Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of
  Multiple Deep-learning Models
Khalid A. Alobaid Yasser Abduallah Jason T. L. Wang Haimin Wang Shen Fan Jialiang Li Huseyin Cavus Vasyl Yurchyshyn
Coronal mass ejections (CMEs) are massive solar eruptions, which have a
significant impact on Earth. In this paper, we propose a new method, called
DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic
energy. Being able to estimate these properties helps better understand CME
dynamics. Our study is based on the CME catalog maintained at the Coordinated
Data Analysis Workshops (CDAW) Data Center, which contains all CMEs manually
identified since 1996 using the Large Angle and Spectrometric Coronagraph
(LASCO) on board the Solar and Heliospheric Observatory (SOHO). We use LASCO C2
data in the period between January 1996 and December 2020 to train, validate
and test DeepCME through 10-fold cross validation. The DeepCME method is a
fusion of three deep learning models, including ResNet, InceptionNet, and
InceptionResNet. Our fusion model extracts features from LASCO C2 images,
effectively combining the learning capabilities of the three component models
to jointly estimate the mass and kinetic energy of CMEs. Experimental results
show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009,
respectively) compared to the MRE of 0.019 (0.017, respectively) of the best
component model InceptionResNet (InceptionNet, respectively) in estimating the
CME mass (kinetic energy, respectively). To our knowledge, this is the first
time that deep learning has been used for CME mass and kinetic energy
estimations.