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Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

文献类型：期刊论文

作者Li, Wenbo2,4; Liu, Libo1,2,4; Chen, Yiding1,2,3; Xiao, Zhuowei2; Le, Huijun1,2,4; Zhang, Ruilong1,2,4
刊名SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
出版日期2023-07-01
卷号21期号:7页码:18
关键词deep learning thermospheric mass density CHAMP feature extraction solar radiation geomagnetic storm
DOI10.1029/2022SW003376
英文摘要Understanding the variation of the Thermospheric Mass Density (TMD) is important for solar-terrestrial physics and applications for spacecraft safety. The thermosphere, as an open system, is impacted by various space environment conditions and has complicated temporal and spatial features. Consequently, TMD observations contain a wealth of multi-scale feature information. How to extract such information from observations is a challenge that requires ongoing research. It is vital to improving our understanding of the TMD features. Deep learning (DL) can learn complex representations directly from raw data, which makes it a compelling feature extraction and modeling tool for providing a novel perspective for TMD modeling. The Residual Network is used in this study to build a DL model with deep network architecture. The observations of CHAllenging Minisatellite Payload are utilized in the training phase, while the Gravity Recovery and Climate Experiment, High Accuracy Satellite Drag Model and Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model are used to evaluate the performance of the DL model. The results reveal that, compared with the shallow model of the typical Multi-Layer Perceptron, the DL model can better extract multi-scale features in the observations while retaining generalization capabilities. Controlled simulation experiments allow us to extract the effects of different physical processes, which improves the interpretability of the DL model. It is demonstrated that the DL model can discriminate the physical processes corresponding to the different space environment indices by simulating Equatorial Mass density Anomaly and geomagnetic storms.
WOS关键词EMPIRICAL-MODEL ; CHAMP ; SCIENCE ; AE
资助项目National Natural Science Foundation of China[42030202] ; National Natural Science Foundation of China[42274223] ; National Natural Science Foundation of China[42241115] ; National Natural Science Foundation of China[42174204] ; Youth Innovation Promotion Association, CAS[Y202021]
WOS研究方向Astronomy & Astrophysics ; Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001023746400001
出版者AMER GEOPHYSICAL UNION
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS ; Youth Innovation Promotion Association, CAS
源URL[http://ir.iggcas.ac.cn/handle/132A11/111270]  
专题地质与地球物理研究所_中国科学院地球与行星物理重点实验室
通讯作者Liu, Libo
作者单位1.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Beijing Natl Observ Space Environm, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Heilongjiang Mohe Observ Geophys, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Li, Wenbo,Liu, Libo,Chen, Yiding,et al. Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning[J]. SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,2023,21(7):18.
APA Li, Wenbo,Liu, Libo,Chen, Yiding,Xiao, Zhuowei,Le, Huijun,&Zhang, Ruilong.(2023).Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning.SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,21(7),18.
MLA Li, Wenbo,et al."Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning".SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 21.7(2023):18.

入库方式： OAI收割

来源：地质与地球物理研究所

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