用新方法预测地磁风暴，提升3-5天预报准确率。
The geomagnetic storm and Kp prediction using Wasserstein transformer
- 融合卫星、太阳图像和地磁数据，用Transformer建模多源信息。
- 引入Wasserstein距离对齐不同模态分布，提升预测稳定性。
- 适合空间天气预警与航天安全领域研究者参考。
准确预测地磁活动至关重要。本文提出一种基于多模态Transformer的框架，通过整合卫星观测、太阳图像和行星Kp时间序列数据，实现对3天和5天行星Kp指数的预测。核心创新在于将Wasserstein距离引入Transformer架构及损失函数，以对齐不同模态间的概率分布。与NOAA模型的对比实验表明，该方法能准确捕捉地磁活动的平静期与风暴期，验证了机器学习与传统模型融合在实时预报中的潜力。
原文摘要 · Abstract (English)
The accurate forecasting of geomagnetic activity is important. In this work, we present a novel multimodal Transformer based framework for predicting the 3 days and 5 days planetary Kp index by integrating heterogeneous data sources, including satellite measurements, solar images, and KP time series. A key innovation is the incorporation of the Wasserstein distance into the transformer and the loss function to align the probability distributions across modalities. Comparative experiments with the NOAA model demonstrate performance, accurately capturing both the quiet and storm phases of geomagnetic activity. This study underscores the potential of integrating machine learning techniques with traditional models for improved real time forecasting.
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