The Development of Spatial Attention U-Net for The Recovery of Ionospheric Measurements and The Extraction of Ionospheric Parameters

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 extraordina...

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Bibliographic Details
Published in:arXiv.org 2022-09
Main Authors: Guan-Han, Huang, Dmitriev, Alexei V, Lin, Chia-Hsien, Yu-Chi, Chang, Mon-Chai Hsieh, Tsogtbaatar, Enkhtuya, Mendoza, Merlin M, Hao-Wei, Hsu, Yu-Chiang, Lin, Tsai, Lung-Chih, Yung-Hui, Li
Format: Article
Language:English
Subjects:
Artificial neural networks
Critical frequencies
F 2 region
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2209.07581