空间科学学报2025，Vol.45Issue(2)：253-265,13.DOI:10.11728/cjss2025.02.2024-0100
基于深度学习的太阳黑子群磁类型分类
Magnetic Type Classification of Sunspot Groups Based on Deep Learning
摘要
Abstract
Solar activity,as a significant manifestation of energy release and material movement in the solar atmosphere,is the main disturbance source of space weather.The violent solar activity represented by sunspots may lead to drastic changes in the near-earth space environment,and then have a profound impact on human production and life.Accurate and efficient prediction of space weather is helpful to re-duce its impact on human production.In this paper,a magnetic type classification model of sunspot Mount Wilson based on squeeze-and-excitation module and deep residual network is established by us-ing the continuum map and magnetogram map data observed by the HMI instrument on the Solar Dy-namics Observatory(SDO)from 2010 to 2017.In order to effectively avoid the problem of model overfit-ting caused by the continuity of time series,this paper uses the time series segmentation method to di-vide the data set,and applies the data augmentation strategy combined with the characteristics of sunspot images to improve the generalization ability of the model.The experimental results show that the model proposed in this study can perform the task of sunspot classification accurately,especially in the recognition of complex sunspots,and its recognition ability has been significantly improved com-pared with traditional methods.In addition,this paper uses the class activation mapping method to vi-sualize the test set samples,analyzes the feature images extracted from the model and the classification basis,so as to improve the interpretability of the model.关键词
太阳黑子/深度残差网络/压缩激励模块/数据增强/类激活映射Key words
Sunspots/Deep residual network/Squeeze-and-excitation block/Data augmentation/Class activation mapping分类
地球科学引用本文复制引用
尹耀,李依洋,黄狮勇,徐思博,袁志刚,吴红红,姜奎,熊启洋,林仁桐..基于深度学习的太阳黑子群磁类型分类[J].空间科学学报,2025,45(2):253-265,13.基金项目
科技部重点研发项目(2022YFF0503700)和国家自然科学基金项目(42430203,42441811)共同资助 （2022YFF0503700）
