The database of Global Ionospheric Maps (GIMs) produced at Jet Propulsion Laboratory is analyzed. We define high density total electron content (TEC) regions (HDRs) in a map, following certain selection criteria. For the first time, we trained four convolutional neural networks (CNNs) corresponding to four phases of a solar cycle to classify the GIMs by the number of HDRs in each map with 80% accuracy on average. We compared HDR counts for GIMs across ten years to draw conclusions on how the number of HDRs in the GIMs changes throughout the solar cycle. Occurrence of HDRs during different geomagnetic activity conditions is discussed. Catalog of selected HDRs for ten years and four CNN-based models that can be used to extend classification to other years are provided for the community to use.

中文翻译：

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使用神经网络对全球电离层地图中的高密度区域进行分类

分析了喷气推进实验室制作的全球电离层图 (GIM) 数据库。我们按照某些选择标准在地图中定义高密度总电子含量 (TEC) 区域 (HDR)。我们第一次训练了四个对应于太阳周期四个阶段的卷积神经网络 (CNN)，根据每个地图中 HDR 的数量对 GIM 进行分类，平均准确率为 80%。我们比较了十年间 GIM 的 HDR 计数，以得出 GIM 中 HDR 数量如何在整个太阳周期变化的结论。讨论了在不同地磁活动条件下 HDR 的发生。提供了十年的精选 HDR 目录和四个基于 CNN 的模型，可用于将分类扩展到其他年份，供社区使用。