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

Guan Han Huang, Alexei V. Dmitriev, Chia Hsien Lin, Yu Chi Chang, Mon Chai Hsieh, Enkhtuya Tsogtbaatar, Merlin M. Mendoza, Hao Wei Hsu, Yu Chiang Lin, Lung Chih Tsai, Yung-Hui Li

Research output: Contribution to journalArticlepeer-review

Abstract

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 and 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.

Original languageEnglish
Article numbere2022RS007471
JournalRadio Science
Volume57
Issue number8
DOIs
StatePublished - Aug 2022

Keywords

  • deep learning
  • image segmentation
  • ionosonde
  • ionosphere

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