Structural planes recognition and occurrence statistics information collection method for high rock slopes
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摘要: 准确识别岩质高边坡结构面和获取产状统计信息是进行边坡稳定性分析的重要前提。无人机摄影测量技术为解决高边坡结构面准确勘测难题提供了可能，但缺少高效准确的影像后处理方法，且现有研究没有考虑结构面产状信息特征的不确定性，致使结构面识别准确性差、效率低。针对该问题，以江西省南昌市某露天矿高边坡为研究背景，提出了融合无人机摄影、后处理算法及统计分析的一体化结构面识别与产状统计信息采集方法。首先，通过Phantom 4 Pro V2.0无人机获取边坡表面影像；其次，利用Context Capture软件进行处理，得到高密度三维点云数据；然后，采用K近邻（KNN）算法中的确定近邻点数量法构建相似点集，采用基于密度的聚类（DBSCAN）算法进行聚类分析，从而实现边坡结构面识别，获得结构面产状信息并进行统计特征分析；最后，通过现场勘测数据进行对比验证。结果表明：该方法能够快速获取完整的高密度点云数据，准确高效地识别岩质高边坡大部分结构面，识别结果与边坡工程现场实际情况基本吻合；该方法可获取高边坡结构面数量、产状信息及其统计特征，大部分结构面倾角和倾向概率分布与实测数据拟合较好，为高边坡裂隙网络模型构建及稳定性分析提供了重要数据来源。Abstract: Accurately recognizing the structural planes of high rock slopes and obtaining occurrence information are important prerequisites for conducting slope stability analysis. Unmanned aerial vehicle photogrammetry technology provides the possibility to solve the problem of accurate surveying of high slope structural planes. But it lacks efficient and accurate image post-processing methods. The existing research has not considered the uncertainty of structural plane occurrence information features, resulting in poor accuracy and efficiency in structural plane recognition. In order to solve the above problems, taking a high slope of an open-pit mine in Nanchang City, Jiangxi Province as the research background, an integrated method for recognizing structural planes and collecting occurrence information by integrating unmanned aerial vehicle photography, post-processing algorithms, and statistical analysis is proposed. Firstly, the method obtains surface images of the slope using the Phantom 4 Pro V2.0 unmanned aerial vehicle. Secondly, the method uses Context Capture software for processing, and the high-density 3D point cloud data is obtained. Secondly, the K-nearest neighbor (KNN) algorithm is used to determine the number of nearest neighbor points to construct a set of similar points. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for clustering analysis to recognize slope structural planes, obtain structural plane occurrence information, and perform statistical feature analysis. Finally, comparative verification is conducted through on-site survey data. The results show that this method can quickly obtain complete high-density point cloud data, accurately and efficiently recognize most structural planes of high rock slopes. The recognition results are basically consistent with the actual situation of slope engineering sites. This method can obtain information on the number, occurrence, and statistical features of high slope structural planes. The probability distribution of most structural plane dip angles and inclinations fits well with the measured data, providing an important data source for the construction of high slope fracture network models and stability analysis.
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图 3 上层岩体三维点云模型
Figure 3. 3D point cloud model of rock mass at the upper part of the slope
图 4 下层岩体三维点云模型
Figure 4. 3D point cloud model of rock mass at the lower part of the slope
图 6 上层岩体法向量密度
Figure 6. Normal vector density of rock mass at the upper part of the slope
图 7 下层岩体法向量密度
Figure 7. Normal vector density of rock mass at the lower part of the slope
图 10 结构面识别结果与实际边坡结构面的对比
Figure 10. Comparison between structural plane recognition results and actual slope structural plane
图 11 结构面组J1—J6倾角和倾向概率分布直方图与拟合曲线的比较
Figure 11. Comparison of probability distribution histograms and fitting curves for dip and dip angles of structural plane groups J1-J6
表 1 上层岩体拟合平面参数及结构面产状信息
Table 1. Parameters of fitting plane and orientation information of structural plane of rock mass at the upper part of the slope
主极点 聚类号 点数量 拟合平面方程参数 倾角/（°） 倾向/（°） Aij Bij Cij Dij J1 1 62 356 −0.407 0.813 −0.416 −49.929 65.43 153.40 2 34 681 0.268 −0.877 0.399 42.268 66.46 163.01 J2 1 54 135 −0.010 0.860 −0.510 −33.920 59.33 179.36 2 37 259 0.253 0.810 −0.529 −39.055 58.04 197.33 J3 1 62 073 0.334 −0.288 0.898 −58.570 26.17 130.82 2 32 684 0.323 −0.492 0.808 −10.036 36.07 146.75 J4 1 88 328 0.269 0.947 −0.177 −44.659 79.83 195.89 2 7 973 0.241 0.961 −0.136 −47.637 82.17 194.10 J5 1 37 880 −0.902 0.420 −0.100 −11.179 84.26 114.98 2 27 762 0.748 −0.620 0.237 33.931 76.31 129.69 J6 1 20 051 0.643 0.766 −0.020 −31.941 88.88 220.03 2 6 660 0.600 0.792 −0.116 0.267 83.32 217.13 
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表 2 下层岩体拟合平面参数及结构面产状信息
Table 2. Parameters of fitting plane and orientation information of structural plane of rock mass at the lower part of the slope
主极点 聚类号 点数 拟合平面方程参数 倾角/（°） 倾向/（°） Aij Bij Cij Dij J7 1 52 786 −0.297 0.687 −0.664 −13.625 48.42 156.60 2 23 217 0.317 −0.645 0.696 22.360 45.93 153.80 J8 1 43 880 0.001 −0.825 0.565 30.001 55.62 179.93 2 28 671 −0.197 0.846 −0.495 −26.153 60.35 166.89 J9 1 90 517 0.742 −0.622 0.251 15.808 75.44 129.97 2 60 842 0.723 −0.691 0.001 56.813 89.96 133.68 J10 1 27 216 0.482 0.793 −0.372 15.417 68.19 211.28 2 10 396 0.252 0.928 −0.274 −42.152 74.07 195.16 J11 1 7 602 0.158 −0.987 0.024 50.753 88.61 350.88 2 1 690 0.137 −0.989 0.049 53.162 87.16 352.14
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表 3 边坡上层和下层岩体结构面产状统计特征
Table 3. Statistical characteristics of rock mass structural plane orientation at the upper and lower part of the slope
上层岩体 下层岩体 主极点 产状 分布特征 均值$\mu $ 标准差$\sigma $ 主极点 产状 分布特征 均值$\mu $ 标准差$\sigma $ J1 倾角 对数正
态分布68.32 2.92 J7 倾角 正态分布 43.39 4.42 倾向 对数正
态分布156.21 6.78 倾向 正态分布 154.31 8.50 J2 倾角 正态分布 56.97 5.31 J8 倾角 对数正
态分布59.33 5.36 倾向 正态分布 184.40 8.40 倾向 对数正
态分布170.74 5.73 J3 倾角 正态分布 34.95 6.64 J9 倾角 正态分布 72.74 7.67 倾向 正态分布 142.94 15.87 倾向 对数正
态分布138.26 12.24 J4 倾角 正态分布 81.39 2.93 J10 倾角 正态分布 72.09 16.48 倾向 对数正
态分布201.24 3.16 倾向 — — — J5 倾角 正态分布 77.58 4.47 J11 倾角 — — — 倾向 正态分布 127.87 6.79 倾向 — — — J6 倾角 正态分布 83.22 4.97 倾向 对数正
态分布216.16 3.17
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[1] 程昊,唐辉明,孙淼军,等. 基于半迹长测线法的岩体结构面全迹长概率分布及平均迹长新算法[J]. 岩石力学与工程学报,2013,32(增刊2):3073-3082.CHENG Hao,TANG Huiming,SUN Miaojun,et al. New algorithm to determine rock mass discontinuity trace length probabilistic distribution and mean trace length based on semi-trace scanline surveys[J]. Chinese Journal of Rock Mechanics and Engineering,2013,32(S2):3073-3082. [2] 黄磊. 测线取样法引起的岩体结构面几何偏差纠正[D]. 武汉:中国地质大学,2014.HUANG Lei. Correction of geometric deviation of rock mass structural plane caused by survey line sampling method[D]. Wuhan:China University of Geosciences,2014. [3] 朱合华,潘柄屹,武威,等. 岩体结构面信息采集及识别方法研究进展[J]. 应用基础与工程科学学报,2023,31(6):1339-1360.ZHU Hehua,PAN Bingyi,WU Wei,et al. Review on collection and extraction methods of rock mass discontinuity information[J]. Journal of Basic Science and Engineering,2023,31(6):1339-1360. [4] 王岩,马宏伟,王星,等. 基于迭代最近点的井下无人机实时位姿估计[J]. 工矿自动化,2019,45(9):25-29.WANG Yan,MA Hongwei,WANG Xing,et al. Real-time pose estimation of underground unmanned aerial vehicle based on ICP method[J]. Industry and Mine Automation,2019,45(9):25-29. [5] 高海跃,王凯,王保兵,等. 基于全局点云地图的煤矿井下无人机定位方法[J]. 工矿自动化,2023,49(8):81-87,133.GAO Haiyue,WANG Kai,WANG Baobing,et al. Positioning method for underground unmanned aerial vehicles in coal mines based on global point cloud map[J]. Journal of Mine Automation,2023,49(8):81-87,133. [6] 赵明宇,王凤艳,王明常,等. 基于无人机摄影测量的岩体结构面信息获取[J]. 工程地质学报,2018,26(增刊1):480-487.ZHAO Mingyu,WANG Fengyan,WANG Mingchang,et al. Rock mass discontinuity acquisition based on photo-grammetry of UAV[J]. Journal of Engineering Geology,2018,26(S1):480-487. [7] 熊开治,任志远,赵亚龙,等. 基于无人机航测的丹霞地貌区危岩结构面识别与三维裂隙网络模型——以重庆四面山景区为例[J]. 中国地质灾害与防治学报,2021,32(5):62-69.XIONG Kaizhi,REN Zhiyuan,ZHAO Yalong,et al. Identification of dangerous rock structural planes and fracture network model in Danxia landform based on UAV aerial survey:a case study at Simianshan scenic area of Chongqing[J]. The Chinese Journal of Geological Hazard and Control,2021,32(5):62-69. [8] 李水清,张慧超,刘乳燕. 无人机摄影测量半自动统计岩体结构面产状[J]. 科学技术与工程,2017,17(26):18-22. doi: 10.3969/j.issn.1671-1815.2017.26.003LI Shuiqing,ZHANG Huichao,LIU Ruyan. Semi-automatically counting orientations of rock mass structural plane based on unmanned aerial vehicle photogrammetry[J]. Science Technology and Engineering,2017,17(26):18-22. doi: 10.3969/j.issn.1671-1815.2017.26.003 [9] 沙鹏,孔德珩,王绍亮,等. 高陡边坡岩体结构信息无人机识别与离散元数值模拟[J]. 工程地质学报,2022,30(5):1658-1668.SHA Peng,KONG Deheng,WANG Shaoliang,et al. Identification of rockmass discontinuities and 3d discrete element modeling of high slope based on unmanned aerial vehicle photogrammetry[J]. Journal of Engineering Geology,2022,30(5):1658-1668. [10] 贾曙光,金爱兵,赵怡晴. 无人机摄影测量在高陡边坡地质调查中的应用[J]. 岩土力学,2018,39(3):1130-1136.JIA Shuguang,JIN Aibing,ZHAO Yiqing. Application of UAV oblique photogrammetry in the field of geology survey at the high and steep slope[J]. Rock and Soil Mechanics,2018,39(3):1130-1136. [11] 张恺,伍法权,沙鹏,等. 基于无人机倾斜摄影的矿山边坡岩体结构编录方法与工程应用[J]. 工程地质学报,2019,27(6):1448-1455.ZHANG Kai,WU Faquan,SHA Peng,et al. Geological cataloging method with oblique photo-graphy of UAV for open-pit slope and its application[J]. Journal of Engineering Geology,2019,27(6):1448-1455. [12] LIU Yongqiang,CHEN Jianping,TAN Chun,et al. Intelligent scanning for optimal rock discontinuity sets considering multiple parameters based on manifold learning combined with UAV photogrammetry[J]. Engineering Geology,2022,309. DOI: 10.1016/j.enggeo.2022.106851. [13] RIQUELME A J,ABELLÁN A,TOMÁS R,et al. A new approach for semi-automatic rock mass joints recognition from 3D point clouds[J]. Computers & Geosciences,2014,68:38-52. [14] COVER T,HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory,1967,13(1):21-27. doi: 10.1109/TIT.1967.1053964 [15] LATO M J,DIEDERICHS M S,HUTCHINSON D J. Bias correction for view-limited lidar scanning of rock outcrops for structural characterization[J]. Rock Mechanics and Rock Engineering,2010,43(5):615-628. doi: 10.1007/s00603-010-0086-5 [16] RENCHER A C,CHRISTENSEN W F. Methods of multivariate analysis[M]. Hoboken:Wiley,2012. [17] ESTER M,KRIEGEL H P,SANDER J,et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]. 2nd Internatinal Conference on Knowledge Discovery and Data Mining,Portland, 1996. [18] TONINI M,ABELLAN A. Rockfall detection from terrestrial LiDAR point clouds:a clustering approach using R[J]. Journal of Spatial Information Science,2014(8):95-110. [19] 何旷宇. 基于无人机贴近摄影高陡边坡岩体表面信息识别方法[D]. 长沙:湖南大学,2021.HE Kuangyu. Study of rock mass surface information identification in high steep slopes based on nap-of-the-object photography[D]. Changsha:Hunan University,2021. [20] 郭戈. 基于三维激光扫描技术的岩体结构面识别与岩体完整性评价研究[D]. 宜昌:三峡大学,2023.GUO Ge. Study on rock mass structural plane identification and rock mass integrity evaluation based on 3D laser scanning technology[D]. Yichang:China Three Gorges University,2023. [21] 叶震,许强,刘谦,等. 无人机倾斜摄影测量在边坡岩体结构面调查中的应用[J]. 武汉大学学报(信息科学版),2020,45(11):1739-1746.YE Zhen,XU Qiang,LIU Qian,et al. Application of unmanned aerial vehicle oblique photogrammetry to investigation of high slope rock structure[J]. Geomatics and Information Science of Wuhan University,2020,45(11):1739-1746. [22] 陈昌富,何旷宇,余加勇,等. 基于无人机贴近摄影的高陡边坡结构面识别[J]. 湖南大学学报(自然科学版),2022,49(1):145-154.CHEN Changfu,HE Kuangyu,YU Jiayong,et al. Identification of discontinuities of high steep slope based on UAV nap-of-the-object photography[J]. Journal of Hunan University (Natural Sciences),2022,49(1):145-154. -
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