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<p>As intelligent coal mining continues to advance, the requirements for accurate geological formation detection are increasing, leading to a growing demand for the efficient storage and retrieval of geophysical big data. In this study, we propose a multi-scale spatiotemporal classification encoding (MSTC) method to address the current challenges associated with data fragmentation, heterogeneity, and multi-scale spatiotemporal complexity. MSTC unifies spatial, temporal, and classification attributes into a compact indexing framework, enhancing data storage and query efficiency. Specifically, MSTC includes spatial encoding based on the Hilbert space-filling curve, flexible temporal encoding with multi-scale temporal segmentation, and optimized taxonomic encoding of operational data through a hierarchical classification structure. In experiments, MSTC significantly outperformed the traditional approach in a variety of database systems and demonstrated strong advantages in index construction time, storage overhead, and query efficiency. Additionally, MSTC was successfully applied to a coal mine geohazard transparency platform that provides strong technical support for intelligent coal mining and geohazard monitoring. This study lays a solid foundation for efficient management and transparent decision-making with respect to geophysical big data.</p>

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MSTC: a unified encoding method for geophysical big data retrieval

  • 期刊论文

  • 发表日期：

    2025年6月6日

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  • Henggao Geng,
  • Suping Peng,
  • Tao He,
  • Na Xu,
  • Zhangang Wang,
  • Xianlei Xu,
  • Xiaoqin Cui,
  • Wenfeng Du,
  • Yang Li,
  • Kunheng Li

摘要

As intelligent coal mining continues to advance, the requirements for accurate geological formation detection are increasing, leading to a growing demand for the efficient storage and retrieval of geophysical big data. In this study, we propose a multi-scale spatiotemporal classification encoding (MSTC) method to address the current challenges associated with data fragmentation, heterogeneity, and multi-scale spatiotemporal complexity. MSTC unifies spatial, temporal, and classification attributes into a compact indexing framework, enhancing data storage and query efficiency. Specifically, MSTC includes spatial encoding based on the Hilbert space-filling curve, flexible temporal encoding with multi-scale temporal segmentation, and optimized taxonomic encoding of operational data through a hierarchical classification structure. In experiments, MSTC significantly outperformed the traditional approach in a variety of database systems and demonstrated strong advantages in index construction time, storage overhead, and query efficiency. Additionally, MSTC was successfully applied to a coal mine geohazard transparency platform that provides strong technical support for intelligent coal mining and geohazard monitoring. This study lays a solid foundation for efficient management and transparent decision-making with respect to geophysical big data.