A Dynamical Model of Equatorial Magnetosonic Waves in the Inner Magnetosphere: A Machine Learning Approach
A Dynamical Model of Equatorial Magnetosonic Waves in the Inner Magnetosphere: A Machine Learning Approach
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DOI：
10.1029/2020ja028439
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发表时间：
2021-06
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影响因子：
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通讯作者：
R. Boynton;S. Walker;Homayon Aryan;Y. Hobara;M. Balikhin
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作者：
R. Boynton;S. Walker;Homayon Aryan;Y. Hobara;M. Balikhin
赤道磁声波（EMS），以及合唱和等离子体层嘶嘶声，在磁层高能电子通量的动力学中起着关键作用。数值模型，开发的第一性原理的方法，用于研究高能电子通量的演变主要是基于准线性扩散。应用这种数字代码需要关键磁层波模式分布的统计模型，以估计适当的扩散系数。这些波通常被统计地建模为空间位置和地磁指数的函数（例如，AE、Kp或Dst）。本研究提出了一种新的EMS波振幅的动态时空模型，使用非线性自回归滑动平均eXogenous机器学习方法。EMS波的振幅，测量的货车艾伦探针，建模使用的时间滞后的太阳风和地磁指数作为输入，以及在测量的位置。在一个单独的货车艾伦探针数据集上评估所得到的模型性能，其中发现预测效率为34.0%，相关系数为56.9%。通过更多的训练和验证数据，性能指标可能会得到改善，但是，EMS波分布也可能受到随机因素的影响，并且针对该模型获得的性能指标接近潜在的最大值。
Equatorial magnetosonic waves (EMS), together with chorus and plasmaspheric hiss, play key roles in the dynamics of energetic electron fluxes in the magnetosphere. Numerical models, developed following a first principles approach, that are used to study the evolution of high energy electron fluxes are mainly based on quasilinear diffusion. The application of such numerical codes requires statistical models for the distribution of key magnetospheric wave modes to estimate the appropriate diffusion coefficients. These waves are generally statistically modeled as a function of spatial location and geomagnetic indices (e.g., AE, Kp, or Dst). This study presents a novel dynamic spatiotemporal model for EMS wave amplitude, developed using the Nonlinear AutoRegressive Moving Average eXogenous machine learning approach. The EMS wave amplitude, measured by the Van Allen Probes, are modeled using the time lags of the solar wind and geomagnetic indices as inputs as well as the location at which the measurement is made. The resulting model performance is assessed on a separate Van Allen Probes data set, where the prediction efficiency was found to be 34.0% and the correlation coefficient was 56.9%. With more training and validation data the performance metrics could potentially be improved, however, it is also possible that the EMS wave distribution is affected by stochastic factors and the performance metrics obtained for this model are close to the potential maximum.