Attention-based machine vision models and techniques for solar wind speed forecasting using solar EUV images
Attention-based machine vision models and techniques for solar wind speed forecasting using solar EUV images
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使用太阳 EUV 图像预测太阳风速的基于注意力的机器视觉模型和技术
DOI：
10.1002/essoar.10508581.1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Brown E
中科院分区：
文献类型：
--
作者：
Brown E
太阳动力学天文台上的大气成像组件拍摄的极紫外图像使使用深度视觉技术预测太阳风速度成为可能-这是一个困难，高影响力和未解决的问题。在4天的时间范围内，这项研究使用基于注意力的模型和一系列方法改进，与2010年至2018年期间的先前工作测试相比，RMSE降低了11.1%，预测相关性提高了17.4%。我们的分析表明，基于注意力的模型与我们的管道相结合，始终优于卷积替代方案。我们的研究表明，通过使用30分钟而不是每天的采样频率，可以大大提高性能。我们的模型已经了解了冕洞的特征与其相关的高速流的速度之间的关系，与经验结果一致。我们的研究发现，我们最好的模型对太阳周期的阶段有很强的依赖性，最好的表现出现在下降阶段。
Extreme ultraviolet images taken by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory make it possible to use deep vision techniques to forecast solar wind speed—a difficult, high‐impact, and unsolved problem. At a 4 day time horizon, this study uses attention‐based models and a set of methodological improvements to deliver an 11.1% lower RMSE and a 17.4% higher prediction correlation compared to the previous work testing on the period from 2010 to 2018. Our analysis shows that attention‐based models combined with our pipeline consistently outperform convolutional alternatives. Our study shows a large performance improvement by using a 30 min as opposed to a daily sampling frequency. Our model has learned relationships between coronal holes' characteristics and the speed of their associated high‐speed streams, agreeing with empirical results. Our study finds a strong dependence of our best model on the phase of the solar cycle, with the best performance occurring in the declining phase.