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The development of Spatial Attention U-Net for the recovery of ionospheric measurements and extraction of ionospheric parametersстатья
- Авторы: Huang G.H., Dmitriev A.V., Lin C.H., Chang Y.C., Hsieh M.C., Tsogtbaatar E.
- Журнал: Radio Science
- Том: 57
- Год издания: 2022
- Издательство: American Geophysical Union
- Местоположение издательства: United States
- Номер статьи: e2022RS007471
- DOI: 10.1029/2022RS007471
- Аннотация: We train a deep learning artificial neural network model, Spatial Attention U-Net, to recoveruseful ionospheric signals from noisy ionogram data measured by Hualien's Vertical Incidence PulsedIonospheric Radar. Our results show that the model can well identify F2 layer ordinary and extraordinarymodes (F2o and F2x) and the combined signals of the E layer (ordinary and extraordinary modes and sporadicEs). 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.
- Добавил в систему: Дмитриев Алексей Владимирович