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- W4248702900 abstract "Abstract. The auroral oval boundary represents important physical process with implications for the ionosphere and magnetosphere. An automatic auroral oval boundary prediction method based on deep learning in this paper are applied to study the variation of auroral oval boundary, associated with different space physical parameters. We construct an auroral oval boundary dataset to train our proposed model, which consists of 184416 auroral oval boundary points extracted from 3842 UVI images captured by Ultraviolet Imager of the Polar satellite and its corresponding 18 space physical parameters selected from OMNI dataset during December 1996 to March 1997. Furthermore, several statistical experiments and correlation analysis experiment are performed based on our dataset to explore the relationship between space physical parameters and the location of auroral oval boundary. The experiment results show that the prediction model based on deep learning method could estimate auroral oval boundary efficiently, and different space physical parameters have different effects on auroral oval boundary, especially interplanetary magnetic field (IMF), geomagnetic indexes and solar wind parameters." @default.
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- W4248702900 date "2019-06-05" @default.
- W4248702900 modified "2023-10-16" @default.
- W4248702900 title "Prediction and variation of auroral oval boundary based on deep learning model and space physical parameters" @default.
- W4248702900 doi "https://doi.org/10.5194/npg-2019-28" @default.
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