Improved ViT model for geomagnetic index prediction by feature quantification of coronal hole images

Coronal holes are the primary source of high-speed solar wind, and the energetic particle flow produced by solar storms can impact the magnetic field environment near Earth, sometimes causing geomagnetic storms. Starting with coronal hole images, this paper explores how to extract coronal hole characteristic parameters from SDO/AIA and SDO/HMI Magnetogram of solar extreme ultraviolet images captured by SDO satellites, and then predicts geomagnetic disturbance by focusing on magnetic field characteristics and physical characteristics of coronal holes through HMI Magnetogram. After segmentation, alignment and other image preprocessing operations, this paper proposes and quantifies 11 characteristic parameters, such as Magnetic Unipolarity, Average Grayscale Value, Coronal Hole Center Location and Area, to establish statistical relationships between coronal holes and geomagnetic storms. Based on the Vision Transformer model, this paper adds the manually extracted magnetic field physical characteristics, which reduces the prediction error RMSE by 0.004, and increases the Correlation Coefficient by 0.034, which enhances the interpretability of the deep learning prediction model. It provides a research basis for the accurate prediction of geomagnetic storms.