动态地球磁层的时空剖分编码

动态地球磁层时空剖分模型借鉴了广泛应用于地学大数据的地球格网模型思想，实现了面向地球磁层的动态、非规则化物理空间的多层级剖分，且剖分格网形变稳定，可以实现一定范围内地球磁层时空特性的形式化。在此基础上，对剖分得到的时空格网进行编码表达，以便于计算机存储和处理，这是构建地球磁层大规模观测数据统一管理基础时空框架的另一个关键问题。由于地球磁层特殊的时空特性，经典、单一思路的地学编码方案较难完整地反映格网间的时空关系，因此难以支持格网间的基础时空关系计算。本文融合了整数坐标编码与Morton曲线编码的基本思路，实现了对漂移壳剖分格网的高效编码方案设计，完成了动态地球磁层的时空框架构建，从而为地磁数据的高效组织和处理奠定了基础。实验证明，本文提出的编码方案编码效率较高，且可以支持高效的相邻关系计算，为动态地球磁层多源、多层级、异构的大规模观测数据的组织与计算提供了一种解决方案。 

Research on the Spatio-temporal Coding Scheme for the Dynamic Earth’s Magnetosphere

The spatio-temporal subdivision model for the dynamic Earth’s magnetosphere is proposed to realize the multi-layered subdivision for the dynamic and irregular physical space of the magnetosphere drawing on the idea of the global discrete grids for the geoscience data. The distortion of the grids can be convergent within a certain number of subdivision times. Therefore, the subdivision model can be taken as a formalization for the spatio-temporal characteristics of the Earth’s magnetosphere in a certain range. On this basis, encoding and expressing the spatio-temporal grids obtained by subdivision for computer storage and processing is another key problem in constructing the basic spatio-temporal framework for the organization and management of large-scale observation data of the Earth’s magnetosphere. Due to the specific spatio-temporal characteristics, the classical geoscience coding schemes have trouble in fully reflecting the spatio-temporal relations between the grids. Therefore, it is difficult to support the basic spatio-temporal computing between the grids. In this paper, an efficient coding scheme is designed for the drift shell subdivision grids combining the basic ideas of the integral coordinate coding and the Morton coding schemes. On this basis, the spatio-temporal framework for the dynamic Earth’s magnetosphere is constructed which lays the foundation for the efficient organization and processing of geomagnetic data. The experiments carried out show that, the coding efficiency of the scheme is relatively high and it can support efficient neighbor relationship calculation. It provides a solution for the organization and computing of the multi-source, multi-layered, heterogeneous and large-scale observation data of the dynamic Earth’s magnetosphere.