We will present signal processing techniques based on shape analysis of the time-frequency signatures of chorus elements in the Van Allen Radiation Belts. Specifically, we will employ the Radon transform and the Distance Transform, which are well-known for isolating local shapes and patterns in the image processing literature, to achieve automated detection of the spectral morphology of chorus elements. In particular, we will introduce “shrink-wrapping” techniques that quantify the salient features defining the microstructure of chorus elements. We will present preliminary results based on case studies of the EMFISIS data from the Van Allen Radiation Belts mission.

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.