Several studies have shown the importance of electromagnetic ion cyclotron (EMIC) waves to the pitch angle scattering of energetic particles in the radiation belt, especially relativistic electrons, thus contributing to their net loss from the outer radiation belt to the upper atmosphere. The huge amount of data collected thus far provides us with the opportunity to use a deep learning technique referred to as the Bag-of-Features (BoF). When applied to images of magnetic field spectrograms in the frequency range of EMIC waves, the BoF allows us to distinguish, in a semi-automated way, several patterns in these spectrograms that can be relevant to describe physical aspects of EMIC waves. Each spectrogram image provided as an input to the BoF corresponds to the windowed Fourier transform of a ∼40 minutes to 1 hour interval of Van Allen Probes’ high time-resolution vector magnetic field observations. Our data set spans the 2012 September 8 to 2016 December 31 period and is at geoce...

中文翻译：

---

基于深度学习技术的电磁离子回旋波模式识别：特征包算法在声谱图中的应用

几项研究表明，电磁离子回旋加速器（EMIC）波对于辐射带中高能粒子（尤其是相对论电子）的俯仰角散射的重要性，因此有助于它们从外部辐射带向高层大气的净损失。到目前为止，收集到的大量数据为我们提供了使用称为功能包（BoF）的深度学习技术的机会。当将BoF应用于EMIC波的频率范围内的磁场频谱图的图像时，BoF允许我们以半自动方式区分这些频谱图中的几种模式，这些模式可能与描述EMIC波的物理方面有关。提供给BoF的每个频谱图图像都对应于范艾伦探针（Van Allen Probes）的高时间分辨率矢量磁场观测值的约40分钟至1小时间隔的开窗傅立叶变换。我们的数据集涵盖了2012年9月8日至2016年12月31日这段时期，位于乔治