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.

The MMS spacecraft routinely observe electron “microinjections” at energies in the 10s-100s keV range across the nightside magnetosphere at distances <20 Re. Microinjections are typically observed in clusters where multiple dispersed injection signatures are recorded in succession over a short time interval (e.g., ~10 in one hour) and may be related to surface wave activity at the magnetopause. Recent work has shown that microinjections have distinctive features in the angular distributions, where field-aligned distributions are observed near dusk, while trapped distributions are observed near dawn. Due to their recent discovery, the origin and generation of microinjections has yet to be conclusively identified and detailed studies thus far have largely been done on a case-by-case basis. In an effort to elucidate more general properties and characteristics of microinjections, we describe an automated routine designed to identify microinjection signatures in the MMS/FEEPS measurements. The algorithm uses image processing techniques (the radon transform) and is based on a similar method developed to identify whistler-mode chorus elements in Van Allen Probes wave observations (Sen Gupta et al., [2017]). We present an initial set of results from a statistical database of microinjection events obtained from the automated algorithm to further our understanding of this intriguing phenomenon.

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.