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<p>In the fields of 3D geological modeling and geoscience big data analysis, efficiently representing volumetric data characterized by massive scale, non-uniform spatial distribution, and complex geological features remains a core bottleneck constraining the performance of geophysical inversion, geostatistics, and spatial data analysis. To address the challenge of representing large-scale geological volumetric data, this paper proposes and implements a novel hierarchical sparse voxel data structure and its implementation framework—VoxOct. The core innovation of the VoxOct framework lies in its integration of the efficient indexing capability of sparse voxel grid with the flexibility of adaptive spatial partitioning offered by octrees, resulting in a hybrid sparse voxel octree (SVO) structure. VoxOct is a hybrid data structure based on octree spatial subdivision and N‑tree hierarchical compression, designed to achieve the representation, storage, and computation of large‑scale sparse geological volumetric data through the adaptive spatial division. In terms of design principles, VoxOct follows a topology‑attribute‑separated construction approach, which can drastically reduce storage space for invalid or redundant data through pruning optimization strategies. At the algorithmic level, VoxOct designs a set of efficient construction, dynamic traversal, and rapid update algorithms for large‑scale geological volumes. The framework implements an efficient sparse storage scheme and memory management strategy, supports native access to explicit octree nodes, and optimizes fast traversal and dynamic refinement algorithms for massive grids. Through an efficient sparse octree indexing mechanism, the framework enables high‑performance processing of billion‑scale voxel models on general‑purpose computing devices and facilitates dynamic updates to the octree grid model. Experimental results demonstrate that VoxOct reduces runtime memory access overhead compared with pointer-based octrees and requires less storage space than out-of-core octrees, which strikes a balance between the two for large and sparse geological volume data with high-resolution attribute distributions. Currently, the core algorithms of the framework have been integrated into commercial geological application software.</p>

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VoxOct: A Sparse Voxel Framework for Large-Scale Geological Volume Representation

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Deyun Zhong,
  • Xingliang Xu,
  • Zhaohao Wu,
  • Binglei Li,
  • Lin Bi,
  • Liguan Wang

摘要

In the fields of 3D geological modeling and geoscience big data analysis, efficiently representing volumetric data characterized by massive scale, non-uniform spatial distribution, and complex geological features remains a core bottleneck constraining the performance of geophysical inversion, geostatistics, and spatial data analysis. To address the challenge of representing large-scale geological volumetric data, this paper proposes and implements a novel hierarchical sparse voxel data structure and its implementation framework—VoxOct. The core innovation of the VoxOct framework lies in its integration of the efficient indexing capability of sparse voxel grid with the flexibility of adaptive spatial partitioning offered by octrees, resulting in a hybrid sparse voxel octree (SVO) structure. VoxOct is a hybrid data structure based on octree spatial subdivision and N‑tree hierarchical compression, designed to achieve the representation, storage, and computation of large‑scale sparse geological volumetric data through the adaptive spatial division. In terms of design principles, VoxOct follows a topology‑attribute‑separated construction approach, which can drastically reduce storage space for invalid or redundant data through pruning optimization strategies. At the algorithmic level, VoxOct designs a set of efficient construction, dynamic traversal, and rapid update algorithms for large‑scale geological volumes. The framework implements an efficient sparse storage scheme and memory management strategy, supports native access to explicit octree nodes, and optimizes fast traversal and dynamic refinement algorithms for massive grids. Through an efficient sparse octree indexing mechanism, the framework enables high‑performance processing of billion‑scale voxel models on general‑purpose computing devices and facilitates dynamic updates to the octree grid model. Experimental results demonstrate that VoxOct reduces runtime memory access overhead compared with pointer-based octrees and requires less storage space than out-of-core octrees, which strikes a balance between the two for large and sparse geological volume data with high-resolution attribute distributions. Currently, the core algorithms of the framework have been integrated into commercial geological application software.