J. Space Weather Space Clim.
Volume 9, 2019
System Science: Application to Space Weather Analysis, Modelling, and Forecasting
|Number of page(s)||11|
|Published online||08 May 2019|
A machine learning approach for automated ULF wave recognition
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Metaxa and Vas. Pavlou St., Penteli, 15236 Athens, Greece
2 Space Applications & Research Consultancy, SPARC, 8 Kleissovis str., 106 77 Athens, Greece
3 Department of Physics, National and Kapodistrian University of Athens, Panepistimiopolis, Zografos, 15784 Athens, Greece
4 European Space Research & Technology Centre, ESTEC, Keplerlaan 1, Postbus 299, 2200 AG Noordwijk, The Netherlands
* Corresponding author: email@example.com
Accepted: 17 March 2019
Machine learning techniques have been successfully introduced in the fields of Space Physics and Space Weather, yielding highly promising results in modeling and predicting many disparate aspects of the geospace environment. Magnetospheric ultra-low frequency (ULF) waves can have a strong impact on the dynamics of charged particles in the radiation belts, which can affect satellite operation. Here, we employ a method based on Fuzzy Artificial Neural Networks in order to detect ULF waves in the time series of the magnetic field measurements on board the low-Earth orbit CHAMP satellite. The outputs of the method are validated against a previously established, wavelet-based, spectral analysis tool, that was designed to perform the same task, and show encouragingly high scores in the detection and correct classification of these signals.
Key words: machine learning / ULF waves / LEO satellites
© G. Balasis et al., Published by EDP Sciences 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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