基于深度学习的太阳黑子Wilson山磁类型识别方法

李书馨; 赵学斌; 陈君; 李伟夫; 陈洪; 陈艳红; 崔延美; 袁天娇

doi:10.11728/cjss2022.03.210107004

基于深度学习的太阳黑子Wilson山磁类型识别方法

doi: 10.11728/cjss2022.03.210107004

李书馨^{1, 2, 3},
赵学斌⁴,
陈君⁴,
李伟夫^4,,
陈洪⁴,
陈艳红^{1, 3},
崔延美^{1, 3},
袁天娇^{1, 3}

1.
中国科学院国家空间科学中心　北京　100190
2.
中国科学院大学　北京　100049
3.
中国科学院空间环境态势感知技术重点实验室　北京　100190
4.
华中农业大学理学院　武汉　430070

基金项目: 中国科学院战略性先导科技专项资助（XDA17010302）

详细信息

作者简介:
李伟夫：E-mail：liweifu@mail.hzau.edu.cn

中图分类号: P352
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-07
- 录用日期: 2021-04-12
- 修回日期: 2021-09-03
- 网络出版日期: 2022-05-23

Recognition Method for Mount Wilson Magnetic Type of Sunspots Based on Deep Learning

LI Shuxin^{1, 2, 3},
ZHAO Xuebin⁴,
CHEN Jun⁴,
LI Weifu^4
,,
CHEN Hong⁴,
CHEN Yanhong^{1, 3},
CUI Yanmei^{1, 3},
YUAN Tianjiao^{1, 3}

1.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
Key Laboratory of Science and Technology on Environment Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190
4.
College of Science, Huazhong Agricultural University, Wuhan 430070

摘要

摘要: 太阳黑子是太阳光球层中带有较强磁场的区域，通常是太阳爆发活动的源区。Wilson山磁分类是当前最为主流的太阳黑子分类方法之一，对研究太阳爆发有重要意义。利用2010－2017年间SDO/HMI成像仪观测到的720s_SHARP磁图和白光图数据，研究使用深度学习对太阳黑子群Wilson山磁分类的方法。实验结果表明，Xception网络在识别太阳黑子Wilson山磁类型上能取得最优的效果，其中对α类型黑子的F₁得分为96.50%，β类为93.20%，其他类型的黑子为84.65%。
- 太阳黑子 /
- Xception网络 /
- 深度学习 /
- Wilson山磁分类
Abstract: Sunspots are the regions with stronger magnetic field in the solar photosphere and most of solar eruptions occur in complex sunspot groups. Mount Wilson magnetic classification is one of the most popular sunspots classification methods, which is of great significance to the study of solar eruptions. In recent years, with the rapid development of China’s space industry, space physics research has entered the era of big data. Deep learning methods for processing space science data are springing up. In this study, based on the SDO/HMI SHARP continuum and magnetogram data during 2010－2017, we propose to apply deep learning for the image recognition of Mount Wilson magnetic type of sunspots. The results show that Xception has a productive performance in the identification of the sunspots magnetic types in solar active regions. The F₁ score of sunspots group of α exceeds 96%, that of β is more than 93%, and that of other types is more than 84%.
- Sunspots /
- Xception network /
- Deep learning /
- Mount Wilson magnetic classification
注释:

1) http://jsoc.stanford.edu/

HTML全文

图 1 (a)为α类型的太阳黑子群磁场观测图像，(b)为β类型的太阳黑子群磁场观测图像，(c)和(d)为β-x类型太阳黑子群磁场观测图像，(e)为(a)相应的α类型太阳黑子群白光观测图像，(f)为(b)相应的β类型太阳黑子群白光观测图像，(g)和(h)为(c)和(d)相应的β-x类型太阳黑子群白光观测图像

Figure 1. (a) Magnetogram of sunspots group of α, (b) magnetogram of sunspots group of β, (c)(d) magnetograms of sunspots group of β-x, (e) the image of sunspots group of α corresponding to (a), (f) the image of sunspots group of β corresponding to (b), (g)(h) the images of sunspots group of β-x corresponding to (c)(d)

下载: 全尺寸图片幻灯片

图 2 两种特殊的β类黑子图像。(a)(b)为白光图，(c)(d)为磁图（顶部标注为相应的活动区信息）

Figure 2. Two kinds of special β-type sunspot images. (a) (b) are white light diagrams, (c) (d) are magnetograms. The top notes are activity area information

下载: 全尺寸图片幻灯片

表 1 Wilson黑子磁类型分类

Table 1. Mount Wilson sunspots classification

类型	释义
α	单极黑子群
β	一个黑子群具有正负两种磁场极性，并且相反极性之间的划分界限简单明显
γ	与 β 类不同，该活动区较为复杂，这种类型的黑子群正负极性分布非常不规则
β-γ	此种类型的黑子群分布十分复杂，相反极性之间没有明显的边界
δ	在此类型的双极黑子群中，存在相反极性本影共用同一半影的情况，且其间距小于 2°
β-δ	包含一个或多个 δ 黑子的 β 磁类型黑子群
β-γ-δ	包含一个或多个 δ 黑子的 β-γ 磁类型黑子群
γ-δ	包含一个或多个 δ 黑子的 γ 磁类型黑子群

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表 2 训练、验证、测试集的分布

Table 2. Distribution of training, validation and test sets

磁类型（时间区间）	α	β	β-x
训练集（2010年5月至2014年5月）	3767	5882	1926
验证集（2014年6月至2015年5月）	942	1471	481
测试集（2015年6月至2017年5月）	567	496	109
总计（2010年5月至2017年5月）	5276	7849	2516

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表 3 二分类混淆矩阵

Table 3. Definition of confusion matrix on binary classification

名称	识别的 α	识别的非 α
真实的 α	T	F
真实的非 α	N	C

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表 4 不同深度学习模型的F₁分数(%)

Table 4. F₁ score (%) of different classification methods

类型	α				β				β-x
数据扩充	×	√	×	√	×	√	×	√	×	√	×	√
TTA	×	×	√	√	×	×	√	√	×	×	√	√
Xception	94.52	96.04	94.96	96.50	89.33	92.32	90.53	93.20	82.10	82.94	83.41	84.65
MobileNetV2	94.35	94.37	95.79	95.44	90.63	91.49	92.43	92.57	83.11	87.08	83.57	86.54
MobileNet	94.34	95.51	94.65	94.65	90.86	90.26	90.51	89.85	85.05	77.68	82.35	81.06
ResNet50	93.35	93.31	94.19	93.72	86.53	88.78	87.84	89.38	71.84	76.33	71.97	78.34
Inception ResNetV2	95.06	93.60	96.28	94.18	90.00	88.35	91.48	89.30	80.52	76.36	80.00	77.78
3层CNN⁸	69.64	–	–	–	64.90	–	–	–	64.54	–	–	–
白光Xception	–	–	–	95.21	–	–	–	90.03	–	–	–	75.86
磁图Xception	–	–	–	91.53	–	–	–	86.09	–	–	–	76.84
注　√和×分别表示有和没有使用数据扩充和TTA策略，其中在 α，β和 β-x 上F₁分数最高的用黑体加粗表示。

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