Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations

2026-05-23

AI Summary

This study addresses the critical challenge of predicting whether coronal mass ejections (CMEs) will trigger geomagnetic storms, thereby safeguarding near-Earth space environments and ground-based infrastructure. For the first time, it integrates multi-source solar observations from SOHO/LASCO, SDO/AIA, and HMI to develop an end-to-end deep learning model based on convolutional neural networks with feature fusion, capable of both deterministic and probabilistic forecasting. Evaluated via five-fold cross-validation, the model achieves a mean True Skill Statistic (TSS) of 0.703 in deterministic prediction and a remarkably low Brier score of 0.095 in probabilistic forecasting—substantially outperforming random baselines. These results underscore the innovative potential and superior performance of combining multi-instrument solar data with deep learning for advancing space weather prediction.

Abstract

Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space environment and on Earth. In this study, we present a novel fusion model to forecast the geoeffectiveness of CME events. Our model combines convolutional neural networks for feature learning and a prediction network for feature fusion and event classification. The model is trained by observations from instruments including the Large Angle Spectroscopic Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) and the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The trained model is then used to predict whether an Earth-reaching CME will cause a geomagnetic storm and/or the probability that the CME will cause such a storm. Experimental results based on a five-fold cross validation scheme demonstrate the good performance of our fusion model, achieving a mean true skill statistic (TSS) score of 0.703 when the model is used as a deterministic prediction tool, and a mean Brier score of 0.095 when the model is used as a probabilistic forecasting tool, where a TSS score of 1 or a Brier score of 0 indicates perfect performance. This work contributes to forecasting the causal relationship between Earth-directed CMEs and geomagnetic storms in solar-terrestrial interactions.

Problem

• geoeffective CMEs
• geomagnetic storm prediction
• solar-terrestrial interactions
• space weather forecasting

Innovation

• deep learning
• coronal mass ejection
• geoeffectiveness prediction
• multi-instrument fusion
• space weather forecasting