首页|Identifying solar flare precursors using time series of SDO/HMI images and SHARP parameters
Identifying solar flare precursors using time series of SDO/HMI images and SHARP parameters
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NETL
NSTL
Amer Geophysical Union
In this paper we present several methods to identify precursors that show great promise for early predictions of solar flare events. A data preprocessing pipeline is built to extract useful data from multiple sources, Geostationary Operational Environmental Satellites and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare inputs for machine learning algorithms. Two classification models are presented: classification of flares from quiet times for active regions and classification of strong versus weak flare events. We adopt deep learning algorithms to capture both spatial and temporal information from HMI magnetogram data. Effective feature extraction and feature selection with raw magnetogram data using deep learning and statistical algorithms enable us to train classification models to achieve almost as good performance as using active region parameters provided in HMI/Space-Weather HMI-Active Region Patch (SHARP) data files. Case studies show a significant increase in the prediction score around 20 hr before strong solar flare events.
solar flaresmachine learningprediction
Chen, Yang、Manchester, Ward B.、Hero, Alfred O.、Toth, Gabor、DuFumier, Benoit、Zhou, Tian、Wang, Xiantong、Zhu, Haonan、Sun, Zeyu、Gombosi, Tamas, I
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Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA
Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA
Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
NETL
NSTL
Amer Geophysical Union
