Developing a Neural Network for the Identification of EMIC Wave Events

Abstract

A supervised convolutional neural network (CNN) was developed to automatically identify electromagnetic ion cyclotron (EMIC) wave events from spectrograms. These events have usually been identified manually, which can be a time-consuming process. Statistical analyses of larger datasets would be facilitated if this process were simplified. The neural network model was trained on spectrogram images from the Halley magnetometer station that had been manually identified as either containing or not containing an EMIC wave event anywhere in the spectrogram. This model was tested on an unseen set of spectrograms, achieving a perfect true positive rate of 1. Size, time, frequency, and pixel color information was extracted from each identified event and exported into a spreadsheet for easier analysis. This method has the capability of reducing time and effort required to identify important spectrogram features by hand. Such an automated method could be applied to other space weather data stored in spectrograms.