Abstract. The auroral oval boundary represents an important physical process with implications for the ionosphere and magnetosphere. An automatic auroral oval boundary prediction method based on deep learning in this paper is applied to study the variation of the auroral oval boundary associated with different space physical parameters. We construct an auroral oval boundary dataset to train our proposed model, which consists of 184 416 auroral oval boundary points extracted from 3842 images captured by the Ultraviolet Imager (UVI) of the Polar satellite and its corresponding 18 space physical parameters selected from the OMNI dataset from December 1996 to March 1997. Furthermore, several statistical experiments and correlation analysis experiments are performed based on our dataset to explore the relationship between space physical parameters and the location of the auroral oval boundary. The experiment results show that the prediction model based on the deep learning method can estimate the auroral oval boundary efficiently, and different space physical parameters have different effects on the auroral oval boundary, especially the interplanetary magnetic field (IMF), geomagnetic indexes, and solar wind parameters.

中文翻译：

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基于深度学习模型和空间物理参数的极光椭圆边界预测与变化

摘要。极光椭圆边界代表了一个重要的物理过程，对电离层和磁层有影响。本文采用基于深度学习的极光椭圆边界自动预测方法研究极光椭圆边界随不同空间物理参数的变化规律。我们构建了一个极光椭圆边界数据集来训练我们提出的模型，该数据集由从极地卫星紫外成像仪（UVI）捕获的 3842 张图像中提取的 184 416 个极光椭圆边界点及其从 OMNI 数据集中选择的相应 18 个空间物理参数组成从 1996 年 12 月到 1997 年 3 月。此外，基于我们的数据集进行了多次统计实验和相关分析实验，以探索空间物理参数与极光椭圆边界位置之间的关系。实验结果表明，基于深度学习方法的预测模型能够有效估计极光椭圆边界，不同空间物理参数对极光椭圆边界的影响不同，尤其是行星际磁场（IMF）、地磁指数和太阳风参数。