用LSTM预测太阳活跃区形成，提前10小时以上预警
Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks
- 基于磁力、光球和多普勒数据构建时序模型
- 对5个活跃区预测成功，最早提前29小时预警
- 适合太阳物理与空间天气预报研究者参考
我们利用太阳动力学观测台（SDO）的日震与磁成像仪（HMI）获取的多普勒速度、连续谱强度和磁场数据，构建了声波功率和磁通量的时间序列数据集，并训练长短期记忆网络（LSTM）模型，提前12小时预测连续谱强度变化。该模型能捕捉到即将出现的磁通量与连续谱强度下降之间的关联。在5个未参与训练的活跃区上测试，表现最佳的模型8在实验环境下成功预测全部5个活跃区，在业务运行中成功预测3个。分别提前10、29和5小时预测了AR11726、AR13165和AR13179的形成；在全太阳盘面的活跃区与宁静区上，模型平均均方根误差（RMSE）均为0.11。本研究为机器学习辅助太阳活跃区预测奠定了基础。
原文摘要 · Abstract (English)
We developed Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Using the Doppler shift velocity, the continuum intensity, and the magnetic field observations from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI), we have created time-series datasets of acoustic power and magnetic flux, which are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease. Testing of the models' performance was done on data for 5 ARs, unseen from the models during training. Model 8, the best performing model trained, was able to make a successful prediction of emergence for all testing active regions in an experimental setting and three of them in an operational. The model predicted the emergence of AR11726, AR13165, and AR13179 respectively 10, 29, and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet areas on the solar disc. This work sets the foundations for ML-aided prediction of solar ARs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。