Deep Learning–based Solar Flare Forecasting Model. III. Extracting Precursors from EUV Images

Abstract

Abstract A solar flare is one of the most intense solar activities, and flare forecasting is necessary to avoid its destructive impact on the near-Earth space environment and technological infrastructure. Previous studies have demonstrated the importance of the photospheric magnetic field in the occurrence of flares. Therefore, most of the input data in traditional solar flare forecasting models are magnetograms of active regions. The magnetic field of the photosphere is routinely measured and observed, but the magnetic field of the corona is not. Hence, the goal of our work is to test whether precursors can be extracted from coronal multiwavelength images of active regions and to build a flare-forecasting model. Therefore, we investigated the effect of using extreme ultraviolet (EUV) images (at 94, 131, 171, 193, 211, and 335 Å) of the active region on solar flare forecasting. We generated a data set consisting of EUV images of the active regions observed by the Solar Dynamics Observatory/Atmospheric Imaging Assembly from 2010 to 2016. Based on this data set, a deep-learning method was used to extract precursors from EUV multiwavelength images. The test results of the forecasting model were discussed and analyzed, and the following conclusions were drawn. (1) Each wavelength achieved good results using the EUV multiwavelength images for flare forecasting. The 94 Å wavelength demonstrated the best result among the single-wavelength results. (2) Among the combined multiwavelength results, the best fusion results were obtained for all six wavelengths.