Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

TL;DR

This paper introduces GeoCME, a deep learning framework trained on SOHO observations to predict whether CMEs will cause geomagnetic storms, achieving high accuracy and offering a new tool for space weather forecasting.

Contribution

The paper presents GeoCME, a novel deep learning model utilizing ensemble and transfer learning to predict CME geoeffectiveness from SOHO data, advancing space weather prediction methods.

Findings

Achieved a Matthew's correlation coefficient of 0.807.

Attained a true skill statistics score of 0.714.

Probabilistic forecasts had a Brier score of 0.094.

Abstract

The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness is crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth's magnetosphere during which the minimum Dst index value is less than -50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew's correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.