数据与计算发展前沿 ›› 2025, Vol. 7 ›› Issue (4): 79-88.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.04.007

doi: 10.11871/jfdc.issn.2096-742X.2025.04.007

• 专刊：空间科学大数据智能算法模型与工具 • 上一篇    下一篇

基于直接体可视化方法提取磁层顶结构

钟佳1,2(),邹自明1,2,*()   

  1. 1.中国科学院国家空间科学中心， 北京 100190
    2.国家空间科学数据中心， 北京 101407
  • 出版日期:2025-08-20 发布日期:2025-08-21
  • 通讯作者: 邹自明
  • 作者简介:钟佳，中国科学院国家空间科学中心，高级工程师，主要研究方向为空间科学数据挖掘与机器学习建模、科学可视化技术。
    负责本文实验开展与论文初稿撰写。
    ZHONG Jia is a senior engineer at the National Space Science Center, Chinese Academy of Sciences. His main research interests include space science data mining and machine learning modeling techniques, as well as scientific visualization technologies.
    In this paper, he is responsible for conducting the experiments and drafting the initial version of the manuscript.
    E-mail: zhongjia@nssc.ac.cn|邹自明，中国科学院国家空间科学中心研究员，国家空间科学数据中心主任，中国科学院大学博士生导师，长期从事空间科学与数据科学交叉领域研究，在科学数据治理理论、标准研制、空间信息组织与互操作、日地空间大数据系统工程、空间天气领域数据挖掘与知识发现等方面开展研究。
    负责本文实验与论文指导。
    ZOU Ziming is a professor at the National Space Science Center, Chinese Academy of Sciences; the Director of the National Space Science Data Center; nd a doctoral supervisor at the University of Chinese Academy of Sciences. He has extensive expertise in interdisciplinary research spanning space science and data science. His work focuses on scientific data governance theory, standards development, spatial information organization and interoperability, big data systems engineering for solar-terrestrial space, as well as data mining and knowledge discovery in the field of space weather.
    In this paper, he is responsible for supervising the experiments and the manuscript preparation.
    E-mail: mzou@nssc.ac.cn
  • 基金资助:
    国家重点研发计划(2022YFF0711400);中国科学院 “十四五” 网络安全和信息化规划项目(CAS-WX2022SDC-XK15);中国科学院 “十四五” 网络安全和信息化规划项目(CAS-WX2022SF-0103)

Extracting the Magnetopause Structure Based on Direct Volume Visualization Methods

ZHONG Jia1,2(),ZOU Ziming1,2,*()   

  1. 1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
    2. National Space Science Data Center, Beijing 101407, China
  • Online:2025-08-20 Published:2025-08-21
  • Contact: ZOU Ziming

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摘要：

【目的】可视化是对磁层顶位形结构进行有效分析的重要手段，对于理解太阳风和磁层能量交换机制具有重要意义。【方法】基于几何绘制的太阳风流线法可视化磁层顶时需定位边界点并连接三角片面，不能透视磁层顶内部结构。本文尝试采用无需定位边界点的直接体可视化方法透视磁层顶结构，基于数据点区间占比分布图和k-means++算法设计传递函数。【结果】利用PPMLR-MHD模拟数据的等离子体数密度梯度、磁感应强度梯度等5种特征进行实验，获得了完整的三维磁层顶结构和弓激波结构，其中，基于太阳风数密度梯度和磁感应强度梯度可视化磁层顶结构更清晰。【结论】实验表明本文提出的磁层顶直接体可视化方法相比于几何绘制方法具有更强大的信息和空间表现力，对于后续可视化磁层内其他结构具有积极借鉴意义。

关键词: 三维磁层顶, 直接体可视化, 传递函数, PPMLR-MHD模拟数据, 太阳风流线

Abstract:

[Objective] Visualization is an important way to effectively analyze the magnetosphere configuration, and it is of great significance to the understanding of the energy exchange mechanism between solar wind and magnetosphere. [Methods] The boundary points should be located and then triangular facets are constructed before drawing the three-dimensional magnetopause based on the streamline method while the interior structure of the magnetopause is invisible. This paper attempts to introduce a direct volume visualization method without locating boundary points to perspectively display the magnetopause structure, and designs transfer functions based on the value rate distribution map and k-means++ algorithm. [Results] The experiment utilized five features, including plasma density gradient, magnetic induction intensity gradient, and so on from PPMLR-MHD simulation data, and successfully reconstructed the complete three-dimensional structures of the magnetopause and bow shock. Among these, the visualization of the magnetopause based on solar wind density gradient and magnetic induction intensity gradient yields clearer results. [Conclusions] The results demonstrate that the magnetopause direct volume visualization method proposed in this study offers more robust information and spatial representation capabilities compared to geometric rendering approaches, providing valuable insights for subsequent visualization of other structures within the magnetosphere.

Key words: three dimensional magnetopause, direct volume visualization, transfer function, PPMLR-MHD simulation data, solar wind streamline