Automatic Classification of Plasma Regions in Near-Earth Space With Supervised Machine Learning: Application to Magnetospheric Multi Scale 2016–2019 Observations
Automatic Classification of Plasma Regions in Near-Earth Space With Supervised Machine Learning: Application to Magnetospheric Multi Scale 2016–2019 Observations
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DOI：
10.3389/fspas.2020.00055
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发表时间：
2020-09
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影响因子：
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通讯作者：
H. Breuillard;R. Dupuis;A. Retinò;O. Le Contel;J. Amaya;G. Lapenta
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文献类型：
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作者：
H. Breuillard;R. Dupuis;A. Retinò;O. Le Contel;J. Amaya;G. Lapenta
近地空间等离子体区域的正确分类对于对基本等离子体过程进行明确的统计研究至关重要，这些过程包括冲击、磁重联、波和湍流、喷流及其组合。大多数现有研究都是通过使用人为驱动的方法进行的，例如视觉数据选择或将预定义阈值应用于不同的可观察血浆量。虽然人为驱动的方法允许进行许多统计研究，但这些方法通常很耗时，并且可能会引入重要的偏差。另一方面，最近出现的大型高质量航天器数据库，加上机器学习算法的重大进展，现在可以将机器学习应用于现场等离子体数据。在这项研究中，我们将全卷积神经网络（FCN）深度机器学习算法应用于最近的磁层多尺度（MMS）使命数据，以便对2016-2019年期间近地空间的10个关键等离子体区域进行分类。为了这个目的，我们使用可用的时间序列为每个这样的等离子体区域，这是通过使用人为驱动的选择性下行链路应用到MMS突发数据标记的间隔。我们讨论了几个定量参数来评估这两种方法的准确性。我们的研究结果表明，FCN方法是可靠的，准确地分类标记的时间序列数据，因为它考虑到在每个区域的等离子体数据的动力学特征。我们还提出了良好的精度的FCN方法时，适用于未标记的MMS数据。最后，我们展示了如何使用MMS数据的这种方法可以扩展到从集群使命的数据，表明这种方法可以成功地应用于任何原位航天器等离子体数据库。
The proper classification of plasma regions in near-Earth space is crucial to perform unambiguous statistical studies of fundamental plasma processes such as shocks, magnetic reconnection, waves and turbulence, jets and their combinations. The majority of available studies have been performed by using human-driven methods, such as visual data selection or the application of predefined thresholds to different observable plasma quantities. While human-driven methods have allowed performing many statistical studies, these methods are often time-consuming and can introduce important biases. On the other hand, the recent availability of large, high-quality spacecraft databases, together with major advances in machine-learning algorithms, can now allow meaningful applications of machine learning to in-situ plasma data. In this study, we apply the fully convolutional neural network (FCN) deep machine-leaning algorithm to the recent Magnetospheric Multi Scale (MMS) mission data in order to classify 10 key plasma regions in near-Earth space for the period 2016-2019. For this purpose, we use available intervals of time series for each such plasma region, which were labeled by using human-driven selective downlink applied to MMS burst data. We discuss several quantitative parameters to assess the accuracy of both methods. Our results indicate that the FCN method is reliable to accurately classify labeled time series data since it takes into account the dynamical features of the plasma data in each region. We also present good accuracy of the FCN method when applied to unlabeled MMS data. Finally, we show how this method used on MMS data can be extended to data from the Cluster mission, indicating that such method can be successfully applied to any in situ spacecraft plasma database.