TEC map completion using DCGAN and Poisson blending

Because of the limited coverage of global navigation satellite system (GNSS) receivers, total electron content (TEC) maps are not complete. The processing to obtain complete TEC maps is time consuming and needs the collaboration of five international GNSS service (IGS) centers to consolidate final completed IGS TEC maps. The advance of deep learning offers powerful tools to perform certain tasks in data science, such as image completion (or inpainting). Among them, deep convolutional generative adversarial network (DCGAN) is capable of learning the properties of the objects and recovering missing data effectively. With years of IGS TEC maps for training, the combination of DCGAN and Poisson blending (DCGAN-PB) is able to effectively learn the completion process of IGS TEC maps. Both random and more realistic masks are used to test the performance of DCGAN-PB. The results with random masks (15-40% missing data) show that DCGAN-PB can achieve better TEC map completion than DCGAN alone, and more training data can significantly improve its generalization. For the cross-validation experiment using the realistic mask from Massachusetts Institute of Technology (MIT)-TEC data (similar to 52% missing data), DCGAN-PB achieves the average root mean squared error about three absolute TEC units (TECu) for high solar activity years and less than two TECu for low solar activity years, which is about 50% reduction of means and more than 50% reduction on standard deviations compared to two conventional single-image inpainting methods. The DCGAN-PB model can lead to an efficient automatic completion tool for TEC maps by minimizing the manual work. Plain Language Summary The limited number of global positioning system (GPS) receivers on Earth's ground leads to the incomplete original total electron content (TEC) maps, which are the GPS measurements of ionosphere. International global navigation satellite system (GNSS) service (IGS) TEC maps are completed with a joint effort of different observation stations involving a lot of manual work. In this work, we propose a deep learning method to learn the completion process of IGS TEC data to facilitate the TEC map completion. The proposed method can automatically recover the incomplete TEC maps with much reduced errors compared to two conventional automatic methods.