用太阳磁场与紫外图像预测耀斑期间的极紫外辐射变化
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
- 融合多仪器观测数据，用轻量注意力模型建模磁场与日冕辐射关系
- 在33次显著耀斑上实现连续3天6.5纳米波段辐射预报，优于基线方法
- 适合空间天气预警、卫星轨道管理等短期辐射预测场景
我们提出FlareEUV，一种用于预测太阳耀斑期间极紫外（EUV）辐射的多模态深度学习框架。该模型基于美国国家航空航天局（NASA）太阳动力学观测台（SDO）的多仪器观测数据，针对2011至2014年太阳活动周期24中的33次显著耀斑事件，预测未来连续三天在6.5纳米波段的日均极紫外辐照度。观测数据包含13幅对齐的全盘图像，涵盖8个AIA极紫外/紫外产品和5个HMI磁力场/连续谱产品。FlareEUV通过轻量级注意力架构从原始图像中学习磁结构与日冕发射之间的关联。实验表明，该模型在显著耀斑期间的短时极紫外辐射预测中表现优异，优于多种基线方法。
原文摘要 · Abstract (English)
We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar flares, using multi-instrument observations from NASA's Solar Dynamics Observatory (SDO). We consider 33 significant flares in the period between 2011 and 2014 in Solar Cycle 24. The SDO observations include 13 co-aligned full-disk images, comprising eight AIA EUV/UV and five HMI magnetic/continuum products. FlareEUV learns the relationship between magnetic structure and coronal emission from the raw imaging data using a lightweight attention-based architecture. Our experimental results demonstrate the good performance of FlareEUV in short-term EUV irradiance forecasting during the significant flares and its superiority over baseline methods.
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