Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning
文献类型：期刊论文
| 作者 | Li, S. Y.2,3,4,5; Kronberg, E. A.2; Mouikis, C. G.1; Luo, H.3,4,5; Ge, Y. S.3,4,5; Du, A. M.3,4,5 |
| 刊名 | SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 21期号:9页码:20 |
| 关键词 | plasma pressure inner magnetosphere machine learning |
| DOI | 10.1029/2022SW003387 |
| 英文摘要 | The information on plasma pressure in the outer part of the inner magnetosphere is important for simulations of the inner magnetosphere and a better understanding of its dynamics. Based on 17-year observations from both Cluster Ion Spectrometry and Research with Adaptive Particle Imaging Detector instruments onboard the Cluster mission, we used machine-learning-based models to predict proton plasma pressure at energies from similar to 40 eV to 4 MeV in the outer part of the inner magnetosphere (L* = 5-9). Proton pressure distributions are assumed to be isotropic. The location in the magnetosphere, the property of stably trapped particles, and parameters of solar, solar wind, and geomagnetic activity from the OMNI database are used as predictors. We trained several different machine-learning-based models and compared their performances with observations. The results demonstrate that the Extra-Trees Regressor has the best predicting performance. The Spearman correlation between the observations and predictions by the model is about 70%. The most important parameter for predicting proton pressure in our model is the L* value, which relates to the property of stably trapped particles. The most important predictor of solar and geomagnetic activity is F-10.7 index. Based on the observations and predictions by our model, we find that no matter under quiet or disturbed geomagnetic conditions, both the dusk-dawn asymmetry at the dayside with higher pressure at the duskside and the day-night asymmetry with higher pressure at the nightside occur. Our results have direct practical applications, for instance, inputs for simulations of the inner magnetosphere or the reconstruction of the 3-D magnetospheric electric current system based on the magnetostatic equilibrium. |
| WOS关键词 | PLASMA SHEET ; ENERGETIC PARTICLES ; EARTHS MAGNETOSPHERE ; MODEL ; DISTRIBUTIONS ; HYDROGEN ; SCIENCE ; SYSTEMS ; CLUSTER ; OXYGEN |
| 资助项目 | National Natural Science Foundation of China[41874197] ; China Scholarship Council ; German Research Foundation (DFG)[KR 4375/2-1] |
| WOS研究方向 | Astronomy & Astrophysics ; Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | AMER GEOPHYSICAL UNION |
| WOS记录号 | WOS:001068551700001 |
| 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 ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; China Scholarship Council ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) ; German Research Foundation (DFG) |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/110790]  |
| 专题 | 地质与地球物理研究所_深部资源勘探装备研发 |
| 通讯作者 | Kronberg, E. A. |
| 作者单位 | 1.Univ New Hampshire, Dept Phys & Space Sci Ctr, Durham, NH USA 2.Ludwig Maximilians Univ Munchen LMU Munich, Dept Earth & Environm Sci Geophys, Munich, Germany 3.Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China 4.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Geol & Geophys, CAS Engn Lab Deep Resources Equipment & Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, S. Y.,Kronberg, E. A.,Mouikis, C. G.,et al. Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning[J]. SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,2023,21(9):20. |
| APA | Li, S. Y.,Kronberg, E. A.,Mouikis, C. G.,Luo, H.,Ge, Y. S.,&Du, A. M..(2023).Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning.SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,21(9),20. |
| MLA | Li, S. Y.,et al."Prediction of Proton Pressure in the Outer Part of the Inner Magnetosphere Using Machine Learning".SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 21.9(2023):20. |
入库方式： OAI收割
来源：地质与地球物理研究所
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