  Lightning strokes create powerful electromagnetic pulses that routinely cause
very low frequency (VLF) waves to propagate across hemispheres along
geomagnetic field lines. VLF antenna receivers can be used to detect these
whistler waves generated by these lightning strokes. The particular
time/frequency dependence of the received whistler wave enables the estimation
of electron density in the plasmasphere region of the magnetosphere. Therefore
the identification and characterisation of whistlers are important tasks to
monitor the plasmasphere in real-time and to build large databases of events to
be used for statistical studies. The current state of the art in detecting
whistler is the Automatic Whistler Detection (AWD) method developed by
Lichtenberger (2009). This method is based on image correlation in 2 dimensions
and requires significant computing hardware situated at the VLF receiver
antennas (e.g. in Antarctica). The aim of this work is to develop a machine
learning-based model capable of automatically detecting whistlers in the data
provided by the VLF receivers. The approach is to use a combination of image
classification and localisation on the spectrogram data generated by the VLF
receivers to identify and localise each whistler. The data at hand has around
2300 events identified by AWD at SANAE and Marion and will be used as training,
validation, and testing data. Three detector designs have been proposed. The
first one using a similar method to AWD, the second using image classification
on regions of interest extracted from a spectrogram, and the last one using
YOLO, the current state of the art in object detection. It has been shown that
these detectors can achieve a misdetection and false alarm of less than 15% on
Marion's dataset.
