Scholarlink

Solar flares are bursts that can disrupt terrestrial and space-based operations. This study aims to develop an unsupervised learning framework to anticipate flares and mitigate their impact. We applied clustering to unlabeled Solar Dynamics Observatory magnetic field time-series data to group similar activities and detect anomalies indicative of impending flares. Features were preprocessed and analyzed with K-Means and DBSCAN. Models differentiated regular activity from anomalous patterns preceding flares, revealing clusters with higher flare propensity. Unsupervised learning effectively forecasts solar flares by autonomously identifying critical patterns and yielded an MSE of 0.06. Integration of wind data and historical records with real-time prediction can be done in the future.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Unsupervised Learning Framework for Solar Flare Forecasting Using Clustering and Anomaly Detection on SDO Magnetogram Data

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Dilip Kumar Gokapay,
  • Ravikumar Munaganuri,
  • Dhruv Diguvapalli,
  • Paul Smayan,
  • Akhil A. S.,
  • Shiva Rama Krishna Mallu,
  • Prem Swarup Mallipudi,
  • Naga Raju Kanchapogu,
  • Sivanagaraju Vallabhuni

摘要

Solar flares are bursts that can disrupt terrestrial and space-based operations. This study aims to develop an unsupervised learning framework to anticipate flares and mitigate their impact. We applied clustering to unlabeled Solar Dynamics Observatory magnetic field time-series data to group similar activities and detect anomalies indicative of impending flares. Features were preprocessed and analyzed with K-Means and DBSCAN. Models differentiated regular activity from anomalous patterns preceding flares, revealing clusters with higher flare propensity. Unsupervised learning effectively forecasts solar flares by autonomously identifying critical patterns and yielded an MSE of 0.06. Integration of wind data and historical records with real-time prediction can be done in the future.