EGU2020-8796
https://doi.org/10.5194/egusphere-egu2020-8796
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Deep Learning Technique for Automated Detection of ULF Waves in Swarm Time Series

Alexandra Antonopoulou, Constantinos Papadimitriou, Georgios Balasis, Adamantia Zoe Boutsi, Konstantinos Koutroumbas, Athanasios Rontogiannis, and Omiros Giannakis
Alexandra Antonopoulou et al.
  • Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS), National Observatory of Athens (NOA), Greece (alexantonop@gmail.com)

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 (e.g. Cluster, THEMIS, Van Allen Probes and Swarm) are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites, one of the most successful mission for the study of the near-Earth electromagnetic environment, 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 (AI), we are now able to use more robust approaches devoted to automated ULF wave event identification and classification. The goal of this effort is to use a deep learning method in order to classify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network (CNN) 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, and whose building blocks consist of two convolution layers, two pooling layers and a fully connected (dense) layer, aiming to classify ULF wave events in four different categories: 1) Pc3 wave events (i.e., frequency range 20-100 MHz), 2) non-events, 3) false positives, and 4) plasma instabilities. Our primary experiments show promising results, yielding successful identification of more than 95% accuracy. We are currently working on producing larger training/test datasets, by analyzing Swarm data from the mid-2014 onwards, when the final constellation was formed, aiming to construct a dataset comprising of more than 50000 wavelet image inputs for our network.

How to cite: Antonopoulou, A., Papadimitriou, C., Balasis, G., Boutsi, A. Z., Koutroumbas, K., Rontogiannis, A., and Giannakis, O.: A Deep Learning Technique for Automated Detection of ULF Waves in Swarm Time Series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8796, https://doi.org/10.5194/egusphere-egu2020-8796, 2020.

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