用深度状态空间模型预测太阳耀斑，提升长期依赖建模能力
Deep Space Weather Model: Long-Range Solar Flare Prediction from Multi-Wavelength Images
- 融合多波段图像与长时序依赖的深度状态空间模型
- 在11年周期数据上超越基线与人工专家表现
- 提出稀疏掩码自编码器预训练策略，保留关键区域信息
精准可靠的太阳耀斑预测对减少关键基础设施干扰至关重要，但现有方法或依赖启发式物理特征而缺乏图像表征学习，或端到端方法难以捕捉太阳图像中的长程时空依赖。本文提出深空天气模型（Deep SWM），基于多个深层状态空间模型，同时处理十通道太阳图像与长程时空依赖。模型引入稀疏掩码自编码器，采用双阶段掩码策略，在压缩空间信息的同时保留黑子等关键区域。此外，构建了覆盖完整11年太阳活动周期的公开基准FlareBench以验证方法。实验表明，Deep SWM在标准指标上优于基线方法甚至人类专家，兼具性能与可靠性。
原文摘要 · Abstract (English)
Accurate, reliable solar flare prediction is crucial for mitigating potential disruptions to critical infrastructure, while predicting solar flares remains a significant challenge. Existing methods based on heuristic physical features often lack representation learning from solar images. On the other hand, end-to-end learning approaches struggle to model long-range temporal dependencies in solar images. In this study, we propose Deep Space Weather Model (Deep SWM), which is based on multiple deep state space models for handling both ten-channel solar images and long-range spatio-temporal dependencies. Deep SWM also features a sparse masked autoencoder, a novel pretraining strategy that employs a two-phase masking approach to preserve crucial regions such as sunspots while compressing spatial information. Furthermore, we built FlareBench, a new public benchmark for solar flare prediction covering a full 11-year solar activity cycle, to validate our method. Our method outperformed baseline methods and even human expert performance on standard metrics in terms of performance and reliability. The project page can be found at https://keio-smilab25.github.io/DeepSWM.
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