Multiple CNN Variants and Ensemble Learning for Sunspot Group Classification by Magnetic Type
Multiple CNN Variants and Ensemble Learning for Sunspot Group Classification by Magnetic Type
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DOI：
10.3847/1538-4365/ac249f
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发表时间：
2021-11
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通讯作者：
R. Tang;Xunwen Zeng;Zhou Chen;Wenti Liao;Jing‐song Wang;B. Luo;Yanhong Chen;Yanmei Cui;Meng Zhou;Xiaohua Deng;Haimeng Li;Kai Yuan;Sheng Hong;Zhiping Wu
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文献类型：
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作者：
R. Tang;Xunwen Zeng;Zhou Chen;Wenti Liao;Jing‐song Wang;B. Luo;Yanhong Chen;Yanmei Cui;Meng Zhou;Xiaohua Deng;Haimeng Li;Kai Yuan;Sheng Hong;Zhiping Wu
太阳活动区是日地空间环境的干扰源，通常会导致极端的空间天气，例如地磁风暴。活跃区域的主要标志是太阳黑子。某些类型的太阳黑子与日冕物质抛射或太阳耀斑等喷发事件引起的极端太空天气有关。因此，太阳黑子群的自动分类有助于快速准确地预测太阳活动。本文基于威尔逊山分类方案完成了对太阳黑子群数据集的自动分类，该数据集包含太阳动力天文台日震和磁成像仪SHARP数据提供的2010年5月1日至2017年12月12日的连续谱和磁图图像。经过图像裁剪和数据标准化等数据预处理步骤，数据中的磁型特征更加明显，数据量有所增加。将处理后的数据拼接成两帧单通道数据，供神经网络进行3D卷积运算。本文构建了多种不同结构和层数的卷积神经网络，选取10个模型作为代表，并选择集成学习算法中常用的XGBoost来融合独立分类模型的结果。我们发现XGBoost是一种有效的模型融合方法，这由三种磁性类型中相对平衡的高分证明。集成模型的准确率达到92%以上。 Alpha、Beta和Beta-x磁性类型的F1分数分别达到0.95、0.91和0.82。
A solar active region is a source of disturbance for the Sun–terrestrial space environment and usually causes extreme space weather, such as geomagnetic storms. The main indicator of an active region is sunspots. Certain types of sunspots are related to extreme space weather caused by eruptive events such as coronal mass ejections or solar flares. Thus, the automatic classification of sunspot groups is helpful to predict solar activity quickly and accurately. This paper completed the automatic classification of a sunspot group data set based on the Mount Wilson classification scheme, which contains continuum and magnetogram images provided by the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager SHARP data from 2010 May 1 to 2017 December 12. After applying some data preprocessing steps such as image cropping and data standardization, the features of magnetic type in the data are more obvious, and the amount of data is increased. The processed data are spliced into two frames of single-channel data for the neural network to perform 3D convolution operations. This paper constructs a variety of convolutional neural networks with different structures and numbers of layers, selects 10 models as representatives, and chooses XGBoost, which is commonly used in ensemble-learning algorithms, to fuse the results of independent classification models. We found that XGBoost is an effective way to fuse models, which is proved by the relatively balanced high scores in the three magnetic types. The accuracy of the ensemble model is above 92%. The F1 scores of the magnetic types of Alpha, Beta, and Beta-x reached 0.95, 0.91, and 0.82 respectively.