Abstract The visualization and analysis of massive, multi-source, multi-dimensional, heterogeneous, dynamic, and complex spatial data is playing an increasingly important role in 3D scene and geological modeling. There are great differences in various research fields regarding data acquisition techniques, morphological descriptions, and application purposes of spatial objects. Therefore, based on an existing three-dimensional visualization technology and analysis program of spatial data, an integrated processing method for the effective fusion and analysis of multi-source complex spatial data is proposed. This method combines a massive point cloud model with a digital elevation model, in addition to combining the complex stratum with fault modeling technology. The structural grid and geological grid are effectively combined to realize the unified modeling and visualization of natural and artificial objects. This method was then applied to a 3D modeling scene of deeply buried mines and a geological construction model for open pit mines. The comprehensive and effective utilization of multi-source geological data is realized by using this modeling method. Finally, the challenges of spatial data visualization from the aspects of the difficulties in spatial data acquisition, the complexities of the spatial structure, the constraint relations of geological bodies, and the complexity of the 3D modeling process are discussed in this study. Three specific suggestions are proposed. This study enhances the application efficiency of multi-source spatial data modeling technologies already in practice and provides reference for future applications and developments.

中文翻译：

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使用集成的多源空间数据进行 3D 场景和地质建模：方法、挑战和建议

摘要 海量、多源、多维、异构、动态、复杂空间数据的可视化和分析在3D场景和地质建模中发挥着越来越重要的作用。各个研究领域在空间对象的数据采集技术、形态描述和应用目的等方面存在很大差异。因此，基于现有的空间数据三维可视化技术和分析程序，提出了一种多源复杂空间数据的有效融合与分析的综合处理方法。该方法将海量点云模型与数字高程模型相结合，同时将复杂地层与断层建模技术相结合。结构网格与地质网格有效结合，实现自然与人工对象的统一建模与可视化。然后将该方法应用于深埋矿山的3D建模场景和露天矿山的地质施工模型。利用该建模方法实现了多源地质数据的综合有效利用。最后，本文从空间数据获取的难点、空间结构的复杂性、地质体的约束关系、3D建模过程的复杂性等方面探讨了空间数据可视化面临的挑战。提出三点具体建议。