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Auroral Oval Boundary Modeling Based on Deep Learning Method

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9243)


Research on the location of the auroral oval is important to understand the coupling processes of the Sun-Earth system. The equatorward boundary and poleward boundary of the auroral oval are significant parameters of the auroral oval location. Thus auroral oval boundary modeling is an efficient way to study the location of auroral oval. As the location of the auroral oval boundary is subject to a variety of geomagnetic factors, there are some limitations on traditional methods, which express the auroral oval boundary as a function of only one or several geomagnetic activity index. Deep learning method is used in this paper to learn the essential features of the inputs, which are a large number of geomagnetic parameters and the former locations of aurora boundary. Furthermore, a model is established to forecast the location of the auroral oval boundary. The experiment results show that our method can model and forecast the boundary of aurora oval efficiently on the data set obtained from Ultraviolet Imager (UVI) on Polar satellite and OMNI database on NASA.


  • Auroral oval boundary modeling
  • Restricted bolzmann machine
  • Deep learning

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This research is supported by the Special Scientific Research of Marine Public Welfare Industry (201005017), the Basic Foundation for Scientific Research, the Fundamental Research Funds for the Central Universities (K5051302008), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry and the Open Funding of State Key Laboratory of Remote Sensing Science (OFSLRSS201415), the Project Funded by China Postdoctoral Science Foundation.

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Correspondence to Bing Han .

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Han, B., Gao, X., Liu, H., Wang, P. (2015). Auroral Oval Boundary Modeling Based on Deep Learning Method. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham.

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  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

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