Ionospheric Anomaly Identification: Based on GNSS-TEC Data Fusion Supported by Three-Dimensional Spherical Voxel Visualization

Ionospheric tomography, an effective method for reconstructing 3-D electron density, is traditionally pictured by 3-D IED (ionospheric electron density) slices to express ionospheric disturbances, which may overlook the critical information in 3-D spherical manifold space. Here, we develop a novel visualization framework that integrates tomography reconstruction with a spherical latitude–longitude grid system, enabling the comprehensive characterization of 3-D IED dynamic evolution in 3-D manifold spherical space. Through this method, we visualized two cases: the Hualien earthquake on 2 April 2024 and the geomagnetic storm on 24 April 2023. The results demonstrate the evolution of the electron density during earthquake and geomagnetic storms in the real 3-D space, showing that seismic events induce bottom-up IED negative anomalies localized near epicenters, while geomagnetic storms trigger top-down depletion processes, with IED propagating from higher altitudes in the real 3-D manifold space. Compared to the conventional slice, our visualization model can visualize the characteristics, with the coverage area being observed to increase with the altitude within the same geospatial coordinates. This framework can advance the identification of ionosphere anomalies by enabling the precise differentiation of anomaly sources. This work bridges gaps in geospatial modeling by harmonizing ionospheric tomography with Earth system grids, offering a feasible solution for analyzing multi-scale ionospheric phenomena.

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Publication Details

Journal
Atmosphere
Published
2025-04-06
DOI
https://doi.org/10.3390/atmos16040428
Primary Topic
Earthquake Detection and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Ionospheric Anomaly Identification: Based on GNSS-TEC Data Fusion Supported by Three-Dimensional Spherical Voxel Visualization

Busheng Xie, Biyan Chen, Boqi Peng, Lixin Wu
Atmosphere
Earthquake Detection and Analysis
article

Ionospheric Anomaly Identification: Based on GNSS-TEC Data Fusion Supported by Three-Dimensional Spherical Voxel Visualization

Busheng Xie, Biyan Chen, Boqi Peng, Lixin Wu
article en

Abstract

Ionospheric tomography, an effective method for reconstructing 3-D electron density, is traditionally pictured by 3-D IED (ionospheric electron density) slices to express ionospheric disturbances, which may overlook the critical information in 3-D spherical manifold space. Here, we develop a novel visualization framework that integrates tomography reconstruction with a spherical latitude–longitude grid system, enabling the comprehensive characterization of 3-D IED dynamic evolution in 3-D manifold spherical space. Through this method, we visualized two cases: the Hualien earthquake on 2 April 2024 and the geomagnetic storm on 24 April 2023. The results demonstrate the evolution of the electron density during earthquake and geomagnetic storms in the real 3-D space, showing that seismic events induce bottom-up IED negative anomalies localized near epicenters, while geomagnetic storms trigger top-down depletion processes, with IED propagating from higher altitudes in the real 3-D manifold space. Compared to the conventional slice, our visualization model can visualize the characteristics, with the coverage area being observed to increase with the altitude within the same geospatial coordinates. This framework can advance the identification of ionosphere anomalies by enabling the precise differentiation of anomaly sources. This work bridges gaps in geospatial modeling by harmonizing ionospheric tomography with Earth system grids, offering a feasible solution for analyzing multi-scale ionospheric phenomena.

AtmosphereVol. 16(4)
Central South University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hainan Province, National Key Research and Development Program of China
Openalex Percentile: Top 90%
Earthquake Detection and Analysis
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Ionospheric Anomaly Identification: Based on GNSS-TEC Data Fusion Supported by Three-Dimensional Spherical Voxel Visualization — Busheng Xie, Biyan Chen, et al. · Atmosphere (2025) | TGRS Research Map | TGRS