Day 4

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Paper title Machine Learning Techniques for Automated ULF Wave Recognition in Swarm Time Series
  1. Georgios Balasis National Observatory of Athens Speaker
  2. Alexandra Antonopoulou National Observatory of Athens
  3. Constantinos Papadimitriou National Observatory of Athens (NOA), Athens, Greece
  4. Zoe Adamantia Boutsi National Observatory of Athens
  5. Omiros Giannakis National Observatory of Athens (NOA), Athens, Greece
Form of presentation Poster
  • B2. Earth Explorer missions
    • B2.05 Swarm - ESA's Extremely Versatile Magnetic Field and Geospace Explorer
Abstract text Machine learning (ML) techniques have been successfully introduced in the fields of Earth Observation, Space Physics and Space Weather, yielding highly promising results in modeling and predicting many disparate aspects of the Earth system. Magnetospheric ultra-low frequency (ULF) waves play a key role in the dynamics of the near-Earth electromagnetic environment 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. Here, we present results employing various neural networks (NNs) methods (e.g. Fuzzy Artificial Neural Networks, Convolutional Neural Networks) in order to detect ULF waves in the time series of low-Earth orbit (LEO) satellites. The outputs of the methods are compared against other ML classifiers (e.g. k-Nearest Neighbors (kNN), Support Vector Machines (SVM)), showing a clear dominance of the NNs in successfully classifying wave events.