Automatic Spread‐F Detection Using Deep Learning
Automatic Spread‐F Detection Using Deep Learning
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DOI：
10.1029/2021rs007419
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发表时间：
2021-12
期刊：
影响因子：
1.6
通讯作者：
Christopher Luwanga;T. Fang;A. Chandran;Yu-Ju Lee
中科院分区：
文献类型：
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作者：
Christopher Luwanga;T. Fang;A. Chandran;Yu-Ju Lee
Spread-F（SF）是当电离层中等离子体不规则性对电离层探测仪信号产生显著影响时，可以在电离图上观察到的一个特征。根据等离子体不规则性的大小，当信号通过电离层时，不同频率的无线电波受到不同的影响。提出了一种自动检测电离图中SF的方法。通过检测电离图中SF的存在，我们可以帮助识别可能影响高频无线电波系统的等离子体不规则性。本研究使用了2008-2019年期间秘鲁Jicamarca天文台的电离图图像。已经进行了三种机器学习方法：使用支持向量机的监督学习，以及两种基于神经网络的学习方法：自动编码器和迁移学习。在这三种方法中，使用卷积神经网络架构的迁移学习方法表现出最佳性能。最适合解决这个问题的现有架构似乎是ResNet 50。关于训练历元数，ResNet 50显示了我们跟踪的关键指标的指标值的最大变化。此外，在2050个电离图的测试集上，基于ResNet 50架构的模型提供了89%的准确率，87%的召回率，95%的精确度以及96%的曲线下面积。这项工作还提供了一个约28，000个电离图的标记数据集，这对未来的机器学习研究社区非常有用。
Spread‐F (SF) is a feature that can be visually observed on ionograms when the ionosonde signals are significantly impacted by plasma irregularities in the ionosphere. Depending on the scale of the plasma irregularities, radio waves of different frequencies are impacted differently when the signals pass through the ionosphere. An automated method for detecting SF in ionograms is presented in this study. Through detecting the existence of SF in ionograms, we can help identify instances of plasma irregularities that are potentially affecting the high‐frequency radio‐wave systems. The ionogram images from Jicamarca observatory in Peru, during the years 2008–2019, are used in this study. Three machine learning approaches have been carried out: supervised learning using Support Vector Machines, and two neural network‐based learning methods: autoencoder and transfer learning. Of these three methods, the transfer learning approach, which uses convolutional neural network architectures, demonstrates the best performance. The best existing architecture that is suitable for this problem appears to be the ResNet50. With respect to the training epoch number, the ResNet50 showed the greatest change in the metric values for the key metrics that we were tracking. Furthermore, on a test set of 2050 ionograms, the model based on the ResNet50 architecture provides an accuracy of 89%, recall of 87%, precision of 95%, as well as Area Under the Curve of 96%. The work also provides a labeled data set of around 28,000 ionograms, which is extremely useful for the community for future machine learning studies.