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Autonomous detection of whistler-mode chorus elements in the Van Allen radiation belts using morphological signal processing and pattern recognition techniques
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  • Ananya Sengupta,
  • Ryan McCarthy,
  • Kawther Rouabhi,
  • Craig Kletzing,
  • Ivar Christopher
Ananya Sengupta
University of Iowa

Corresponding Author:ananya-sengupta@uiowa.edu

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Ryan McCarthy
University of Iowa
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Kawther Rouabhi
University of Iowa
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Craig Kletzing
University of Iowa
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Ivar Christopher
University of Iowa
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Abstract

Autonomous large-scale detection of whistler-ode chorus elements in the Van Allen radiation belts has been an open computational challenge. This is primarily due to: (i) Variability of the spectral morphology of chorus elements, and (ii) Structured background interference from hiss-like chorus that can make elements difficult to detect using traditional signal processing and pattern recognition techniques. We will present computational techniques drawing on pixel connectivity, signal-to-noise (SNR) considerations as well as supervised and unsupervised pattern recognition techniques. Specifically, we will explore the efficacy of popular machine learning techniques trained on unfiltered spectral images versus those trained on culled features generated by unsupervised feature extraction techniques. Representative results will be presented based on magnetic field measurements taken by the EMFISIS instrument suite in the Van Allen probes mission.