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 (~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.

中文翻译：

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使用DCGAN和泊松混合完成TEC贴图

由于全球导航卫星系统（GNSS）接收器的覆盖范围有限，因此总电子含量（TEC）图并不完整。获取完整的TEC地图的过程非常耗时，需要五个国际GNSS服务（IGS）中心的合作来合并最终完成的IGS TEC地图。深度学习的进步提供了强大的工具来执行数据科学中的某些任务，例如图像完成（或修复）。其中，深度卷积生成对抗网络（DCGAN）能够学习对象的属性并有效地恢复丢失的数据。经过多年的IGS TEC地图培训，DCGAN和泊松混合（DCGAN-PB）的结合能够有效地学习IGS TEC地图的完成过程。随机掩码和更真实的掩码均用于测试DCGAN‐PB的性能。带有随机掩码的结果（丢失数据的15-40％）表明，与单独使用DCGAN相比，DCGAN-PB可以实现更好的TEC映射完成，更多的训练数据可以显着提高其推广性。对于使用麻省理工学院（MIT）‐TEC数据的真实蒙版进行交叉验证的实验（〜52％的数据缺失），对于高日照，DCGAN‐PB获得了大约三个绝对TEC单位（TECu）的平均均方根误差相对于两种传统的单幅图像修复方法而言，低太阳活动年的平均活度年数小于2个TECu，均值减少了约50％，标准差减少了50％以上。DCGAN‐PB模型可以通过减少手工工作，为TEC地图提供高效的自动完成工具。