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<p>As a critical component of near-Earth space environment, the ionosphere exerts profound impacts on radio navigation and positioning systems, while playing a pivotal role in investigating the spatial physical coupling mechanisms among lithosphere, atmosphere, and thermosphere. Computerized ionospheric tomography (CIT) has established its central position in both theoretical exploration and practical applications through the reconstruction of three-dimensional (3D) spatiotemporal electron density distributions. Nevertheless, inherent challenges such as insufficient observational data and non-uniform signal path distribution contribute to the ill-posed nature of inversion processes, thereby constraining the accuracy and resolution of 3D ionospheric inversions. This paper presents a comprehensive retrospective analysis of the developmental trajectory of CIT technology, with particular emphasis on key innovations including enhanced inversion algorithms, multi-source data fusion and assimilation techniques, as well as machine learning integration. The study highlights the instrumental role of neural network-based machine learning and multi-source data convergence in addressing the ill-posed inversion problem, while pioneering an in-depth exploration of potential applications in geological hazard early warning systems and precision navigation positioning. Building upon current research advancements, this work proposes that multi-system data fusion coupled with deep learning methodologies will emerge as critical enablers for improving the accuracy, resolution, and robustness of ionospheric tomography. Furthermore, data-driven physical modeling is identified as a promising direction for future investigations. With continued technological evolution, CIT is anticipated to exert amplified influence in enhancing navigation services, elucidating spatial physical coupling mechanisms across lithospheric-atmospheric-thermospheric systems, and advancing geological disaster early warning frameworks.</p>

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An Important Monitoring Technology for Near-Earth Space Environment—Ionospheric Tomography: Evolution, Challenges, Application and Perspectives

  • 期刊论文

  • 发表日期：

    2025年6月18日

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  • Wang Li,
  • Fangsong Yang,
  • Xiaoqing Zuo,
  • Changyong He,
  • Dongsheng Zhao,
  • Kefei Zhang

摘要

As a critical component of near-Earth space environment, the ionosphere exerts profound impacts on radio navigation and positioning systems, while playing a pivotal role in investigating the spatial physical coupling mechanisms among lithosphere, atmosphere, and thermosphere. Computerized ionospheric tomography (CIT) has established its central position in both theoretical exploration and practical applications through the reconstruction of three-dimensional (3D) spatiotemporal electron density distributions. Nevertheless, inherent challenges such as insufficient observational data and non-uniform signal path distribution contribute to the ill-posed nature of inversion processes, thereby constraining the accuracy and resolution of 3D ionospheric inversions. This paper presents a comprehensive retrospective analysis of the developmental trajectory of CIT technology, with particular emphasis on key innovations including enhanced inversion algorithms, multi-source data fusion and assimilation techniques, as well as machine learning integration. The study highlights the instrumental role of neural network-based machine learning and multi-source data convergence in addressing the ill-posed inversion problem, while pioneering an in-depth exploration of potential applications in geological hazard early warning systems and precision navigation positioning. Building upon current research advancements, this work proposes that multi-system data fusion coupled with deep learning methodologies will emerge as critical enablers for improving the accuracy, resolution, and robustness of ionospheric tomography. Furthermore, data-driven physical modeling is identified as a promising direction for future investigations. With continued technological evolution, CIT is anticipated to exert amplified influence in enhancing navigation services, elucidating spatial physical coupling mechanisms across lithospheric-atmospheric-thermospheric systems, and advancing geological disaster early warning frameworks.