利用时空组合神经网络预测海南有/无展宽-F 的电离层图

IF 3.7 2区 地球科学
Space Weather Pub Date : 2024-01-24 DOI:10.1029/2023sw003727
Pengdong Gao, Jinhui Cai, Zheng Wang, Chu Qiu, Guojun Wang, Quan Qi, Bo Wang, Jiankui Shi, Xiao Wang, Kai Ding
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引用次数: 0

摘要

本文介绍了针对海南(北纬 19.5°,东经 109.1°,磁 11°N)观测的有/无 Spread-F 电离图的智能高清短期预测,它由时空 ConvGRU 网络和超分辨率 EDSR 网络组成。我们的预测仅基于离子图中的时空特征。在一个太阳周期(14 年)内,有 469,227 张电离图被分为 5 类,即频率/范围/混合/强范围/无展宽 F,这些电离图由研究小组人工标注,我们将这些电离图处理成两个数据集,用于训练上述两个网络。我们设计并进行了一系列综合实验,以确定最佳超级参数。我们的方法输入 8 个连续的真实电离图(持续 2 小时),并生成下两个数字(接下来的 30 分钟)。值得注意的是,所有预测数字在预测 Spread-F 的发生方面都达到了 94% 以上的高准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Ionograms With/Without Spread-F at Hainan by a Combined Spatio-Temporal Neural Network
An intelligent high-definition and short-term prediction of ionograms with/without Spread-F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio-temporal ConvGRU network and a super-resolution EDSR network. Our prediction is based on spatio-temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super-parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread-F.
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