Completion of Global Ionospheric TEC Maps Using a Deep Learning Approach
Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing IGS Global TEC maps using analysis algorithms. However, collecting the completed data is difficult because of the lack of ground receivers, an...
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| Veröffentlicht in: | Journal of geophysical research. Space physics Jg. 127; H. 5 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Blackwell Publishing Ltd
01.05.2022
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| ISSN: | 2169-9380, 2169-9402 |
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| Abstract | Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing IGS Global TEC maps using analysis algorithms. However, collecting the completed data is difficult because of the lack of ground receivers, and the processing to obtain the completed IGS TEC maps is time‐consuming. The fast development of deep learning brings an effective way to solve these problems. Among the various deep learning methods, the generative adversarial network (GAN) exhibits great potential in recovering missing data. In this paper, we fill the missing data of the global IGS TEC maps using pix2pixhd, which is a novel deep learning method based on GAN. Differing from the traditional GAN, pix2pixhd has two generators and three discriminators. The network enhances the ability of our model to complete images with large‐scale missing areas. The result demonstrates that our model generates the ionospheric peak structures at low latitudes well, while behaving badly (the average correlation coefficient: 0.6857) around the edge of the ionospheric peak region. Comparing different scales of the missing data areas, our model has the best performance with 0%–15% missing data. With the large scale of missing data areas (30%–45% and >45%), the performance is still satisfactory. In addition, the completion effect of our model is slightly affected by geomagnetic and solar activity. Our work demonstrates a new possibility for the application of deep learning to a broader field of geosciences, particularly for problems of missing observational data.
Key Points
We make a model to complete International GNSS Service total electron content maps using pix2pixhd based on generative adversarial network
The completion effect of our model with the large scale of missing data areas is still satisfactory
Our model generates the ionospheric peak structures well while it behaves slightly badly around the edge of ionospheric peak region |
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| AbstractList | Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing IGS Global TEC maps using analysis algorithms. However, collecting the completed data is difficult because of the lack of ground receivers, and the processing to obtain the completed IGS TEC maps is time‐consuming. The fast development of deep learning brings an effective way to solve these problems. Among the various deep learning methods, the generative adversarial network (GAN) exhibits great potential in recovering missing data. In this paper, we fill the missing data of the global IGS TEC maps using pix2pixhd, which is a novel deep learning method based on GAN. Differing from the traditional GAN, pix2pixhd has two generators and three discriminators. The network enhances the ability of our model to complete images with large‐scale missing areas. The result demonstrates that our model generates the ionospheric peak structures at low latitudes well, while behaving badly (the average correlation coefficient: 0.6857) around the edge of the ionospheric peak region. Comparing different scales of the missing data areas, our model has the best performance with 0%–15% missing data. With the large scale of missing data areas (30%–45% and >45%), the performance is still satisfactory. In addition, the completion effect of our model is slightly affected by geomagnetic and solar activity. Our work demonstrates a new possibility for the application of deep learning to a broader field of geosciences, particularly for problems of missing observational data. Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing IGS Global TEC maps using analysis algorithms. However, collecting the completed data is difficult because of the lack of ground receivers, and the processing to obtain the completed IGS TEC maps is time‐consuming. The fast development of deep learning brings an effective way to solve these problems. Among the various deep learning methods, the generative adversarial network (GAN) exhibits great potential in recovering missing data. In this paper, we fill the missing data of the global IGS TEC maps using pix2pixhd, which is a novel deep learning method based on GAN. Differing from the traditional GAN, pix2pixhd has two generators and three discriminators. The network enhances the ability of our model to complete images with large‐scale missing areas. The result demonstrates that our model generates the ionospheric peak structures at low latitudes well, while behaving badly (the average correlation coefficient: 0.6857) around the edge of the ionospheric peak region. Comparing different scales of the missing data areas, our model has the best performance with 0%–15% missing data. With the large scale of missing data areas (30%–45% and >45%), the performance is still satisfactory. In addition, the completion effect of our model is slightly affected by geomagnetic and solar activity. Our work demonstrates a new possibility for the application of deep learning to a broader field of geosciences, particularly for problems of missing observational data. We make a model to complete International GNSS Service total electron content maps using pix2pixhd based on generative adversarial network The completion effect of our model with the large scale of missing data areas is still satisfactory Our model generates the ionospheric peak structures well while it behaves slightly badly around the edge of ionospheric peak region Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing IGS Global TEC maps using analysis algorithms. However, collecting the completed data is difficult because of the lack of ground receivers, and the processing to obtain the completed IGS TEC maps is time‐consuming. The fast development of deep learning brings an effective way to solve these problems. Among the various deep learning methods, the generative adversarial network (GAN) exhibits great potential in recovering missing data. In this paper, we fill the missing data of the global IGS TEC maps using pix2pixhd, which is a novel deep learning method based on GAN. Differing from the traditional GAN, pix2pixhd has two generators and three discriminators. The network enhances the ability of our model to complete images with large‐scale missing areas. The result demonstrates that our model generates the ionospheric peak structures at low latitudes well, while behaving badly (the average correlation coefficient: 0.6857) around the edge of the ionospheric peak region. Comparing different scales of the missing data areas, our model has the best performance with 0%–15% missing data. With the large scale of missing data areas (30%–45% and >45%), the performance is still satisfactory. In addition, the completion effect of our model is slightly affected by geomagnetic and solar activity. Our work demonstrates a new possibility for the application of deep learning to a broader field of geosciences, particularly for problems of missing observational data. Key Points We make a model to complete International GNSS Service total electron content maps using pix2pixhd based on generative adversarial network The completion effect of our model with the large scale of missing data areas is still satisfactory Our model generates the ionospheric peak structures well while it behaves slightly badly around the edge of ionospheric peak region |
| Author | Yang, Ding Fang, Hanxian Liu, Zhendi |
| Author_xml | – sequence: 1 givenname: Ding surname: Yang fullname: Yang, Ding organization: National University of Defense Technology – sequence: 2 givenname: Hanxian orcidid: 0000-0002-9866-2293 surname: Fang fullname: Fang, Hanxian email: fanghx@hit.edu.cn organization: National University of Defense Technology – sequence: 3 givenname: Zhendi orcidid: 0000-0001-6705-137X surname: Liu fullname: Liu, Zhendi organization: National University of Defense Technology |
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| Snippet | Total electron content (TEC) is an important parameter that describes the features of the ionosphere. The International GNSS Service (IGS) has been providing... |
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| SubjectTerms | Algorithms Correlation coefficient Correlation coefficients Deep learning Discriminators Generative adversarial networks Ionosphere Ionospheric models Machine learning Missing data Modelling Solar activity Teaching methods Total Electron Content |
| Title | Completion of Global Ionospheric TEC Maps Using a Deep Learning Approach |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1029%2F2022JA030326 https://www.proquest.com/docview/2672281614 |
| Volume | 127 |
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