Abstract. Relying on geological data to construct 3D models can provide a more intuitive and easily comprehensible spatial perspective. This process aids in exploring underground spatial structures and geological evolutionary processes, providing essential data and assistance for the exploration of geological resources, energy development, engineering decision-making, and various other applications. As one of the methods for 3D geological modeling, multipoint statistics can effectively describe and reconstruct the intricate geometric shapes of nonlinear geological bodies. However, existing multipoint statistics algorithms still face challenges in efficiently extracting and reconstructing the global spatial distribution characteristics of geological objects. Moreover, they lack a data-driven modeling framework that integrates diverse sources of heterogeneous data. This research introduces a novel approach that combines multipoint statistics with multimodal deep artificial neural networks and constructs the 3D crustal P-wave velocity structure model of the South China Sea by using 44 OBS forward profiles, gravity anomalies, magnetic anomalies and topographic relief data. The experimental results demonstrate that the new approach surpasses multipoint statistics and Kriging interpolation methods, and can generate a more accurate 3D geological model through the integration of multiple geophysical data. Furthermore, the reliability of the 3D crustal P-wave velocity structure model, established using the novel method, was corroborated through visual and statistical analyses. This model intuitively delineates the spatial distribution characteristics of the crustal velocity structure in the South China Sea, thereby offering a foundational data basis for researchers to gain a more comprehensive understanding of the geological evolution process within this region.

中文翻译：

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基于多模态深度学习和多点统计的多源异构数据融合3D地理建模框架：以南海为例

摘要。依靠地质数据构建3D模型可以提供更直观、更容易理解的空间视角。该过程有助于探索地下空间结构和地质演化过程，为地质资源勘探、能源开发、工程决策等各种应用提供必要的数据和帮助。多点统计作为三维地质建模方法之一，可以有效地描述和重建非线性地质体的复杂几何形状。然而，现有的多点统计算法在有效提取和重建地质物体的全局空间分布特征方面仍然面临挑战。此外，它们缺乏集成不同来源的异构数据的数据驱动建模框架。本研究引入了一种将多点统计与多模态深度人工神经网络相结合的新方法，利用44个OBS正演剖面、重力异常、磁异常和地形起伏数据构建了南海3D地壳纵波速度结构模型。实验结果表明，新方法超越了多点统计和克里格插值方法，可以通过整合多个地球物理数据生成更准确的3D地质模型。此外，通过视觉和统计分析证实了使用新方法建立的三维地壳纵波速度结构模型的可靠性。该模型直观地刻画了南海地壳速度结构的空间分布特征，为研究人员更全面地了解该区域的地质演化过程提供了基础数据基础。