Earth Science Frontiers ›› 2025, Vol. 32 ›› Issue (4): 182-198.DOI: 10.13745/j.esf.sf.2025.4.72

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Advance of 3D smart geological modeling

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

  1. 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

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

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