Application of Recurrent Neural Network to Modeling Earth's Global Electron Density

Sheng Huang; Wen Li; Xiao-Chen ShenQianli MaXiangning ChuDonglai MaJacob BortnikLuisa CapannoloYukitoshi NishimuraJerry Goldstein

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Application of Recurrent Neural Network to Modeling Earth's Global Electron Density

机译：Application of Recurrent Neural Network to Modeling Earth's Global Electron Density

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The total electron density is a fundamental quantity in the Earth's magnetosphere and plays an important role in a number of physical processes, but its dynamic global evolution is not fully quantified yet. We present an implementation of a specific type of recurrent neural network (encoder-decoder), which is distinct from previous models, to construct global electron density based on the multiyear data from Van Allen Probes. The history of geomagnetic indices is first encoded into a hidden state H, then together with auxiliary information (satellite location), they are decoded into the quantity of interest (total electron density in this study). In this process the input of historical geomagnetic indices is detangled from the satellite location and is processed chronologically by the encoder. As a result, time evolution of geomagnetic indices is explicitly embedded in the structure and the encoded hidden state H can be viewed as the representation of the inner magnetospheric state. The magnetospheric state is then decoded to predict global electron density evolution. Our results show that the model can capture the dynamical evolution of total electron density with the formation and evolution of stable and evident plume configurations that roughly agree with global observations. Our findings demonstrate the importance of applying recurrent neural networks to specify the inner magnetospheric state in a novel way, which will potentially improve our fundamental understanding of wave and particle dynamics in the Earth's magnetosphere.

机译：总电子密度是根本数量在地球的磁气圈和戏剧一个重要的角色在一个物理的数量过程,但其全球动态进化没有完全量化。实现一种特定类型的复发神经网络(encoder-decoder)不同于以前的模型,来构建基于多年的全球电子密度范艾伦辐射探测器的数据。地磁指数第一编码成隐藏状态H,然后一起辅助信息(卫星定位),解码的数量(总感兴趣电子密度在这项研究)。历史地磁指数的输入从卫星位置和攻克加工顺序的编码器。结果是,地磁指数的时间演化嵌入式的结构和明确编码H可以看作是隐藏状态表示内心的磁性层的状态。磁性层的状态然后解码预测全球电子密度进化。结果表明,该模型可以捕捉总电子密度的动态演化与稳定的形成和演化明显的羽流配置大致同意与全球观测。示范应用复发的重要性神经网络指定内部将小说的方式磁性层的状态潜在的改善我们的基本理解波和粒子动力学在地球的磁气圈。

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《Journal of Geophysical Research, A. Space Physics: JGR》 |2022年第9期|e2022JA030695-1-e2022JA030695-11|共11页
作者
Sheng Huang; Wen Li; Xiao-Chen ShenQianli MaXiangning ChuDonglai MaJacob BortnikLuisa CapannoloYukitoshi NishimuraJerry Goldstein;
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Center for Space Physics, Boston University, Boston, MA, USA;

Laboratory for Atmospheric and Space Physics, University of Colorado-Boulder, Boulder, CO, USA;

Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, USASpace Science and Engineering Division, Southwest Research Institute, San Antonio, TX, USA;

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中图分类空间物理;地球物理学;
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Application of Recurrent Neural Network to Modeling Earth's Global Electron Density

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