基于两种插值算法的三维地质建模对比

doi:10.13278/j.cnki.jjuese.20180250

吉林大学学报(地球科学版) ›› 2019, Vol. 49 ›› Issue (4): 1200-1208.doi: 10.13278/j.cnki.jjuese.20180250

• 地球探测与信息技术 • 上一篇

基于两种插值算法的三维地质建模对比

冯波¹, 陈明涛¹, 岳冬冬², 李胜涛^1,2, 贾小丰², 宋丹¹

1. 地下水资源与环境教育部重点实验室(吉林大学), 长春 130021;
2. 中国地质调查局水文地质环境地质调查中心, 河北保定 071051

收稿日期:2018-09-27 出版日期:2019-07-26 发布日期:2019-07-26
通讯作者: 李胜涛(1982-),男,高级工程师,博士研究生,主要从事水工环地质调查与热储工程研究,E-mail:lishengtao@chegs.cn E-mail:lishengtao@chegs.cn
作者简介:冯波(1982-),男,副教授,主要从事干热岩储层改造及资源评价方面的研究,E-mail:fengbo82@126.com
基金资助:
国家重点研发计划项目（2018YFB1501802）；吉林省与吉林大学共建项目（SXGJSF2017-5）；中国地质调查局项目（DD20179621）；吉林省教育厅项目（JJKH20170807KJ）

Comparison of 3D Geological Modeling Based on Two Different Interpolation Methods

Feng Bo¹, Chen Mingtao¹, Yue Dongdong², Li Shengtao^1,2, Jia Xiaofeng², Song Dan¹

1. Key Laboratory of Groundwater Resources and Environment(Jilin University), Ministry of Education, Changchun 130021, China;
2. Center for Hydrogeology and Environmental Geology, China Geological Survey, Baoding 071051, Hebei, China

Received:2018-09-27 Online:2019-07-26 Published:2019-07-26
Supported by:
Supported by National Key Research and Development Plan Project (2018YFB1501802), Joint Project Between Jilin Province and Jilin University (SXGJSF2017-5), Project of China Geological Survey (DD20179621) and Project of Jilin Provincial Department of Education (JJKH20170807KJ)

摘要/Abstract

摘要： 三维地质建模过程中，插值算法对模型准确性具有显著影响。为评价不同插值算法对三维建模准确性的影响，本文选取反距离权重插值法和自然邻域插值法开展对比研究。通过理论分析和案例研究，从统计学原理、插值误差和可视化效果等三方面进行了对比分析。结果表明：反距离权重插值法在建模中精度较高，适应面更广。与自然邻域插值法相比，反距离权重插值法更加适用于地层缺失严重的层位，能够更好地保留地层缺失的特征；同时，反距离权重插值法能够更好地处理断层构造，对于地层的错断起伏情况表现效果更好；反距离权重插值法在沉积地层中误差更小，与实际情况更接近。

关键词: 三维地质建模, GMS, 反距离权重插值法, 自然邻域插值法

Abstract: In the process of 3D geological modeling, interpolation algorithm has a significant influence on the accuracy of the model. In order to evaluate the influence of different interpolation algorithms on 3D modeling, the inverse distance weight interpolation and the natural neighborhood interpolation methods were selected to make a comparative study. Through theoretical analyses and case studies, these two methods were analyzed and compared in aspects of statistics, interpolation errors,and visualization. The results show that the inverse distance weight interpolation method has higher accuracy and wider adaptation area in modeling. Compared to the natural neighborhood interpolation method, the inverse distance weight interpolation method is, at first, more applicable to the area where some strata lack seriously, and can better retain characteristics of the lacked strata. Next,the method is able to handle fault structures and perform better for the fault fluctuation of strata. Finally, its result has a smaller error in sedimentary strata, and is closer to the actual situation.

Key words: 3D geological modeling, groundwater modeling system, the inverse distance weight interpolation method, natural neighborhood interpolation method

中图分类号:

P628+.3

冯波, 陈明涛, 岳冬冬, 李胜涛, 贾小丰, 宋丹. 基于两种插值算法的三维地质建模对比[J]. 吉林大学学报(地球科学版), 2019, 49(4): 1200-1208.

Feng Bo, Chen Mingtao, Yue Dongdong, Li Shengtao, Jia Xiaofeng, Song Dan. Comparison of 3D Geological Modeling Based on Two Different Interpolation Methods[J]. Journal of Jilin University(Earth Science Edition), 2019, 49(4): 1200-1208.

参考文献

[1] Chen Guoxing, Zhu Jiao, Qiang Mengyun, et al. Three-Dimensional Site Characterization with Borehole Data:A Case Study of Suzhou Area[J]. Engineering Geology, 2018, 234:65-82.
[2] He Zhenwen, Wu Chonglong, Cheng Wan, et al. Progressive Simplification of General Meshes in Geological Modeling[C]//The 12th Conference of the International Association for Mathematical Geology. Beijing:IMAG, 2007:738-742.
[3] Hou H, Sun Z, Li M, et al. Three-Dimensional Geological Modeling in Mining Area and Its Geomechanical Applications[C]//22nd International Conference on Geoinformatics. Kaohsiung:Institute of Electrical and Electronics Engineers Inc, 2014:1-5.
[4] Gonçalves Í G, Kumaira S, Guadagnin F. A Machine Learning Approach to the Potential:Field Method for Implicit Modeling of Geological Structures[J]. Computers & Geosciences, 2017, 103:173-182.
[5] Arfaoui M, Inoubli M H. Advantages of Using the Kriging Interpolator to Estimate the Gravity Surface, Comparison and Spatial Variability of Gravity Data in the El Kef-Ouargha Region (Northern Tunisia)[J]. Arabian Journal of Geosciences, 2013, 6(8):3139-3147.
[6] Xiong Z, Ce Y. Study on the 3D Engineering-Geological Modeling and Visualization System[C]//Computer Science and Software Engineering. Wuhan:IEEE Computer Society, 2008:931-934.
[7] Hu J, Sun C. 3D Geological Modeling Based on Boreholes Data and Application in Underground Engineering[C]//Information Science and Engineering. Shanghai:IEEE Computer Society, 2011:409-411.
[8] Zhao Z, Hou E, Zhang Z, et al. Three Dimensional Geological Modeling from Component-Based Topological Data Model[C]//Computer Modeling and Simulation. Macau:IEEE Computer Society, 2009:43-46.
[9] Bilibin S I, Perepechkin M V, Kovalevskiy Ye V. Hydrocarbon Reservoir Modeling for Use in Hydrocarbon Reserves Estimation in DV-Geo[J]. Lithologic Reservoirs, 2010, 22(F07):90-92.
[10] Qu H, Li J, Pan M. Three-Dimensional Urban Geological Modeling and Its Applications[C]//The 18th International Conference on Geoinformatics:GIScience in Change. Beijing:IEEE Computer Society, 2010:1-6.
[11] Liu J B, Dai H Y, Wang X, et al. Three-Dimensional Geological Modeling of Mining Subsidence Prediction Based on the Blocks[J]. Advanced Materials Research, 2014, 905:697-701.
[12] Li C L, Zhang J H, Li H K, et al. Application of New Geological Modeling Technology in Secondary Development in Daqing Oil Field[C]//IOP Conference Series:Earth and Environmental Science. Beijing:Institute of Physics Publishing, 2016:1-7.
[13] Wu Q, Xu H. Three-Dimensional Geological Modeling and Its Application in Digital Mine[J]. Science China Earth Sciences, 2014, 57(3):491-502.
[14] Saffarzadeh S, Javaherian A, Hasani H, et al. Improving Fault Image by Determination of Optimum Seismic Survey Parameters Using Ray-Based Modeling[J]. Journal of Geophysics and Engineering, 2018, 15(3):668-680.
[15] Li X T, Lin Q F, Lin C S. Application of Geological Modeling in Carbonate Reservoirs[J]. Advanced Materials Research, 2012, 446/447/448/449:2033-2040.
[16] 陈鹏,邓飞,刘思廷. 三维空间属性插值方法的研究[J]. 电脑知识与技术,2015,11(7):235-239. Chen Peng, Deng Fei, Liu Siting.Research on 3D Spatial Property Interpolation Method[J]. Computer Knowledge and Technology, 2015,11(7):235-239.
[17] 孔龙星,汤晓安,张俊达,等. 顾及局部特性的自适应3D矢量场反距离权重插值法[J]. 系统工程与电子技术, 2016,38(7):1697-1702. Kong Longxing, Tang Xiaoan, Zhang Junda, et al. Adaptive 3D Vector Field Inverse Distance Weight Interpolation Method Considering Local Characteristics[J]. Systems Engineering and Electronics, 2016, 38(7):1697-1702.
[18] 刘光孟,汪云甲,王允. 反距离权重插值因子对插值误差影响分析[J]. 中国科技论文在线,2010,5(11):879-884. Liu Guangmeng, Wang Yunjia, Wang Yun. Analysis of the Influence of Interpolation Factor of Anti-Distance Weight on Interpolation Error[J]. China Science and Technology Paper Online, 2010, 5(11):879-884.
[19] 郭艳军,潘懋,燕飞,等. 自然邻点插值方法在三维地质建模中的应用[J]. 解放军理工大学学报(自然科学版),2009,10(6):650-655. Guo Yanjun, Pan Mao, Yan Fei, et al.Application of Natural Neighbor Interpolation Method in 3D Geological Modeling[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2009, 10(6):650-655.
[20] 王三军,胡现辉. 稀少监测点沉降量插值方法选取研究[J]. 北京测绘,2017(3):71-74. Wang Sanjun, Hu Xianhui. Research on Selection of Interpolation Methods for Sparse Monitoring Points Settlement[J]. Beijing Surveying and Mapping, 2017(3):71-74.
[21] Braun J, Sambridge M. A Numerical Method for Solving Partial Differential Equations on Highly Irregular Evolving Grids[J]. Nature, 1995, 376:655-660.
[22] 刚什婷,邓英尔. 基于GMS在地下水资源评价与管理中的应用综述[J]. 地下水,2015,37(2):33-36. Gang Shiting, Deng Ying'er. Review of the Application of GMS in the Evaluation and Management of Groundwater Resources[J]. Groundwater, 2015, 37(2):33-36.
[23] 王福刚,梁秀娟,于军. 可视化地层模型信息系统在地面沉降研究中的应用[J]. 岩土工程学报,2005,27(2):219-223. Wang Fugang, Liang Xiujuan, Yu Jun.Application of Visual Stratigraphic Model Information System in Land Subsidence Research[J]. Chinese Journal of Geotechnical Engineering, 2005, 27(2):219-223.
[24] 谭家华,雷宏武. 基于GMS的三维TOUGH2模型及模拟[J]. 吉林大学学报(地球科学版),2017,47(4):1229-1235. Tan Jiahua, Lei Hongwu. Three-Dimensional TOUGH2 Model and Simulation Based on GMS[J]. Journal of Jilin University (Earth Science Edition), 2017, 47(4):1229-1235.
[25] 李琴,叶水根,高盼. 基于GMS的北京市房山平原区地下水数值模拟研究[J]. 中国农村水利水电,2012(5):1-5. Li Qin, Ye Shuigen, Gao Pan. Numerical Simulation of Groundwater in Fangshan Plain Area of Beijing Based on GMS[J]. China Rural Water and Hydropower, 2012(5):1-5.

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基于两种插值算法的三维地质建模对比

Comparison of 3D Geological Modeling Based on Two Different Interpolation Methods

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