文章基本信息

标题：Classification of High Density Regions in Global Ionospheric Maps With Neural Networks
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作者：O. Verkhoglyadova ; N. Maus ; X. Meng 等
期刊名称：Earth and Space Science
电子版ISSN：2333-5084
出版年度：2021
卷号：8
期号：7
页码：n/a-n/a
DOI：10.1029/2021EA001639
语种：English
出版社：John Wiley & Sons, Ltd.
摘要：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.