日本地球惑星科学連合2019年大会

講演情報

[E] ポスター発表

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM10] Multi-scale Coupling in the Magnetosphere-Ionosphere-Thermosphere System

2019年5月27日(月) 13:45 〜 15:15 ポスター会場 (幕張メッセ国際展示場 8ホール)

コンビーナ:Yue Deng(University of Texas at Arlington)、Toshi Nishimura(Boston University)、Liu Huixin(九州大学理学研究院地球惑星科学専攻 九州大学宙空環境研究センター)、Yanshi Huang(Harbin Institute of Technology, Shenzhen)

[PEM10-P03] A regularized deep convolutional general adversarial network (R-DCGAN) for total electron content map completion

*Mingwu Jin1Zhou Chen2,3Yue Deng1Yang Pan1Jin-Song Wang2 (1.University of Texas at Arlington、2.Institute of Space Science and Technology, Nanchang University, Nanchang, China、3.Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration, Beijing, China)

キーワード:Deep learning method, TEC , map completion

Observations with a complete global coverage are important for space physics research and applications. However, due to the technical limitations and costs, such a complete global coverage is often unavailable. Thus, data filling algorithms are often pursued to fill missing data gaps to create a global map from the incomplete data. In this work, a novel deep learning algorithm, regularized deep convolutional generative adversarial network (R-DCGAN), is developed by adding an extra discriminator to the conventional DCGAN to fill the missing data of total electron content (TEC) map images. R-DCGAN incorporates the knowledge from reference TEC maps of the International Global Navigation Satellite Systems Service Ionosphere Working Group to achieve much better TEC map completion performance than the conventional DCGAN as demonstrated by both synthetic and real data. The R-DCGAN framework can be extended to other fields of space sciences to address the missing observation data issues.