Distribution and Evolution of Chorus Waves Modeled by a Neural Network: The Importance of Imbalanced Regression

IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Xiangning Chu, Jacob Bortnik, Wen Li, Xiao‐Chen Shen, Qianli Ma, Donglai Ma, David Malaspina, Sheng Huang
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Abstract

Abstract Whistler‐mode chorus waves play an essential role in the acceleration and loss of energetic electrons in the Earth’s inner magnetosphere, with the more intense waves producing the most dramatic effects. However, it is challenging to predict the amplitude of strong chorus waves due to the imbalanced nature of the data set, that is, there are many more non‐chorus data points than strong chorus waves. Thus, traditional models usually underestimate chorus wave amplitudes significantly during active times. Using an imbalanced regressive (IR) method, we develop a neural network model of lower‐band (LB) chorus waves using 7‐year observations from the EMFISIS instrument onboard Van Allen Probes. The feature selection process suggests that the auroral electrojet index alone captures most of the variations of chorus waves. The large amplitude of strong chorus waves can be predicted for the first time. Furthermore, our model shows that the equatorial LB chorus’s spatiotemporal evolution is similar to the drift path of substorm‐injected electrons. We also show that the chorus waves have a peak amplitude at the equator in the source MLT near midnight, but toward noon, there is a local minimum in amplitude at the equator with two off‐equator amplitude peaks in both hemispheres, likely caused by the bifurcated drift paths of substorm injections on the dayside. The IR‐based chorus model will improve radiation belt prediction by providing chorus wave distributions, especially storm‐time strong chorus. Since data imbalance is ubiquitous and inherent in space physics and other physical systems, imbalanced regressive methods deserve more attention in space physics.
用神经网络模拟合唱波的分布和演化:不平衡回归的重要性
惠斯勒模式合唱波在地球内磁层中高能电子的加速和损失中起着至关重要的作用,其中强度越强的波产生的效果越显著。然而,由于数据集的不平衡性,预测强合唱波的振幅是具有挑战性的,也就是说,非合唱数据点比强合唱波要多得多。因此,传统的模型通常低估合唱波振幅显著在活跃时期。使用不平衡回归(IR)方法,我们利用Van Allen探测器上搭载的EMFISIS仪器7年的观测数据建立了低频段(LB)合唱波的神经网络模型。特征选择过程表明,极光电喷指数单独捕获了合唱波的大部分变化。首次对强合唱波的大振幅进行了预报。此外,我们的模型表明,赤道LB合唱的时空演化与亚风暴注入电子的漂移路径相似。我们还发现,在午夜附近,副声波在赤道处有一个振幅峰值,但在接近正午时,在赤道处有一个局部振幅最小值,在两个半球都有两个赤道外振幅峰值,这可能是由白天侧亚暴注入的分岔漂移路径引起的。基于红外的合唱模式将通过提供合唱波分布,特别是风暴时的强合唱,来改善辐射带的预测。由于数据不平衡在空间物理和其他物理系统中是普遍存在和固有的,因此不平衡回归方法在空间物理中更值得关注。
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来源期刊
CiteScore
5.90
自引率
29.70%
发文量
166
审稿时长
>12 weeks
期刊介绍: Space Weather: The International Journal of Research and Applications (SWE) is devoted to understanding and forecasting space weather. The scope of understanding and forecasting includes: origins, propagation and interactions of solar-produced processes within geospace; interactions in Earth’s space-atmosphere interface region produced by disturbances from above and below; influences of cosmic rays on humans, hardware, and signals; and comparisons of these types of interactions and influences with the atmospheres of neighboring planets and Earth’s moon. Manuscripts should emphasize impacts on technical systems including telecommunications, transportation, electric power, satellite navigation, avionics/spacecraft design and operations, human spaceflight, and other systems. Manuscripts that describe models or space environment climatology should clearly state how the results can be applied.
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