Automated Large‐Scale Extraction of Whistlers Using Mask‐Scoring Regional Convolutional Neural Network

Abstract

Abstract Extremely and very low frequency (ELF/VLF) radio waves are generated from a variety of natural geophysical sources. Ground‐based observations often contain signals of interest; however, the signals are typically immersed in a noisy environment due to lightning‐generated sferics and additional anthropogenic sources. Although automated detection algorithms have been employed successfully in the past, extraction of arbitrary and broadband signal classes has been a challenge. In this work, we employ a mask‐scoring regional convolutional neural network (MSRCNN) for automated extraction of whistlers from ground measurements at Palmer station, Antarctica. Statistics of several hundred thousand whistler receptions are evaluated to determine seasonal and diurnal variations at Palmer station along with strong correlations to lightning activity in the conjugate hemisphere. Although MSRCNN has been employed for whistler extraction in this work, the method has can be easily extended to other signal classes including chorus, hiss, and VLF triggered emissions.