Volume 50 Issue 6
Jun.  2022
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Abstract
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AN Lin，HAN Baoshan，LI Peng，et al. Research on interpolation error analysis of geological modeling of intelligent working face[J]. Coal Geology & Exploration，2022，50(6)：184−189. DOI: 10.12363/issn.1001-1986.21.07.0368
Citation: AN Lin，HAN Baoshan，LI Peng，et al. Research on interpolation error analysis of geological modeling of intelligent working face[J]. Coal Geology & Exploration，2022，50(6)：184−189. DOI: 10.12363/issn.1001-1986.21.07.0368
shu

Research on interpolation error analysis of geological modeling of intelligent working face

  • 1.

    Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China

  • 2.

    College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China

  • 3.

    China Coal Research Institute, Beijing 100013, China

More Information
  • Received Date: July 04, 2021
  • Revised Date: December 07, 2021
  • Available Online: June 07, 2022
  • Published Date: June 24, 2022
    Graphical Abstract
    • Abstract
  • Working face modeling generally obtains the actual distribution of the working face through detection methods such as three-dimensional seismic, tunnel and borehole surveys, and then it establishes the corresponding model using interpolation algorithms. During the modeling process, sampling data are the foundation and interpolation is the necessary way to realize the working face model. The interpolation algorithm and the amount of sampled data affect the accuracy of the face model to varying degrees. Quantitative research on the factors affecting the accuracy of the face model will provide important reference value for the optimization of the interpolation algorithm and the amount of sampled data. On the basis of the detection data of the working face, firstly, the cross-validation method is used to calculate the interpolation errors of the contrast function interpolation, DSI interpolation and Kriging interpolation. Then, in order to solve the problem of large sampling amount of transparent working surface modeling, the relative spacing error is proposed, and the relative spacing error of the models with 13 groups of different sampling scales is calculated. The results show that: (1) The average absolute errors of DSI interpolation, Kriging interpolation and function interpolation are respectively in the process of constructing a transparent face model. They are 0.015 5, 0.022 5, and 0.231 2, so the model constructed according to the DSI interpolation algorithm has the highest accuracy, followed by the Kriging interpolation algorithm, and the function interpolation algorithm is the worst. (2) As the amount of sampled data increases, the error of the model gradually decreases. When the amount of sampled data is less than 10%, the interpolation error decreases greatly; but when the amount of sampled data is greater than 10%, the decrease tends to be gentle. It is recommended that the sample data volume could be greater than 10% for constructing the working face model. (3) In the actual construction process of the transparent working face, it is recommended to use the DSI interpolation algorithm; at the same time, sampling is carried out according to the optimal update distance and the optimal sampling interval obtained by the analysis of the minimum sampling data volume, so as to increase the local data volume of the working face.
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    • References (22)
  • [1]
    张瑞新,毛善君,赵红泽,等. 智慧露天矿山建设基本框架及体系设计[J]. 煤炭科学技术,2019,47(10):1−23. ZHANG Ruixin,MAO Shanjun,ZHAO Hongze,et al. Framework and structure design of system construction for intelligent open–pit mine[J]. Coal Science and Technology,2019,47(10):1−23.

    ZHANG Ruixin, MAO Shanjun, ZHAO Hongze, et al. Framework and structure design of system construction for intelligent open–pit mine[J]. Coal Science and Technology, 2019, 47(10): 1–23.
    [2]
    王忠鑫,孙鑫,王金金,等. 基于BIM的智慧露天矿协同工作平台架构与关键技术[J]. 煤炭科学技术,2019,47(10):36−42. WANG Zhongxin,SUN Xin,WANG Jinjin,et al. Collaborative work platform architecture and key technologies of intelligent open pit mine based on BIM[J]. Coal Science and Technology,2019,47(10):36−42.

    WANG Zhongxin, SUN Xin, WANG Jinjin, et al. Collaborative work platform architecture and key technologies of intelligent open pit mine based on BIM[J]. Coal Science and Technology, 2019, 47(10): 36–42.
    [3]
    王国法,刘峰,孟祥军,等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1−36. WANG Guofa,LIU Feng,MENG Xiangjun,et al. Research and practice on intelligent coal mine construction (primary stage)[J]. Coal Science and Technology,2019,47(8):1−36.

    WANG Guofa, LIU Feng, MENG Xiangjun, et al. Research and practice on intelligent coal mine construction (primary stage)[J]. Coal Science and Technology, 2019, 47(8): 1–36.
    [4]
    程建远,朱梦博,崔伟雄,等. 回采工作面递进式煤厚动态预测试验研究[J]. 煤炭科学技术,2019,47(1):237−244. CHENG Jianyuan,ZHU Mengbo,CUI Weixiong,et al. Experimental study of coal thickness progressive prediction in working face[J]. Coal Science and Technology,2019,47(1):237−244.

    CHENG Jianyuan, ZHU Mengbo, CUI Weixiong, et al. Experimental study of coal thickness progressive prediction in working face[J]. Coal Science and Technology, 2019, 47(1): 237–244.
    [5]
    程建远,朱梦博,王云宏,等. 煤炭智能精准开采工作面地质模型梯级构建及其关键技术[J]. 煤炭学报,2019,44(8):2285−2295. CHENG Jianyuan,ZHU Mengbo,WANG Yunhong,et al. Cascade construction of geological model of longwall panel for intelligent precision coal mining and its key technology[J]. Journal of China Coal Society,2019,44(8):2285−2295.

    CHENG Jianyuan, ZHU Mengbo, WANG Yunhong, et al. Cascade construction of geological model of longwall panel for intelligent precision coal mining and its key technology[J]. Journal of China Coal Society, 2019, 44(8): 2285–2295.
    [6]
    李海涛,邵泽东. 空间插值分析算法综述[J]. 计算机系统应用,2019,28(7):1−8. LI Haitao,SHAO Zedong. Review of spatial interpolation analysis algorithm[J]. Computer Systems & Applications,2019,28(7):1−8.

    LI Haitao, SHAO Zedong. Review of spatial interpolation analysis algorithm[J]. Computer Systems & Applications, 2019, 28(7): 1–8.
    [7]
    郑佳荣,李青元,曹代勇. 地矿建模中常用插值方法插值效果分析[J]. 有色金属(矿山部分),2012,64(1):69−72. ZHENG Jiarong,LI Qingyuan,CAO Daiyong. Analysis on interpolation methods used in geological modeling[J]. Nonferrous Metals (Mining Section),2012,64(1):69−72.

    ZHENG Jiarong, LI Qingyuan, CAO Daiyong. Analysis on interpolation methods used in geological modeling[J]. Nonferrous Metals(Mining Section), 2012, 64(1): 69–72.
    [8]
    续东,花向红,王彬. 一种应用K–近邻算法优选点云数据生成格网DEM插值的方法[J]. 测绘地理信息,2019,44(6):27−30. XU Dong,HUA Xianghong,WANG Bin. Optimization of an interpolation method of point cloud data using K–NN algorithm to generate grid DEM[J]. Journal of Geomatics,2019,44(6):27−30.

    XU Dong, HUA Xianghong, WANG Bin. Optimization of an interpolation method of point cloud data using K–NN algorithm to generate grid DEM[J]. Journal of Geomatics, 2019, 44(6): 27–30.
    [9]
    陈学习,吴立新,车德福,等. 基于钻孔数据的含断层地质体三维建模方法[J]. 煤田地质与勘探,2005,33(5):5−8. CHEN Xuexi,WU Lixin,CHE Defu,et al. 3D modeling method of geological bodies including faults based on borehole data[J]. Coal Geology & Exploration,2005,33(5):5−8.

    CHEN Xuexi, WU Lixin, CHE Defu, et al. 3D modeling method of geological bodies including faults based on borehole data[J]. Coal Geology & Exploration, 2005, 33(5): 5–8.
    [10]
    王润怀,李永树. 异源点集下三维断层建模方法[J]. 煤田地质与勘探,2007,35(5):11−15. WANG Runhuai,LI Yongshu. 3D fault modeling on point sets from different sources[J]. Coal Geology & Exploration,2007,35(5):11−15. DOI: 10.3969/j.issn.1001-1986.2007.05.003

    WANG Runhuai, LI Yongshu. 3D fault modeling on point sets from different sources[J]. Coal Geology & Exploration, 2007, 35(5): 11–15. DOI: 10.3969/j.issn.1001-1986.2007.05.003
    [11]
    樊帆,李思,傅佩红,等. 基于等高线渲染法的克里金插值展示技术[J]. 中国农学通报,2019,35(35):129−133. FAN Fan,LI Si,FU Peihong,et al. A technology of displaying Kriging interpolation based on contour rendering method[J]. Chinese Agricultural Science Bulletin,2019,35(35):129−133. DOI: 10.11924/j.issn.1000-6850.casb18090136

    FAN Fan, LI Si, FU Peihong, et al. A technology of displaying Kriging interpolation based on contour rendering method[J]. Chinese Agricultural Science Bulletin, 2019, 35(35): 129–133. DOI: 10.11924/j.issn.1000-6850.casb18090136
    [12]
    MALLET J L. Discrete smooth interpolation[J]. Acm Transactions on Graphics,1989,8(2):121−144. DOI: 10.1145/62054.62057
    [13]
    MALLET J L. Discrete smooth interpolation in geometric modelling[J]. Computer Aided Design,1992,24(4):178−191. DOI: 10.1016/0010-4485(92)90054-E
    [14]
    潘懋,方裕,屈红刚. 三维地质建模若干基本问题探讨[J]. 地理与地理信息科学,2007,23(3):1−5. PAN Mao,FANG Yu,QU Honggang. Discussion on several foundational issues in three–dimensional geological modeling[J]. Geography and Geo–Information Science,2007,23(3):1−5. DOI: 10.3969/j.issn.1672-0504.2007.03.001

    PAN Mao, FANG Yu, QU Honggang. Discussion on several foundational issues in three–dimensional geological modeling[J]. Geography and Geo–Information Science, 2007, 23(3): 1–5. DOI: 10.3969/j.issn.1672-0504.2007.03.001
    [15]
    孟小红, 王卫民, 姚长利, 等. 地质模型计算机辅助设计原理与应用[M]. 北京: 地质出版社, 2001.
    [16]
    徐能雄, 段庆伟, 梅钢, 等. 三维地质建模方法及程序实现[M]. 北京: 地质出版社, 2011.
    [17]
    NALDER I A,WEIN R W. Spatial interpolation of climatic normals:Test of a new method in the Canadian boreal forest[J]. Agricultural and Forest Meteorology,1998,92(4):211−225. DOI: 10.1016/S0168-1923(98)00102-6
    [18]
    KIDNER D B. Higher–order interpolation of regular grid digital elevation models[J]. International Journal of Remote Sensing,2003,24(14):2981−2987. DOI: 10.1080/0143116031000086835
    [19]
    MARINONI O. Improving geological models using a combined ordinary–indicator Kriging approach[J]. Engineering Geology,2003,69(1/2):37−45.
    [20]
    KRIGE D G. A statistical approach to some basic mine valuation problems on the Witwatersrand[J]. Journal of the Southern African Institute of Mining and Metallurgy,1951,52(6):119−139.
    [21]
    李金洁,王爱慧. 基于西南地区台站降雨资料空间插值方法的比较[J]. 气候与环境研究,2019,24(1):50−60. LI Jinjie,WANG Aihui. Comparison of spatial interpolation methods based on monthly precipitation observation data of station in Southwest China[J]. Climatic and Environmental Research,2019,24(1):50−60. DOI: 10.3878/j.issn.1006-9585.2017.17072

    LI Jinjie, WANG Aihui. Comparison of spatial interpolation methods based on monthly precipitation observation data of station in Southwest China[J]. Climatic and Environmental Research, 2019, 24(1): 50–60. DOI: 10.3878/j.issn.1006-9585.2017.17072
    [22]
    丁卉,余志,徐伟嘉,等. 3种区域空气质量空间插值方法对比研究[J]. 安全与环境学报,2016,16(3):309−315. DING Hui,YU Zhi,XU Weijia,et al. Comparative study of the three spatial interpolation methods for the regional air quality evaluation[J]. Journal of Safety and Environment,2016,16(3):309−315.

    DING Hui, YU Zhi, XU Weijia, et al. Comparative study of the three spatial interpolation methods for the regional air quality evaluation[J]. Journal of Safety and Environment, 2016, 16(3): 309–315.
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