As geological exploration technology advances, geoscience relies on digitization and intelligence to address challenges such as data fragmentation, multi-source heterogeneity, and visual analysis. This study develops a big data-driven 3D visual analysis system for regional-scale applications. The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. Experimental validation shows the improved CMMN algorithm reduces cloud task completion time by 2.37% while increasing resource utilization by 0.652% compared with conventional methods. The dynamic data model integrates 12 geological data types across eight categories through semantic mapping. Rendering optimizations achieve a 93.7% memory reduction and 60.6% faster visualization compared with baseline approaches. This system provides robust decision support and reliable tools for the digital transformation of geoscience work.

Multiscale 3D geological characterization and joint analysis are increasingly important topics in spatial information science. However, the non-uniform spatial distribution of objects and scale heterogeneity in geological surveys lead to dispersed storage, long access paths, and limited query performance in managing multiscale 3D geological model data. This study presents a W-Hilbert curve-based multiscale data model (WH-MSDM) that improves data indexing and management through a unified data structure (UDS) for multi-scale blocks and a bidirectional mapping model (BMM) linking spatial coordinates to memory locations. It supports spatial, attribute, hybrid, and cross-scale queries for diverse retrieval tasks. By exploiting the space-filling properties of the W-Hilbert curve to linearize multidimensional geological data into a one-dimensional index, it preserves locality and increases query efficiency across scales. Experimental results on a real 3D geological model demonstrate that WH-MSDM outperforms three mainstream baselines in both unified data organization and diverse query workloads. It thus provides a data-model foundation for Digital Earth-oriented multiscale geological analysis.