Abstract Scientists can study the global ionospheric weather by manually or automatically scaling ionograms recorded by global ionosondes to obtain characteristic values of D, E, F regions in the ionosphere. Therefore, fast and accurate ionogram scaling is crucial to real-time space weather monitoring, which is closely related to the performance of space-borne and ground-based technological systems as well as life on earth. The significant increase in data collections during recent years makes an impossible task for human experts to manually scale large amounts of ionograms in time. While the scaling accuracy of traditional automatic methods is less than that by human experts, making them insufficient for scientific tasks. Deep-learning is currently attracting immense research interest in many scaling tasks due to its powerful ability to deal with huge data collections. In this study, we present a deep-learning method for ionogram automatic scaling (DIAS) that can rapidly scale ionograms precisely from the ionosonde data. We trained and tested on data recorded by Wuhan ionosonde located at 114.4° E and 30.5° N. Our results show that the proposed deep-learning method improved the precision and recall rate by 8%, 17%, respectively, compared to using Automatic Real-Time Ionogram Scaling with True-height (ARTIST), which is the most-widely-used automatically scaling routine, in scaling E, F1 and F2 layers. The scaling accuracy of the ionograms provided by our deep-learning model is close to that by human experts, which suggests that the ionograms provided by our deep-learning method can be applied directly to global ionospheric weather nowcasting. Therefore, this study may contribute greatly to improve our knowledge of the ionospheric space.

中文翻译：

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离子图自动缩放的深度学习

摘要 科学家们可以通过手动或自动缩放全球电离层记录的电离图来研究全球电离层天气，以获得电离层中 D、E、F 区域的特征值。因此，快速准确的电离图缩放对于实时空间天气监测至关重要，这与星载和地基技术系统的性能以及地球上的生命密切相关。近年来数据收集的显着增加使得人类专家无法及时手动缩放大量电离图。而传统自动方法的缩放精度低于人类专家，这使得它们不足以完成科学任务。由于其强大的处理海量数据的能力，深度学习目前在许多扩展任务中引起了极大的研究兴趣。在这项研究中，我们提出了一种用于电离图自动缩放 (DIAS) 的深度学习方法，该方法可以根据离子探空仪数据快速准确地缩放电离图。我们对位于 114.4° E 和 30.5° N 的武汉离子探空仪记录的数据进行了训练和测试。我们的结果表明，与使用 Automatic Real 相比，所提出的深度学习方法将精度和召回率分别提高了 8% 和 17% - Time Ionogram Scaling with True-height (ARTIST)，这是最广泛使用的自动缩放例程，用于缩放 E、F1 和 F2 层。我们的深度学习模型提供的电离图的缩放精度接近于人类专家，这表明我们的深度学习方法提供的电离图可以直接应用于全球电离层天气临近预报。因此，这项研究可能有助于提高我们对电离层空间的认识。