使用基于注意力机制的深度神经网络迈向可解释的太阳耀斑预测
Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks
AI总结：
本研究开发基于注意力机制的深度学习模型执行全盘二元耀斑预测，通过注意力图可视化提升可解释性，模型达到TSS=0.54和HSS=0.37，并能有效预测边缘耀斑。
摘要：
太阳耀斑预测是空间天气预报的核心问题，机器学习和深度学习的最新进展加速了复杂数据驱动太阳耀斑预测模型的采用。在本研究中，我们开发了一种基于注意力机制的深度学习模型，作为对标准卷积神经网络（CNN）流程的改进，用于执行全盘二元耀斑预测，即预测未来24小时内发生≥M1.0级耀斑的情况。为此任务，我们收集了由全盘视线（LoS）磁图生成的压缩图像。我们使用数据增强的过采样来解决类别不平衡问题，并使用真实技能统计量（TSS）和Heidke技能分数（HSS）作为评估指标。此外，我们通过将注意力图叠加在输入磁图上解释了模型，并可视化了模型聚焦的重要区域，这些区域导致了最终的决策。本研究的重要发现是：（i）我们成功实现了一个基于注意力机制的全盘耀斑预测器，可用于业务化预报，其中候选模型达到平均TSS=0.54±0.03和HSS=0.37±0.07。（ii）我们证明了我们的全盘模型能够从全盘磁图图像中学习与活动区相对应的显著特征。（iii）我们的实验评估表明，模型能以熟练的技能预测边缘附近的耀斑，且预测基于全盘磁图中的相关活动区（ARs）或AR特征。
英文摘要：
Solar flare prediction is a central problem in space weather forecasting and recent developments in machine learning and deep learning accelerated the adoption of complex models for data-driven solar flare forecasting. In this work, we developed an attention-based deep learning model as an improvement over the standard convolutional neural network (CNN) pipeline to perform full-disk binary flare predictions for the occurrence of $\geq$M1.0-class flares within the next 24 hours. For this task, we collected compressed images created from full-disk line-of-sight (LoS) magnetograms. We used data-augmented oversampling to address the class imbalance issue and used true skill statistic (TSS) and Heidke skill score (HSS) as the evaluation metrics. Furthermore, we interpreted our model by overlaying attention maps on input magnetograms and visualized the important regions focused on by the model that led to the eventual decision. The significant findings of this study are: (i) We successfully implemented an attention-based full-disk flare predictor ready for operational forecasting where the candidate model achieves an average TSS=0.54$\pm$0.03 and HSS=0.37$\pm$0.07. (ii) we demonstrated that our full-disk model can learn conspicuous features corresponding to active regions from full-disk magnetogram images, and (iii) our experimental evaluation suggests that our model can predict near-limb flares with adept skill and the predictions are based on relevant active regions (ARs) or AR characteristics from full-disk magnetograms.
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