Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions - Qingfeng Li; Hanxian Fang; Chao Xiao; Die Duan; Hongtao Huang; Ganming Ren (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025)
Total electron content (TEC) is a key ionospheric parameter, but data gaps, especially over oceans, remain challenging due to sparse receiver coverage. Deep learning offers promising solutions for such problems. This study proposes a novel hybrid model combining a variational autoencoder (VAE) and a generative adversarial network (GAN) to improve the quality and diversity of generated TEC samples. By integrating these algorithms, the model achieves more balanced outputs than standalone approaches. Additionally, the international reference ionosphere (IRI) models TEC maps are incorporated as constraints to form the Completion GAN Variational Autoencoder-label (CGVAE-label) model, enhancing large-scale data completion, robustness, and integration of prior knowledge. The study evaluates the models performance under varying conditions, including geomagnetic quiet/disturbed periods, high/low solar activity years, and different mask scales. Results show that the CGVAE-label model excels in TEC completion, achieving a mean structural similarity of 93.80% and a root mean square error of 2.20 TECU. It outperforms the baseline CGVAE model, particularly in reconstructing peak ionospheric structures. Performance is better during geomagnetic quiet periods than disturbed periods and during solar minimum years compared to solar maximum years.This work demonstrates the potential of deep learning in geoscience, particularly for data completion tasks, offering a robust framework adaptable to diverse ionospheric conditions. The CGVAE-label model provides a reliable solution for TEC gap-filling, with implications for broader applications in remote sensing and space weather studies.