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太阳活动区EUV图像的生成式模型耀斑分级与预报

郭大蕾 张振 朱凌锋 薛炳森

郭大蕾, 张振, 朱凌锋, 薛炳森. 太阳活动区EUV图像的生成式模型耀斑分级与预报[J]. 空间科学学报, 2023, 43(1): 60-67. doi: 10.11728/cjss2023.01.220214015
引用本文: 郭大蕾, 张振, 朱凌锋, 薛炳森. 太阳活动区EUV图像的生成式模型耀斑分级与预报[J]. 空间科学学报, 2023, 43(1): 60-67. doi: 10.11728/cjss2023.01.220214015
GUO Dalei, ZHANG Zhen, ZHU Lingfeng, XUE Bingsen. Generative Model-based of Flare Hierarchic Recognition and Forecast of Extreme Ultraviolet Images in Solar Active Region (in Chinese). Chinese Journal of Space Science, 2023, 43(1): 60-67 doi: 10.11728/cjss2023.01.220214015
Citation: GUO Dalei, ZHANG Zhen, ZHU Lingfeng, XUE Bingsen. Generative Model-based of Flare Hierarchic Recognition and Forecast of Extreme Ultraviolet Images in Solar Active Region (in Chinese). Chinese Journal of Space Science, 2023, 43(1): 60-67 doi: 10.11728/cjss2023.01.220214015

太阳活动区EUV图像的生成式模型耀斑分级与预报

doi: 10.11728/cjss2023.01.220214015
基金项目: 国家自然科学基金项目资助(11272333)
详细信息
    作者简介:

    郭大蕾:E-mail:dalei.guo@ia.ac.cn

  • 中图分类号: P353

Generative Model-based of Flare Hierarchic Recognition and Forecast of Extreme Ultraviolet Images in Solar Active Region

  • 摘要: 近年来,不断发射的空基观测台持续传送回海量日面图像及日地间气象数据,为采用人工智能技术对太阳活动进行预报预警提供了数据基础。但是,极端天气爆发少,样本量较少;中等程度爆发稍多,样本量较多;常规无爆发天气常见,样本较为集中,样本不均衡状况严重影响机器学习方法在空间天气领域的广泛应用。本文面向多源多通道多尺度日面图像信息,构建了来自SOHO和SDO的1996-2015年日面活动区图像数据集;针对数据分布的不平衡,对太阳活动区图像作耀斑分级与预报。在对比分析元学习算法的基础上,设计了结合分类头设计和卷积核初始化的生成式模型;在使网络轻量化的基础上,能够将M和X级耀斑预报的检测率指标相较于普通的深度学习模型和无监督度量式模型分别提升10%和7%。

     

  • 图  1  按耀斑级别的数据分布

    Figure  1.  Data distribution by flare level

    图  2  包含卷积核初始化的生成式度量学习模型

    Figure  2.  Generative metric learning model with kernel initialization

    图  3  不同模型shot数目变化对TSS的影响

    Figure  3.  Effect of shots number of different models on TSS

    表  1  耀斑级别数据集分布

    Table  1.   Flare dataset of different levels

    数据集BCM+X合计
    训练集669958837+982562
    测试集194234247+28703
    总计86311921084+1263265
    下载: 导出CSV

    表  2  耀斑分类混淆矩阵

    Table  2.   Confusion matrix for flare classification

    i 级耀斑(True)i 级耀斑(False)
    预报为i 级(Positive)TPFP
    预报非i 级(Negative)FNTN
    下载: 导出CSV

    表  3  不同类别的分类结果

    Table  3.   Classification results of different categories

    模型ClassPODTSS
    I3 D[14] B 0.52 0.33
    C 1 0.95
    MX 0.60 0.40
    Unsup_1 shot B 0.44 0.26
    C 0.99 0.99
    MX 0.63 0.35
    Unsup_15 shot B 0.55 0.40
    C 1 0.99
    MX 0.76 0.48
    Generate_1 shot B 0.69 0.54
    C 0.70 0.55
    MX 0.70 0.55
    下载: 导出CSV

    表  4  两种模型在4-way情况下的TSS结果

    Table  4.   TSS index in the 4-way case under different models

    模型ClassTSS
    Unsup_10 shot B 0.413
    C 0.976
    M 0.166
    X 0.209
    Generate_1 shot B 0.345
    C 0.366
    M 0.362
    X 0.349
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-13
  • 录用日期:  2022-05-30
  • 修回日期:  2022-10-14
  • 网络出版日期:  2023-02-04

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