基于太阳黑子群数据的多模态太阳耀斑预报模型

当前的太阳耀斑预报模型主要通过统计关系建立，直接将从太阳黑子群磁图中提取的特征参量作为模型输入，系统的自主性低，导致图像数据中包含的与太阳耀斑相关的高阶抽象信息难以被充分利用，进而限制模型预报的精度。为解决当前太阳耀斑预报中数据利用不充分的问题，文章将海量太阳观测数据与先进的人工智能技术结合，综合利用太阳活动区磁场观测图、磁场特征参量和对应的耀斑事件标签，并借助全连接神经网络高精确率以及卷积神经网络高召回率和可有效提取高层语义信息的优点，构建基于深度学习的多模态太阳耀斑预报模型。实验证明该模型的主要评价指标结果比其他模型至少提高7.8%。

Multi-modal solar flare forecasting model based on sunspot group data

The current solar flare forecasting models are mainly based on the statistical relations and the use of the sunspot group feature parameters extracted from the magnetogram as inputs. Thus, the level of the system autonomy is impaired and the precision of the model is lowered, since the high order elements and the more abstract features contained in the magnetogram are missing there. This study, by combining the advanced artificial intelligence technologies and the massive sunspot-related data, such as the magnetograms, the corresponding feature parameters, and the tags of the flare events, establishes a multi-modal solar flare forecasting model based on the deep learning techniques. Specifically, the model takes the advantage of the high precision of the fully connected neural networks and the high recallability of the convolutional neural networks which can effectively extract the high-level semantic information. Experiments show that the main evaluation indexes of this model are at least 7.8% higher than those of the other models.