[1]张可军 赵 龙 罗永亮.一种基于泊松重建的三维地质面推演算法[J].高速铁路技术,2025,(02):15-21.[doi:10.12098/j.issn.1674-8247.2025.02.003]
　ZHANG Kejun ZHAO Long LUO Yongliang.An Algorithm for Three-dimensional Geological Surface Extrapolation Based on Poisson Reconstruction[J].HIGH SPEED RAILWAY TECHNOLOGY,2025,(02):15-21.[doi:10.12098/j.issn.1674-8247.2025.02.003]
点击复制

一种基于泊松重建的三维地质面推演算法()
分享到：

《高速铁路技术》[ISSN:1674-8247/CN:51-1730/U]

卷:
期数:
2025年02期
页码:
15-21
栏目:
理论探索
出版日期:
2025-04-20

文章信息/Info

Title:
An Algorithm for Three-dimensional Geological Surface Extrapolation Based on Poisson Reconstruction
文章编号:
1674-8247(2025)02-0015-07
作者:
张可军 赵 龙 罗永亮
(中铁二院工程集团有限责任公司, 成都 610031)
Author(s):
ZHANG Kejun ZHAO Long LUO Yongliang
(China Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031,China)
关键词:
三维地质建模;  泊松重建;  地质面推演;  点云数据处理
Keywords:
three-dimensional geological modeling;  Poisson reconstruction;  geological surface extrapolation;  point cloud data processing
分类号:
U212.4
DOI:
10.12098/j.issn.1674-8247.2025.02.003
文献标志码:
A
摘要:
三维地质面的生成是地质建模的关键,通常需要通过钻孔、物探、勘探等资料生成地质面,这不可避免地耗费大量人力和物力。本文提出了一种基于产状推演的三维地质面生成算法,在缺少钻孔和物探资料的情况下,也能准确生成三维地质面。该算法首先利用产状推算出地质模型的大致走向,生成大量点云及其法向量; 然后,利用泊松重建方法对点云进行全局重建,生成连续的三维地质表面。与传统的插值方法相比,泊松重建能够更好地处理不规则和稀疏的数据分布,提高模型的精度。研究结果表明,该算法在复杂地质条件下具有较高的适用性,可有效支撑铁路选线、地质勘探和资源评估等领域的应用。
Abstract:
The generation of three-dimensional geological surfaces is crucial for geological modeling and typically requires data from drilling, geophysical exploration, and surveys, which inevitably consumes substantial manpower and resources. This paper proposed a three-dimensional geological surface generation algorithm based on structural attitude extrapolation, capable of accurately generating three-dimensional geological surfaces even in the absence of drilling and geophysical data. The algorithm first utilized geological attitudes to estimate the general trend of the geological model, generating a large number of point clouds and their normal vectors. Then, it employed the Poisson reconstruction method for global reconstruction of the point clouds, producing continuous three-dimensional geological surfaces. Compared with traditional interpolation methods, Poisson reconstruction better handles irregular and sparse data distributions, thus enhancing the accuracy of the model. Study results demonstrate that the algorithm has high applicability under complex geological conditions and can effectively support applications in railway route selection, geological exploration, and resource evaluation.

参考文献/References:

[1] 郭福钟, 郑博文, 祁生文, 等. 三维地质建模技术与方法综述[J]. 工程地质学报, 2024, 32(3): 1143-1153.
GUO Fuzhong, ZHENG Bowen, QI Shengwen, et al. A Review of 3D Geological Modeling Technology and Methods[J]. Journal of Engineering Geology, 2024, 32(3): 1143-1153.
[2] 陈麒玉, 刘刚, 何珍文, 等. 面向地质大数据的结构-属性一体化三维地质建模技术现状与展望[J]. 地质科技通报, 2020, 39(4): 51-58.
CHEN Qiyu, LIU Gang, HE Zhenwen, et al. Current Situation and Prospect of Structure-attribute Integrated 3D Geological Modeling Technology for Geological Big Data[J]. Bulletin of Geological Science and Technology, 2020, 39(4): 51-58.
[3] 杨鹏程, 文卫兵, 王彦兵, 等. 基于遗传-克里金插值算法的岩土勘探工程三维地质建模[J]. 湘潭大学学报(自然科学版), 2021, 43(4): 85-92.
YANG Pengcheng, WEN Weibing, WANG Yanbing, et al. Three-dimensional Geological Modeling of Geotechnical Exploration Engineering Based on Genetic-Kriging Interpolation Algorithm[J]. Journal of Xiangtan University(Natural Science Edition), 2021, 43(4): 85-92.
[4] MALLET J L. Discrete Smooth Interpolation in Geometric Modelling[J]. Computer-Aided Design, 1992, 24(4): 178-191.
[5] 康帅, 季灵运, 焦其松, 等. 基于地基LiDAR点云数据插值方法的对比研究[J]. 大地测量与地球动力学, 2020, 40(4): 400-404.
KANG Shuai, JI Lingyun, JIAO Qisong, et al. Comparative Study of Point Cloud Data Interpolation Based on Ground-based LiDAR[J]. Journal of Geodesy and Geodynamics, 2020, 40(4): 400-404.
[6] 薛源, 胡丹, 白林, 等. 基于产状数据的三维岩层分界面模拟[J]. 中国科技论文, 2015, 10(3): 327-330.
XUE Yuan, HU Dan, BAI Lin, et al. Simulation on 3D Rock Stratum Interface Based on Orientation Data[J]. China Sciencepaper, 2015, 10(3): 327-330.
[7] 徐诗宇. 基于地质图的断层构造发育过程解析与时空建模[D]. 南京: 南京师范大学, 2021.
XU Shiyu. Analysis and Spatio-temporal Modeling of Fault Structure Development Process Based on Geological Map[D]. Nanjing: Nanjing Normal University, 2021.
[8] 黄北秀, 李丽慧, 薛媛, 等. 准噶尔盆地东北缘平地泉组岩体结构特征与三维模型构建[J]. 工程地质学报, 2024, 32(4): 1234-1248.
HUANG Beixiu, LI Lihui, XUE Yuan, et al. Rock Mass Structure Characteristics and 3D Modeling of the Pingdiquan Formation, Northeast Margin of the Jung-Gar Basin[J]. Journal of Engineering Geology, 2024, 32(4): 1234-1248.
[9] 孟令瀚, 崔光耀. 水平产状砂质板岩铁路隧道洞室成形爆破参数研究[J]. 高速铁路技术, 2019, 10(6): 21-25.
MENG Linghan, CUI Guangyao. Study on Blasting Parameters of Cavern Formation of Railway Tunnel in Horizontal Occurrence Sandy Slate[J]. High Speed Railway Technology, 2019, 10(6): 21-25.
[10] 乔金海, 潘懋, 金毅, 等. 基于DEM三维地层建模及一体化显示[J]. 地理与地理信息科学, 2011, 27(2): 34-37.
QIAO Jinhai, PAN Mao, JIN Yi, et al. 3D Strata Modeling and Integrative Visualization Based on DEM[J]. Geography and Geo-Information Science, 2011, 27(2): 34-37.
[11] 毕钲发. 人工智能三维地震解释与地质建模方法研究[D]. 合肥: 中国科学技术大学, 2023.
BI Zhengfa. Research on Artificial Intelligence Three-dimensional Seismic Interpretation and Geological Modeling Method[D]. Hefei: University of Science and Technology of China, 2023.
[12] 刘彦锋, 段太忠, 黄渊, 等. 沉积过程模拟驱动下的深度学习地质建模方法[J]. 石油与天然气地质, 2023, 44(1): 226-237.
LIU Yanfeng, DUAN Taizhong, HUANG Yuan, et al. Deep Learning-based Geological Modeling Driven by Sedimentary Process Simulation[J]. Oil & Gas Geology, 2023, 44(1): 226-237.
[13] 黄旭日, 代月, 徐云贵, 等. 基于深度学习算法不同数据集的地震反演实验[J]. 西南石油大学学报(自然科学版), 2020, 42(6): 16-25.
HUANG Xuri, DAI Yue, XU Yungui, et al. Seismic Inversion Experiments Based on Deep Learning Algorithm Using Different Datasets[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 16-25.
[14] 刘彦锋, 张文彪, 段太忠, 等. 深度学习油气藏地质建模研究进展[J]. 地质科技通报, 2021, 40(4): 235-241.
LIU Yanfeng, ZHANG Wenbiao, DUAN Taizhong, et al. Progress of Deep Learning in Oil and Gas Reservoir Geological Modeling[J]. Bulletin of Geological Science and Technology, 2021, 40(4): 235-241.
[15] 韦子文. 基于改进蚁群算法的路径规划算法研究[J]. 高速铁路技术, 2023, 14(5): 69-74.
WEI Ziwen. A Study on Path Planning Algorithm Based on Improved Ant Colony Algorithm[J]. High Speed Railway Technology, 2023, 14(5): 69-74.
[16] 张永亮, 朱美正, 李欣, 等. 基于稠密与稀疏高程点的DEM插值算法[J]. 计算机工程与应用, 2014, 50(1): 167-174.
ZHANG Yongliang, ZHU Meizheng, LI Xin, et al. Algorithms of DEM Interpolation Based on Dense and Sparse Elevation Points[J]. Computer Engineering and Applications, 2014, 50(1): 167-174.
[17] 陈锡超, 宁芊. 基于Kriging方法的动态地形反向多级细化研究[J]. 计算机工程与应用, 2013, 49(9): 171-175.
CHEN Xichao, NING Qian. Research on Kriging-based Multi-level Backward Refinement of Dynamic Terrain Modeling[J]. Computer Engineering and Applications, 2013, 49(9): 171-175.
[18] 刘昌军, 叶长锋, 赵雨, 等. 基于激光扫描技术的复杂工程挖填方计算[J]. 水电能源科学, 2011, 29(12): 95-97, 131.
LIU Changjun, YE Changfeng, ZHAO Yu, et al. Excavation and Embankment Volume Calculation for Complicated Engineering Based on Laser Scanning[J]. Water Resources and Power, 2011, 29(12): 95-97, 131.
[19] 李胜天, 徐贵兴. DEM构建及地形特征线提取研究[J]. 安徽地质, 2022, 32(4): 365-370.
LI Shengtian, XU Guixing. A Study on DEM Construction and Topographic Feature Line Extraction[J]. Geology of Anhui, 2022, 32(4): 365-370.
[20] 唐为峰, 高照祥, 田强, 等. 基于布料模拟滤波和点云泊松曲面重建技术的土方量计算方法研究[J]. 城市勘测, 2024(3): 128-133.
TANG Weifeng, GAO Zhaoxiang, TIAN Qiang, et al. Research on Earthwork Quantity Calculation Method Based on Cloth Simulation Filter and Point Cloud Poisson Surface Reconstruction Technology[J]. Urban Geotechnical Investigation & Surveying, 2024(3): 128-133.

备注/Memo

备注/Memo:
收稿日期:2024-10-22
作者简介:张可军(1967-),男,教授级高级工程师。
更新日期/Last Update: 2025-04-20