论文标题
空间注意力U-NET的发展，用于恢复电离层测量和开采电离层参数
The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters
论文作者
论文摘要
我们训练一个深度学习的人工神经网络模型，空间注意力U-net，从通过Hualien的垂直发病率脉冲电离层雷达测量的嘈杂的离子图数据中恢复有用的电离层信号。我们的结果表明，该模型可以很好地识别F2层普通和非凡的模式（F2O，F2X）以及E层的组合信号（普通和非凡模式以及零星的ES）。该模型还能够识别一些未标记的信号。模型的性能可以通过数据集中的样本数量不足来显着降低。从恢复的信号中，我们确定F2O和F2X的临界频率以及两个信号之间的相交频率。两个临界频率之间的差异为0.63 MHz，不确定性为0.18 MHz。
We train a deep learning artificial neural network model, Spatial Attention U-Net to recover useful ionospheric signals from noisy ionogram data measured by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that the model can well identify F2 layer ordinary and extraordinary modes (F2o, F2x) and the combined signals of the E layer (ordinary and extraordinary modes and sporadic Es). The model is also capable of identifying some signals that were not labeled. The performance of the model can be significantly degraded by insufficient number of samples in the data set. From the recovered signals, we determine the critical frequencies of F2o and F2x and the intersection frequency between the two signals. The difference between the two critical frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.
