Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917882748731392 |
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| author | Alobaid, Khalid A. Wang, Jason T. L. Wang, Haimin Jing, Ju Abduallah, Yasser Wang, Zhenduo Farooki, Hameedullah Cavus, Huseyin Yurchyshyn, Vasyl |
| author_facet | Alobaid, Khalid A. Wang, Jason T. L. Wang, Haimin Jing, Ju Abduallah, Yasser Wang, Zhenduo Farooki, Hameedullah Cavus, Huseyin Yurchyshyn, Vasyl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning Alobaid, Khalid A. Wang, Jason T. L. Wang, Haimin Jing, Ju Abduallah, Yasser Wang, Zhenduo Farooki, Hameedullah Cavus, Huseyin Yurchyshyn, Vasyl Machine Learning Solar and Stellar Astrophysics Space Physics 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. |
| title | Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning |
| topic | Machine Learning Solar and Stellar Astrophysics Space Physics |
| url | https://arxiv.org/abs/2501.01011 |