671-2552.2018.12.019

    ZHANG Yongting,XU Youning,LIANG Wei,et al. Technical methods for colliery subsidence disaster monitoring using UAV LiDAR:A case study of the Maliantai colliery,Ningdong coal base,Ningxia[J]. Geological Bulletin of China,2018,37(12):2270−2277. doi: 10.12097/j.issn.1671-2552.2018.12.019
    [18]
    WANG Shuqing,BAI Zechao,LV Yuepeng,et al. Monitoring extractive activity-induced surface subsidence in highland and alpine opencast coal mining areas with Multi-Source data[J]. Remote Sensing,2022,14(14):1−12.
    [19]
    汤伏全,孙伟,樊志刚,等. 基于无人机影像的西部矿区地表沉陷信息提取方法改进[J]. 煤炭科学技术,2023,51(S1):334−342.

    TANG Fuquan,SUN Wei,FAN Zhigang,et al. Improvement of surface subsidence information extraction method based on UAV image modeling in Western Mining Area[J]. Coal Science and Technology,2023,51(S1):334−342.
    [20]
    高银贵,周大伟,安士凯,等. 煤矿开采地表沉陷UAV-摄影测量监测技术研究[J]. 煤炭科学技术,2022,50(5):57−65.

    GAO Yingui,ZHOU Dawei,AN Shikai,et al. Study on surface subsidence in coal mining by UAV-photogrammetry monitoring technology[J]. Coal Science and Technology,2022,50(5):57−65.
    [21]
    ZHOU Dawei,QI Lizhuang,ZHANG Demin,et al. Unmanned Aerial Vehicle (UAV) photogrammetry technology for dynamic mining subsidence monitoring and parameter inversion:a case study in China[J]. IEEE Access,2020,8:16372−16386. doi: 10.1109/ACCESS.2020.2967410
    [22]
    ZHOU Dawei,WANG Ling,AN Shikai,et al. Integration of unmanned aerial vehicle (UAV)-based photogrammetry and InSAR for mining subsidence and parameters inversion:a case study of the Wangjiata Mine,China[J]. Bulletin of Engineering Geology and the Environment,2022,81(8):343. doi: 10.1007/s10064-022-02845-2
    [23]
    周大伟,安士凯,吴侃,等. 矿山开采损害InSAR /UAV融合监测关键技术及应用研究[J]. 煤炭科学技术,2022,50(10):121−134.

    ZHOU Dawei,AN Shikai,WU Kan,et al. Research on the key issues and application of InSAR /UAV fusion monitoring for coal mining damages[J]. Coal Science and Technology,2022,50(10):121−134.
    [24]
    WANG Rui,WU Kan,HE Qimin,et al. A novel method of monitoring surface subsidence law based on probability integral model combined with active and passive remote sensing data[J]. Remote Sensing,2022,14(2):299. doi: 10.3390/rs14020299
    [25]
    胡东升,程小凯,张雅飞,等. 空天地一体化监测联合反演开采沉陷概率积分预计参数研究[J]. 煤炭工程,2023,55(1):81−86.

    HU Dongsheng,CHENG Xiaokai,ZHANG Yafei,et al. Parameter inversion of mining subsidence probability integration prediction method based on space-air-ground integrated monitoring[J]. Coal Engineering,2023,55(1):81−86.
    [26]
    ZHANG Yafei,LIAN Xugang,GE Linlin,et al. Surface subsidence monitoring induced by underground coal mining by combining DInSAR and UAV photogrammetry[J]. Remote Sensing,2022,14(19):4711. doi: 10.3390/rs14194711
    [27]
    郭增长,柴华彬. 煤矿开采沉陷学 [M]. 北京:煤炭工业出版社,2013:127−128.
    [28]
    符茵,刘巧,刘国祥,等. 基于无人机影像的冰面流速与高程变化提取方法[J]. 地理学报,2021,76(5):1245−1256.

    FU Yin,LIU Qiao,LIU Guoxiang,et al. Monitoring glacier surface velocity and ablation using high-resolution UAV imagery[J]. Acta Geographica Sinica,2021,76(5):1245−1256.
    [29]
    BENOIT L,GOURDON A,VALLAT R,et al. A high-resolution image time series of the Gorner Glacier – Swiss Alps – derived from repeated unmanned aerial vehicle surveys[J]. Earth System Science Data,2019,11(2):579−588. doi: 10.5194/essd-11-579-2019
    [30]
    DALL’ASTA E,FORLANI G,RONCELLA R,et al. Unmanned Aerial Systems and DSM matching for rock glacier monitoring[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2017,127:102−114. doi: 10.1016/j.isprsjprs.2016.10.003
    [31]
    TURNER D,LUCIEER A,DE JONG S. Time series analysis of landslide dynamics using an Unmanned Aerial Vehicle (UAV)[J]. Remote Sensing,2015,7(2):1736−1757. doi: 10.3390/rs70201736
    [32]
    何柯璐,汤伏全,李振洪. 基于地形特征的采煤沉陷盆地构建与水平移动信息智能提取方法[J]. 武汉大学学报(信息科学版),2023,48(5):717−729.

    HE Kelu,TANG Fuquan,LI Zhenhong. Coal mining subsidence basin construction and horizontal movement intelligent extraction based on topographic features[J]. Geomatics and Information Science of Wuhan University,2023,48(5):717−729.
    [33]
    侯恩科,张杰,谢晓深,等. 无人机遥感与卫星遥感在采煤地表裂缝识别中的对比[J]. 地质通报,2019,38(S1):443−448.

    HOU Enke,ZHANG Jie,XIE Xiaoshen,et al. Contrast application of unmanned aerial vehicle remote sensing and satellite remote sensing technology relating to ground surface cracks recognition in coal mining area[J]. Geological Bulletin of China,2019,38(S1):443−448.
    [34]
    CAO Wenming,LIU Qifan,HE Zhequan. Review of pavement defect detection methods[J]. IEEE Access,2020,8:14531−14544. doi: 10.1109/ACCESS.2020.2966881
    [35]
    STUMPF A,MALET J-P,KERLE N,et al. Image-based mapping of surface fissures for the investigation of landslide dynamics[J]. Geomorphology,2013,186:12−27. doi: 10.1016/j.geomorph.2012.12.010
    [36]
    韦博文,刘国祥,汪致恒. 基于改进的MF-FDOG算法和无人机影像提取黄土地区地裂缝[J]. 测绘,2018,41(2):51−56,61. doi: 10.3969/j.issn.1674-5019.2018.02.001

    WEI Bowen,LIU Guoxiang,WANG Zhiheng. Extracting ground fissures in loess landform area using Modified F-FDOG algorithm and UAV images[J]. Surveying and Mapping,2018,41(2):51−56,61. doi: 10.3969/j.issn.1674-5019.2018.02.001
    [37]
    杨娜,张翀,李天昊. 基于无人机与计算机视觉的中国古建筑木结构裂缝监测系统设计[J]. 工程力学,2021,38(3):27−39. doi: 10.6052/j.issn.1000-4750.2020.04.0263

    YANG Na,ZHANG Chong,LI Tianhao. design of crack monitoring system for Chinese ancient wooden buildings based on UAV and CV[J]. Engineering Mechanics,2021,38(3):27−39. doi: 10.6052/j.issn.1000-4750.2020.04.0263
    [38]
    彭瑶瑶,王思远,傅兴玉,等. 无人机影像辅助下的路桥病害智能检测[J]. 测绘通报,2017,63(8):67−70,105.

    PENG Yaoyao,WANG Siyuan,FU Xingyu,et al. Intelligent road and bridge disease detection method based on UAV images[J]. Bulletin of Surveying and Mapping,2017,63(8):67−70,105.
    [39]
    袁依文,雷斌. 无人机视觉技术在道路裂缝分类中的应用[J]. 机械设计与制造,2022,60(5):235−239. doi: 10.3969/j.issn.1001-3997.2022.05.052

    YUAN Yiwen,LEI Bin. The application of UAV vision technology in the classification of road cracks[J]. Machinery Design & Manufacture,2022,60(5):235−239. doi: 10.3969/j.issn.1001-3997.2022.05.052
    [40]
    郝巨鸣,杨景玉,韩淑梅,等. 引入Ghost模块和ECA的YOLOv4公路路面裂缝检测方法[J]. 计算机应用,2023,43(4):1284−1290.

    HAO Juming,YANG Jingyu,HAN Shumei,et al. YOLOv4 highway pavement crack detection method using Ghost module and ECA[J]. Journal of Computer Applications,2023,43(4):1284−1290.
    [41]
    马学志,范剑雄,柴雪松,等. 无人机巡检系统在铁路混凝土桥梁检测中的应用[J]. 铁道建筑,2021,61(12):76−80. doi: 10.3969/j.issn.1003-1995.2021.12.17

    MA Xuezhi,FAN Jianxiong,CHAI Xuesong,et al. Application of UAV inspection system in railway concrete bridge inspection[J]. Railway Engineering,2021,61(12):76−80. doi: 10.3969/j.issn.1003-1995.2021.12.17
    [42]
    袁磊,苏永华,张斌,等. 无人机摄影测量技术在铁路桥梁巡检中的应用[J]. 铁道建筑,2022,62(3):83−87. doi: 10.3969/j.issn.1003-1995.2022.03.19

    YUAN Lei,SU Yonghua,ZHANG Bin,et al. Application of UAV photogrammetry technology in railway bridge inspection[J]. Railway Engineering,2022,62(3):83−87. doi: 10.3969/j.issn.1003-1995.2022.03.19
    [43]
    刘春,艾克然木·艾克拜尔,蔡天池. 面向建筑健康监测的无人机自主巡检与裂缝识别[J]. 同济大学学报(自然科学版),2022,50(7):921−932,918.

    LIU Chun,AKBAR Akram,CAI Tianchi. UAV autonomous inspection and crack detection towards building health monitoring[J]. Journal of Tongji University(Natural Science),2022,50(7):921−932,918.
    [44]
    丁威,俞珂,舒江鹏. 基于深度学习和无人机的混凝土结构裂缝检测方法[J]. 土木工程学报,2021,54(S1):1−12.

    DING Wei,YU Ke,SHU Jiangpeng. Method for detecting cracks in concrete structures based on deep learning and UAV[J]. China Civil Engineering Journal,2021,54(S1):1−12.
    [45]
    文青. 基于深度学习的建筑物表面裂缝检测技术研究与实现[D]. 北京:北京邮电大学,2019.

    WEN Qing. Research and implementation of building surface crack detection technology based on Deep Learning [D]. Beijing:Beijing University of Posts and Telecommunications,2019.
    [46]
    李怡静,程浩东,李火坤,等. 基于改进U2-Net与迁移学习的无人机影像堤防裂缝检测[J]. 水利水电科技进展,2022,42(6):52−59.

    LI Yijing,CHENG Haodong,LI Huokun,et al. Crack detection of embankment in UAV images based on improved U²Net and transfer learning[J]. Advances in Science and Technology of Water Resources,2022,42(6):52−59.
    [47]
    杨奇让,胡振琪,韩佳政,等. 煤矿区无人机影像采动地裂缝提取方法研究[J]. 煤炭科学技术,2023,51(6):187−196.

    YANG Qirang,HU Zhenqi,HAN Jiazheng,et al. Research on extraction method of ground fissures from UAV image mining in coal mine[J]. Coal Science and Technology,2023,51(6):187−196.
    [48]
    汤伏全,李林宽,李小涛,等. 基于无人机影像的采动地表裂缝特征研究[J]. 煤炭科学技术,2020,48(10):130−136.

    TANG Fuquan,LI Linkuan,LI Xiaotao,et al. Research on characteristics of mining-induced surface cracks based on UAV images[J]. Coal Science and Technology,2020,48(10):130−136.
    [49]
    ZHANG Lei,YANG Fan,ZHANG Y D,et al. Road crack detection using deep convolutional neural network[A]. Proceedings of the IEEE International Conference on Image Processing [C]. Phoenix,AZ,USA:Institute of Electrical and Electronics Engineers (IEEE),2016:3708−3712.
    [50]
    邓雅心,骆旭佳,李红林,等. 基于无人机倾斜摄影测量技术的水电站坝面裂缝检测研究[J]. 科技创新与应用,2021,11(5):158−161,166.

    DENG Yaxin,LUO Xujia,LI Honglin,et al,Research on water power station dam surface crack detection based on UAV oblique photogrammetry [J]. Technological Innovation and Application,2021,11 (5):158−161,166.
    [51]
    余加勇,刘宝麟,尹东,等. 基于YOLOv5和U-Net3+的桥梁裂缝智能识别与测量 [J]. 湖南大学学报(自然科学版),2023,50(5):65−73.

    YU Jiayong,LIU Baolin,YIN Dong,et al,Intelligent identification and measurement of bridge cracks based on YOLOv5 and U-Net3+ [J]. Journal of Hunan University(Natural Sciences),2023,50(5):65−73.
    [52]
    程健,叶亮,郭一楠,等. 采空区地裂缝混合域注意力变形卷积网络检测方法[J]. 煤炭学报,2020,45(S2):993−1002.

    CHENG Jian,YE Liang,GUO Yinan,et al. An aerial image detection method of ground crack in goaf based on deformable convolutional network with hybrid domain attention[J]. Journal of China Coal Society,2020,45(S2):993−1002.
    [53]
    王臻,王辉,李国锋. 弱监督的无人机影像地裂缝自动提取[J]. 实验技术与管理,2022,39(3):51−56.

    WANG Zhen,WANG Hui,LI Guofeng. Automatic extraction of ground fissures from UAV images by weakly supervised learning[J]. Experimental Technology and Management,2022,39(3):51−56.
    [54]
    赵毅鑫,许多,孙波,等. 基于无人机红外遥感和边缘检测技术的采动地裂缝辨识[J]. 煤炭学报,2021,46(2):624−637.

    ZHAO Yixin,XU Duo,SUN Bo,et al. Investigation on ground fissure identification using UAV infrared remote sensing and edge detection technology[J]. Journal of China Coal Society,2021,46(2):624−637.
    [55]
    ZHAO Yixin,SUN Bo,LIU Shimin,et al. Identification of mining induced ground fissures using UAV and infrared thermal imager:Temperature variation and fissure evolution[J]. ISPRS Journal of Photogrammetry and Remote Sensing,2021,180:45−64. doi: 10.1016/j.isprsjprs.2021.08.005
    [56]
    赵毅鑫,许多,张康宁,等. 采动地表浅层隐蔽裂缝的无人机红外识别现场试验[J]. 煤炭学报,2022,47(5):1921−1932.

    ZHAO Yixin,XU Duo,ZHANG Kangning,et al. In-situ experiment on the identification of shallow hidden mining induced ground fissure using UAV infrared technology[J]. Journal of China Coal Society,2022,47(5):1921−1932.
    [57]
    LIU Yufei,CHO Soojin,SPENCER B F,et al. Concrete crack assessment using digital image processing and 3D scene reconstruction[J]. Journal of Computing in Civil Engineering,2016,30(1):04014124. doi: 10.1061/(ASCE)CP.1943-5487.0000446

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