The Study on the Global Evolution of Energetic Electron Precipitation During Geomagnetic Storm Based on Deep Learning Algorithm
The Study on the Global Evolution of Energetic Electron Precipitation During Geomagnetic Storm Based on Deep Learning Algorithm
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DOI：
10.1029/2022ja030974
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Zhou Chen;Hang Tian;Haimeng Li;R. Tang;Zhihai Ouyang;X. Deng
中科院分区：
文献类型：
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作者：
Zhou Chen;Hang Tian;Haimeng Li;R. Tang;Zhihai Ouyang;X. Deng
高能电子沉淀（EEP）在磁层-电离层-热层系统中起着重要作用。它会导致环电流能量密度的降低，电离层下部电子密度的增强，以及臭氧层的破坏。利用高能电子沉淀通量深度神经网络（EPFN）模型，重构了电激振荡的全局动态演化过程。表明该模型能较好地捕捉地磁活动期间全球电电位的变化。在此基础上，基于EPFN模式分析了地磁暴不同阶段EEP的形态演变及其潜在机制。该模型为理解地磁暴过程中环电流电子的损耗过程提供了一个很好的方法。
The energetic electron precipitation (EEP) plays important role in the magnetosphere‐ionosphere‐thermosphere system. It can lead to the decrease of ring current energy densities, the enhancement of electron density in the lower ionosphere, and the destruction of the ozone layer. In the study, using the Energetic Electron Precipitation flux Deep Neural Networks (EPFN) model, the global dynamic evolution of EEP is reconstructed. It suggests that the model can better capture the variation of global EEP during geomagnetic activity. With that, the morphological evolution of EEP and associated potential mechanisms during different phases of geomagnetic storm are analyzed based on the EPFN model. The model provides a good way to understand the loss processes of ring current electrons during geomagnetic storm.