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ULF wave investigations at Swarm altitudes using machine learning techniques

Alexandra Antonopoulou - National Observatory of Athens; Constantinos Papadimitriou - National Observatory of Athens, Greece; George Balasis - National Observatory of Athens, Greece; Zoe Boutsi - National Observatory of Athens, Greece; Omiros Giannakis - National Observatory of Athens, Greece; Konstantinos Koutroumbas - National Observatory of Athens, Greece; Athanasios Rontogiannis - National Observatory of Athens, Greece

Earth's Magnetosphere and Radiation Belts


Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence, we are now able to use more robust approaches for automated ULF wave identification and classification. The goal of this effort is to use a deep learning technique to classify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network that takes as input the wavelet spectra of the Earth’s magnetic field variations per track, as measured by each one of the three Swarm satellites, aiming to classify ULF wave events in four categories: Pc3 wave events, non-events, false positives, and plasma instabilities. Our primary experiments show promising results, yielding successful identification of more than 95% accuracy. We are currently working on producing larger datasets, by analyzing Swarm data from mid-2014 onwards, when the final constellation was formed.



Approaching deadlines:

Registration opens:

16 July 2020

Abstract submission opens:

16 July 2020

European Space Weather Medals:

6 September 2020

Registration deadline:

25 September 2020

Registration deadline: [extended]

10 October 2020

Abstract submission deadline:

4 September 2020