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Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms
Huang, Xin1; Wang, Huaning1,2; Xu, Long1; Liu, Jinfu3; Li, Rong4; Dai, Xinghua1
2018-03-20
Source PublicationASTROPHYSICAL JOURNAL
ISSN0004-637X
Volume856Issue:1Pages:11
AbstractSolar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.
Keywordmethods: data analysis Sun: activity Sun: flares techniques: image processing
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration
DOI10.3847/1538-4357/aaae00
WOS KeywordMAGNETIC-FIELD PROPERTIES ; QUIET ACTIVE REGIONS ; VECTOR MAGNETOGRAMS ; NEURAL-NETWORKS ; PREDICTION ; CLASSIFICATION ; PRODUCTIVITY ; ERUPTIONS ; GRADIENT ; IMAGER
Language英语
Funding ProjectNational Natural Science Foundation of China[11303051] ; National Natural Science Foundation of China[11673034] ; National Natural Science Foundation of China[61572461] ; National Natural Science Foundation of China[1179305] ; National Natural Science Foundation of China[11433006] ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration ; Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000428051200006
PublisherIOP PUBLISHING LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/37289
Collection中国科学院国家天文台
Corresponding AuthorHuang, Xin
Affiliation1.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing, Peoples R China
3.Harbin Inst Technol, Harbin, Heilongjiang, Peoples R China
4.Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Huang, Xin,Wang, Huaning,Xu, Long,et al. Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms[J]. ASTROPHYSICAL JOURNAL,2018,856(1):11.
APA Huang, Xin,Wang, Huaning,Xu, Long,Liu, Jinfu,Li, Rong,&Dai, Xinghua.(2018).Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms.ASTROPHYSICAL JOURNAL,856(1),11.
MLA Huang, Xin,et al."Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms".ASTROPHYSICAL JOURNAL 856.1(2018):11.
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