Predicting the 1 AU Arrival Time of Coronal Mass Ejections Based on Convolutional Neural Network

Abstract

Coronal mass ejections (CMEs) are one of the most violent solar eruptions, which can burst out large amounts of magnetized plasma with speeds up to thousands of kilometers per second. When it reaches the Earth, a CME can cause geomagnetic storm, affecting aviation safety, satellite operations, communications systems and power facilities. Therefore, fast and accurate prediction of CME arrival time is crucial for avoiding severe damaging effects and reducing economic losses. The initial morphology and kinematics of a CME in the corona can be observed by the coronagraphs equipped on the Solar and Heliospheric Observatory (SOHO), so that the coronagraphs should be useful to predict the CME arrival times. In this study, convolutional neural network (CNN) is used to obtain the features of SOHO/LASCO coronagraph pictures related to the CME transit time, and establish a model capable of predicting the CME arrival time. The influence of different hyperparameters of CNN on the prediction results is studied. Further, we add a physical information constraint of the initial velocities of CME to the basic CNN outputs, and found that smaller prediction errors can be obtained.