The Application of a Deep Convolutional Generative Adversarial Network on Completing Global TEC Maps

Total electron content (TEC) map is one of the important ionospheric parameters. The International Global Navigation Satellite System Service (Ionosphere Working Group) provides the combined vertical TEC maps. However, the postprocessing of the IGS TEC maps may cost quite a long time, and it's not easy for the organization to collect the complete data. It is necessary for researchers to figure out a method to complete the global TEC maps efficiently with regard to the problems of lack of data or not available to the standard IGS TEC. With the rapid development of the deep learning methods, the Deep Convolutional Generative Adversarial Network exhibits the great potential in computer vision. In this paper, we propose a new method called Global and Local GAN (GLGAN) based on the DCGAN and apply it on completing the global TEC maps. Different from the traditional GAN, the GLGAN consists of a generator (or called completion network) and two discriminators. The completion network is powerful enough to Extract features of IGS TEC maps to complete the TEC maps. The design of two discriminators enhances the ability of judging the quality of output images, and improves the accuracy of the completion network. After analyzing the results, we find the GLGAN have a better performance in complicate structures during geomagnetic storm time. The success of the GLGAN in completing the TEC maps suggests that the deep learning methods are able to solve many problems regarding to data and images in ionospheric parameters' reconstruction or forecasting. Key Points This study adopts an improved Generative Adversarial Network with a local discriminator and a global discriminator to completing the TEC maps with missing data The completion network of Global and Local GAN is able to complete arbitrary regions on the images The model achieves better performance in the more complicated structures during geomagnetic storm timeKeywords