Volume 43 Issue 1
Jan.  2023
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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

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

doi: 10.11728/cjss2023.01.220214015 cstr: 32142.14.cjss2023.01.220214015
  • Received Date: 2022-02-13
  • Accepted Date: 2022-05-30
  • Rev Recd Date: 2022-10-14
  • Available Online: 2023-02-04
  • In the past 20 years, massive solar images and space meteorological data that have been transmitted back continuously and constantly with increasing space-based observatories launched, provide a promising material basis for the application of artificial intelligence technology to forecast and early warning solar activities. However, due to the less the extreme solar eruption and therefore the smaller relevant sample size, a slightly more the moderate solar activities outbreak and a little more the sample data set size, and the common routine non-outbreak space-weather always occurring and thus the samples become concentrated, thereby these condition and phenomenon result in sample imbalance and unlabeled data and so on which seriously affects the wide application of machine learning methods in the field of space weather forecasting and early warning. To handle the imbalance disturbance of sample data set for flare hierarchic recognition and forecast, this paper designs artificial intelligence algorithms for extreme ultraviolet images of solar active regions. Firstly, a dataset of extreme ultraviolet images of solar active regions from SOHO and SDO from 1996 to 2015 was constructed. Then the generated models combined classification head and initialization of convolution kernel are well-designed, and better index of accuracy for M and X flare are experimentally achieved and proved. Simultaneously, in terms of lightweight networks for deep learning, some comparison and analysis of multi algorithms on Meta learning were also discussed, this proposed method achieves finally 10% and 7% increments in POD accuracy compared with ordinary deep learning based method and unsupervised metric learning method, respectively.

     

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  • [1]
    GOLUB L, PASACHOFF J M. Nearest Star: The Surprising Science of Our Sun[M]. Cambridge: Harvard University Press, 2001
    [2]
    DUDA R O, HART P E, STORK D G. Pattern Classification[M]. Beijing: China Machine Press, 2003
    [3]
    CHEN Yunji, LI Ling, LI Wei, et al. AI Computing Systems[M]. Beijing: China Machine Press, 2020
    [4]
    PARK E, MOON Y J, SHIN S, et al. Application of the deep convolutional neural network to the forecast of solar flare occurrence using full-disk solar magnetograms[J]. The Astrophysical Journal, 2018, 869(2): 91 doi: 10.3847/1538-4357/aaed40
    [5]
    NISHIZUKA N, SUGIURA K, KUBO Y, et al. Deep flare net (DeFN) model for solar flare prediction[J]. The Astrophysical Journal, 2018, 858(2): 113 doi: 10.3847/1538-4357/aab9a7
    [6]
    HUANG X, WANG H N, XU L, et al. Deep learning based solar flare forecasting model. I. Results for line-of-sight magnetograms[J]. The Astrophysical Journal, 2018, 856(1): 7 doi: 10.3847/1538-4357/aaae00
    [7]
    SHEN F, YANG Z C, ZHANG J, et al. Three-dimensional MHD simulation of solar wind using a new boundary treatment: comparison with in situ data at Earth[J]. The Astrophysical Journal, 2018, 866(1): 18 doi: 10.3847/1538-4357/aad806
    [8]
    杨易, 沈芳, 杨子才. 多种观测数据驱动的三维行星际太阳风MHD模拟[J]. 空间科学学报, 2020, 40(3): 305-314 doi: 10.11728/cjss2020.03.305

    YANG Yi, SHEN Fang, YANG Zicai. Simulation of interplanetary solar wind with three-dimensional MHD model driven by multiple observations[J]. Chinese Journal of Space Science, 2020, 40(3): 305-314 doi: 10.11728/cjss2020.03.305
    [9]
    GALVEZ R, FOUHEY D F, JIN M, et al. A machine-learning data set prepared from the NASA solar dynamics observatory mission[J]. The Astrophysical Journal Supplement Series, 2019, 242(1): 7 doi: 10.3847/1538-4365/ab1005
    [10]
    HE K M, ZHANG X Y, REN S Q, et al. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification[C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE, 2015: 1026-1034
    [11]
    ALIPOUR N, MOHAMMADI F, SAFARI H. Prediction of flares within 10 days before they occur on the sun[J]. The Astrophysical Journal Supplement Series, 2019, 243(2): 20 doi: 10.3847/1538-4365/ab289b
    [12]
    The SunPy Community, MUMFORD S J, CHRISTE S, et al. SunPy-python for solar physics[J]. Computational Science & Discovery, 2015, 8(1): 014009 doi: 10.1088/1749-4699/8/1/014009
    [13]
    叶茜, 宋乔, 薛炳森. 基于活动区面积统计的F10.7指数预报方法[J]. 空间科学学报, 2019, 39(5): 582-590 doi: 10.11728/cjss2019.05.582

    YE Qian, SONG Qiao, XUE Bingsen. F10.7 index forecasting method based on area statistics of solar active regions[J]. Chinese Journal of Space Science, 2019, 39(5): 582-590 doi: 10.11728/cjss2019.05.582
    [14]
    朱凌锋. 多尺度日面信息的生成式度量模型空间天气预报[D]. 北京: 中国科学院大学, 2021

    ZHU Lingfeng. Generative Model Space Weather Forecasting with Multi-Scale Solar Information[D]. Beijing: University of Chinese Academy of Sciences, 2021
    [15]
    LI H Y, DONG W M, MEI X, et al. LGM-Net: Learning to generate matching networks for few-shot learning[C]//Proceedings of the 36 th International Conference on Machine Learning. Long Beach, USA: ICML, 2019: 3825-3834
    [16]
    BLOOMFIELD D S, HIGGINS P A, MCATEER R T J, et al. Toward reliable benchmarking of solar flare forecasting methods[J]. The Astrophysical Journal Letters, 2012, 747(2): L41 doi: 10.1088/2041-8205/747/2/L41
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