Global Ionospheric VTEC Data Completion Method Based on Aggregated Contextual-Transformation Generative Adversarial Nets

The determination of ionospheric total electron content (TEC) is crucial for various applications in space weather. However, due to the uneven distribution of stations, complex data processing and reconstruction algorithms are usually required to obtain a seamless global TEC map. To reduce the difficulty of this process, we show the possibility of reconstructing a high-resolution global TEC map based on the image inpainting method aggregated contextual-transformation generative adversarial nets (AOT-GANs). First, the AOT-GAN model is used to learn the data fitting process and TEC spatial distribution characteristics in the UQRG, and then the data completion performance and generalization ability of the model are verified under different data-missing conditions and geomagnetic activity levels. The verification results show that the model has relatively reliable completion results under different conditions. The average root mean squared error (RMSE) between the filled results and UQRG is mainly concentrated in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\sim 3$ </tex-math></inline-formula> TECU, and the average structural similarity index measure (SSIM) index is mainly concentrated in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.95\sim 0.97$ </tex-math></inline-formula>. Even during the geomagnetic storm periods and the land data missing rate exceeds 30%, more than 92% of the biases are still within ±5 TECU. In addition, the model can also achieve considerable results when completing CODG, with more than 63% of the biases within ±1 TECU and more than 92% of the biases within ±5 TECU. Finally, the Massachusetts Institute of Technology (MIT)-TEC map is filled using the trained model. When part of MIT-TEC is removed, the biases between the completed result and the original MIT-TEC are relatively small. For the ocean area, the completed MIT-TEC map has the lowest RMSE and the STD level is similar to the ESAG product. The completed MIT-TEC maps not only maintain the global structure of TEC but also have rich details.

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Publication Details

Journal
IEEE Transactions on Geoscience and Remote Sensing
Published
2024-12-12
DOI
https://doi.org/10.1109/tgrs.2024.3511083
Citations
2
Primary Topic
Advanced Computational Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.69

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article

Global Ionospheric VTEC Data Completion Method Based on Aggregated Contextual-Transformation Generative Adversarial Nets

Peng Chen, Xin Gao, Yibin Yao, Longlong Fu et al.
2 citations
IEEE Transactions on Geoscience and Remote Sensing
Advanced Computational Techniques and Applications
0.69
article

Global Ionospheric VTEC Data Completion Method Based on Aggregated Contextual-Transformation Generative Adversarial Nets

Peng Chen, Xin Gao, Yibin Yao, Longlong Fu, Rong Wang, Liang Zhang
article en
2 citations

Abstract

The determination of ionospheric total electron content (TEC) is crucial for various applications in space weather. However, due to the uneven distribution of stations, complex data processing and reconstruction algorithms are usually required to obtain a seamless global TEC map. To reduce the difficulty of this process, we show the possibility of reconstructing a high-resolution global TEC map based on the image inpainting method aggregated contextual-transformation generative adversarial nets (AOT-GANs). First, the AOT-GAN model is used to learn the data fitting process and TEC spatial distribution characteristics in the UQRG, and then the data completion performance and generalization ability of the model are verified under different data-missing conditions and geomagnetic activity levels. The verification results show that the model has relatively reliable completion results under different conditions. The average root mean squared error (RMSE) between the filled results and UQRG is mainly concentrated in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\sim 3$ </tex-math></inline-formula> TECU, and the average structural similarity index measure (SSIM) index is mainly concentrated in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.95\sim 0.97$ </tex-math></inline-formula>. Even during the geomagnetic storm periods and the land data missing rate exceeds 30%, more than 92% of the biases are still within ±5 TECU. In addition, the model can also achieve considerable results when completing CODG, with more than 63% of the biases within ±1 TECU and more than 92% of the biases within ±5 TECU. Finally, the Massachusetts Institute of Technology (MIT)-TEC map is filled using the trained model. When part of MIT-TEC is removed, the biases between the completed result and the original MIT-TEC are relatively small. For the ocean area, the completed MIT-TEC map has the lowest RMSE and the STD level is similar to the ESAG product. The completed MIT-TEC maps not only maintain the global structure of TEC but also have rich details.

IEEE Transactions on Geoscience and Remote SensingVol. 63
Xi'an University of Science and Technology (CN), Wuhan University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hubei Province, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 23%
Advanced Computational Techniques and Applications
0.69
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