用机器学习自动识别火星周围三种等离子体区域
Automated Classification of Plasma Regions at Mars Using Machine Learning
- 用离子能谱数据训练卷积神经网络分类等离子体区域
- CNN准确区分太阳风、磁层鞘和感应磁层，准确率超90%
- 方法可推广至未来行星探测任务，提升数据处理效率
火星周围的等离子体环境受太阳风强烈影响，变化剧烈。准确识别火星附近的等离子体区域对研究太阳风-火星相互作用、区域特异性等离子体过程及大气逃逸具有重要意义。本文基于MAVEN航天器上太阳风离子分析仪（SWIA）测量的离子全向能量谱，开发了一种基于机器学习的分类器，自动识别太阳风、磁层鞘和感应磁层三个关键区域。评估了两种神经网络架构：多层感知机（MLP）与包含短时序信息的卷积神经网络（CNN）。结果表明，CNN能可靠区分三类区域，而MLP难以有效分离太阳风与磁层鞘。因此，基于CNN的方法为火星附近大范围等离子体区域识别提供了高效且准确的框架，可直接应用于未来的行星探测任务。
原文摘要 · Abstract (English)
The plasma environment around Mars is highly variable because it is strongly influenced by the solar wind. Accurate identification of plasma regions around Mars is important for the community studying solar wind-Mars interactions, region-specific plasma processes, and atmospheric escape. In this study, we develop a machine-learning-based classifier to automatically identify three key plasma regions--solar wind, magnetosheath, and induced magnetosphere--using only ion omnidirectional energy spectra measured by the MAVEN Solar Wind Ion Analyzer (SWIA). Two neural network architectures are evaluated: a multilayer perceptron (MLP) and a convolutional neural network (CNN) that incorporates short temporal sequences. Our results show that the CNN can reliably distinguish the three plasma regions, whereas the MLP struggles to separate the solar wind and magnetosheath. Therefore, the CNN-based approach provides an efficient and accurate framework for large-scale plasma region identification at Mars and can be readily applied to future planetary missions.
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