三维地质智能建模研究进展

doi:10.13745/j.esf.sf.2025.4.72

地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 182-198.DOI: 10.13745/j.esf.sf.2025.4.72

三维地质智能建模研究进展

叶舒婉¹(), 侯卫生¹^,²^,^*(), 杨玠³, 汪海城⁴, 白芸⁴, 王永志⁵

1.中山大学地球科学与工程学院, 广东珠海 519082
2.地质过程与矿产资源探查广东省重点实验室, 广东珠海 519082
3.中国地质大学(北京) 科学研究院, 北京 100083
4.中国地质调查局自然资源综合调查指挥中心, 北京 100055
5.吉林大学地球探测科学与技术学院, 吉林长春 130061

收稿日期:2025-02-20 修回日期:2025-05-10 出版日期:2025-07-25 发布日期:2025-08-04
通信作者: *侯卫生(1976—),男,教授,博士生导师,主要从事三维地质建模和全波形反演研究。E-mail: houwsh@mail.sysu.edu.cn
作者简介:叶舒婉(1998—),女,博士研究生,主要从事三维地质建模研究。E-mail: yeshw6@mail2.sysu.edu.cn
基金资助:
国家自然科学基金项目(42372341);国家自然科学基金项目(41972302);广东省“珠江人才计划”引进创新团队项目(2021ZT09H399)

Advance of 3D smart geological modeling

YE Shuwan¹(), HOU Weisheng¹^,²^,^*(), YANG Jie³, WANG Haicheng⁴, BAI Yun⁴, WANG Yongzhi⁵

1. School of Earth Sciences and Engineering, Sun Yat-sen University, Zhuhai 519082, China
2. Guangdong Provincial Key Lab of Geological Processes and Mineral Resources, Zhuhai 519082, China
3. Institute of Scientific Research, China University of Geosciences (Beijing), Beijing 100083, China
4. Command Center of Natural Resource Comprehensive Survey, China Geological Survey, Beijing 100055, China
5. College of Geoexploration Science and Technology, Jilin University, Changchun 130061, China

Received:2025-02-20 Revised:2025-05-10 Online:2025-07-25 Published:2025-08-04

摘要/Abstract

摘要：

高精度的三维地质建模是数字孪生技术快速发展的重要基础,为资源预测、工程规划和灾害防治等领域提供了关键支撑。传统三维地质建模方法多依靠人工交互,难以满足复杂地质环境下对精细结构表达和实时更新的需求。为突破这些局限,近年来引入的机器学习与深度学习为地质建模提供了新的智能化解决方案,有效提升了模型的自动化程度和复杂结构的表达能力。本文系统回顾了三维地质建模的发展历程,总结了半智能化、机器学习和深度学习三个发展阶段的技术特征;深入剖析了深度学习与不确定性分析、迁移学习、主成分分析及多点地质统计学等方法的融合方法。同时,针对现有方法在数据稀疏处理、计算复杂性、模型可解释性和实时更新能力方面存在的不足,提出未来的研究趋势与发展方向,包括多模态数据融合、地质知识嵌入、轻量化模型优化、不确定性量化和人工智能大语言模型等。随着智能化建模技术的不断进步,三维地质模型的精度、可靠性和适应性将持续提升,进一步推动地质领域的数字孪生技术应用与工程实践发展。

关键词: 三维地质建模, 数字孪生, 深度学习, 机器学习

Abstract:

High-precision 3D geological modeling serves as a crucial foundation for the rapid advancement of digital twin technology, providing essential support for resource prediction, engineering planning, and disaster prevention. Traditional 3D geological modeling methods often rely on manual interaction, struggling to meet the demands of precise structural representation and real-time updates in complex geological conditions. To overcome these limitations, the recent introduction of machine learning and deep learning approaches offers new intelligent solutions, significantly improving model automation and the representation of complex geological structures. This paper systematically reviews the development of 3D geological modeling, summarizing technical characteristics across three distinct stages: semi-intelligent modeling, machine learning-based modeling, and deep learning-based modeling. Furthermore, we comprehensively analyze the integrated applications of deep learning with uncertainty analysis, transfer learning, principal component analysis and multiple-point geostatistics. Considering existing challenges such as sparse data processing, computational complexity, model interpretability, and real-time updating capabilities, we propose future research trends, including multimodal data fusion, embedding of geological knowledge, lightweight model optimization, uncertainty quantification and Large Language Models. With ongoing progress in intelligent modeling techniques, the accuracy, reliability, and adaptability of 3D geological models are expected to continuously improve, further advancing the application and engineering practice of digital twin technology in geology.

Key words: 3D geological modeling, digital twin, deep learning, machine learning

中图分类号:

P628

叶舒婉, 侯卫生, 杨玠, 汪海城, 白芸, 王永志. 三维地质智能建模研究进展[J]. 地学前缘, 2025, 32(4): 182-198.

YE Shuwan, HOU Weisheng, YANG Jie, WANG Haicheng, BAI Yun, WANG Yongzhi. Advance of 3D smart geological modeling[J]. Earth Science Frontiers, 2025, 32(4): 182-198.

图/表 19

参考文献 65

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分类	插值方法	描述	优点	缺点
确定性插值	反距离权重插值	基于已知点到未知点的距离加权计算属性值,邻近点影响大	简单高效,适合小规模数据	忽略空间方向性和自相关性,易过渡平滑
	多样条插值	采用分段多项式函数拟合数据,并保持边界连续性	适用于连续分布数据,光滑性好,适应复杂边界	不适合非平稳数据,计算量大
	径向基函数插值	采用多维曲面拟合,构造最优插值函数,确保插值结果平滑	适用于非均匀分布数据,空间连续性强	对参数选择敏感,计算复杂
	离散光滑插值	通过最小化误差和光滑度调整插值值,保证整体平滑性和空间一致性	插值结果光滑且连续,可处理不规则几何形态	计算复杂度高,高异质性区域难以捕捉局部特征
统计插值	克里金插值	基于变异函数描述空间自相关性,进行最优线性无偏估计	考虑空间结构,支持误差估计	计算复杂,参数选择依赖用户经验;在大规模数据计算时计算量大

分类	插值方法	描述	优点	缺点
确定性插值	反距离权重插值	基于已知点到未知点的距离加权计算属性值,邻近点影响大	简单高效,适合小规模数据	忽略空间方向性和自相关性,易过渡平滑
	多样条插值	采用分段多项式函数拟合数据,并保持边界连续性	适用于连续分布数据,光滑性好,适应复杂边界	不适合非平稳数据,计算量大
	径向基函数插值	采用多维曲面拟合,构造最优插值函数,确保插值结果平滑	适用于非均匀分布数据,空间连续性强	对参数选择敏感,计算复杂
	离散光滑插值	通过最小化误差和光滑度调整插值值,保证整体平滑性和空间一致性	插值结果光滑且连续,可处理不规则几何形态	计算复杂度高,高异质性区域难以捕捉局部特征
统计插值	克里金插值	基于变异函数描述空间自相关性,进行最优线性无偏估计	考虑空间结构,支持误差估计	计算复杂,参数选择依赖用户经验;在大规模数据计算时计算量大

常见分类器	描述	优点	缺点
朴素贝叶斯	基于贝叶斯定理,假设特征条件独立,用于地质单元分类和初步建模	计算高效,参数简单,适合小样本数据	泛化能力弱,不适用于连续数据,对特征分布依赖性强
BP神经网络	基于多层前馈神经网络,建模非线性地质特征,适用于预测与分类任务	非线性拟合强,结构灵活	训练慢,参数敏感,易过拟合,计算效率低
随机森林	多决策树集成,适用于复杂地质属性建模和多源数据整合	非线性建模能力强,抗噪性好,可并行计算	模型复杂,超参数敏感,资源消耗大
XGBoost	基于梯度提升决策树,适用于处理复杂地质数据的非线性特征和稀疏性问题	计算高效,非线性建模能力强,适用于多源异构数据	参数调优困难,可解释性较低
支持向量机	通过核函数将数据映射到高维空间,实现地层预测和结构分割	适用于高维小样本,核函数灵活,鲁棒性和泛化强	计算复杂,参数调优困难,对不平衡数据敏感

常见分类器	描述	优点	缺点
朴素贝叶斯	基于贝叶斯定理,假设特征条件独立,用于地质单元分类和初步建模	计算高效,参数简单,适合小样本数据	泛化能力弱,不适用于连续数据,对特征分布依赖性强
BP神经网络	基于多层前馈神经网络,建模非线性地质特征,适用于预测与分类任务	非线性拟合强,结构灵活	训练慢,参数敏感,易过拟合,计算效率低
随机森林	多决策树集成,适用于复杂地质属性建模和多源数据整合	非线性建模能力强,抗噪性好,可并行计算	模型复杂,超参数敏感,资源消耗大
XGBoost	基于梯度提升决策树,适用于处理复杂地质数据的非线性特征和稀疏性问题	计算高效,非线性建模能力强,适用于多源异构数据	参数调优困难,可解释性较低
支持向量机	通过核函数将数据映射到高维空间,实现地层预测和结构分割	适用于高维小样本,核函数灵活,鲁棒性和泛化强	计算复杂,参数调优困难,对不平衡数据敏感

分类方法	类型	描述	优点	缺点
核心思想	图像学方法	以移动模板方式从训练图像提取空间模式,并粘贴至模拟网格,实现模型重构(如ANSIM、DISPAT、GOSIM等)	模板可调,直观表达局部模式	小模板丢失宏观特征,大模板降低随机性
核心思想	数据事件方法	依据待模拟点的概率分布函数进行随机模拟,从已知数据提取统计特征(如SNESIM、IMPALA、HOSIM等)	自动化高,适应复杂建模条件	难捕捉隐藏隐含特征,依赖数据驱动
模拟过程	序贯模拟方法	按预设路径逐点赋值,仅遍历网格一次(如SNESIM、SIMPAT、DS、HOSIM、CCSIM等)	模拟效率高,方法简单,易实现	误差逐步累积,难以捕捉复杂全局特征
模拟过程	迭代过程方法	以初始模型为基础,反复优化属性值,直至满足损失函数阈值或迭代条件(如GOSIM、PCTO-SIM等)	误差少,精度高,保留更多已知信息	依赖初始模型,计算成本高

三维地质智能建模研究进展

Advance of 3D smart geological modeling

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类别	方法	优点	缺点	适用场景
无条件建模	传统GAN方法	简单直接,可捕捉整体地质模式;再现多样性高的地质模式	模式崩塌风险大,高分辨率建模细节不足	简单地质分布建模
无条件建模	渐进式GAN方法	逐层训练,适配高分辨率,细节更清晰	训练复杂,需高算力	多尺度地质建模,高分辨率地质建模
有条件建模	模拟器输入向量搜寻的条件化方法	适合多约束条件,可结合梯度下降和MCMC(Markov Chain Monte Carlo)优化	条件变动需重新搜索,计算开销大	精确井控建模,数据稀疏场景
有条件建模	模拟器直接条件化方法	条件数据直接输入,操作便捷,建模效率高	模型结构复杂,对间接条件数据适应性差	快速建模,适用于复杂多源约束

方法	仅采用深度学习的方法	融合不确定分析和深度学习的方法
模型输出	单一确定性结果	输出预测结果及其置信度和不确定度
预测可信度表达	可通过后验概率或准确率等间接评估,但存在过度置信问题	量化每一预测的置信度和不确定度,能区分高可信与低可信预测
结果表达能力	难以判断模型在非平稳或异常区域的可信程度	根据不确定度识别模型效果,增强对地质异常区域的响应能力
对抗鲁棒性	易被扰动误导,错误预测置信度高	错误预测伴随高不确定度,增强安全性和鲁棒性
计算复杂度	较低	较高
适用环境	数据充足、结构清晰、分类明确的地质环境	数据稀缺、存在噪声或结构复杂的地质场景

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