Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akasofu, S.I.: The development of the auroralsubstorm. Planet. Space Sci. 12(4), 273–282 (1964)
Carbary, J.F.: A Kp-based model of auroral boundaries. Space Weather, 3(10) (2005)
Feldstein, Y.I., Starkov, G.V.: Dynamics of auroral belt and polar geomagnetic disturbances. Planet. Space Sci. 15(2), 209–229 (1967)
Starkov, G.V.: Mathematical model of the auroral boundaries. Geomag. Aeron. 34, 331–336 (1994)
Zhang, Y., Paxton, L.J.: An empirical Kp-dependent global auroral model based on TIMED/GUVI FUV data. J. Atmos. Solar Terr. Phys. 70(8), 1231–1242 (2008)
Milan, S.E., Hutchinson, J., Boakes, P.D., Hubert, B.: Influences on the radius of the auroral oval. Annales Geophysicae 27(7), 2913–2924 (2009). Copernicus GmbH
Lukianova, R., Kozlovsky, A.: Dynamics of polar boundary of the auroral oval derived from the IMAGE satellite data. Cosm. Res. 51(1), 46–53 (2013)
Yang, Q.J.: Auroral Events Detection and Analysis Based on ASI and UVI Images. Ph.D. thesis (2013)
Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Dong, Y., Li, D.: Deep learning and its applications to signal and information processing. IEEE Signal Process. Mag. 28, 145–154 (2011)
Bengio, Y.: Learning deep architectures for AI. Mach. Learn. 2(1), 1–127 (2009)
Liu, H., Gao, X., Han, B., Yang, X.: An automatic MSRM method with a feedback based on shape information for auroral oval segmentation. In: Sun, C., Fang, F., Zhou, Z.-H., Yang, W., Liu, Z.-Y. (eds.) IScIDE 2013. LNCS, vol. 8261, pp. 748–755. Springer, Heidelberg (2013)
Lukaszyk, S.: A new concept of probability metric and its applications in approximation of scattered data sets. Comput. Mech. 33, 299–304 (2004)
Hinton, G.: A Practical Guide to Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)
OMNI dataset description: http://omniweb.gsfc.nasa.gov/html/HROdocum.html.
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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. https://doi.org/10.1007/978-3-319-23862-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-23862-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23861-6
Online ISBN: 978-3-319-23862-3
eBook Packages: Computer ScienceComputer Science (R0)