Machine Learning Techniques to Detect and Characterise Whistler Radio Waves

• URL: http://arxiv.org/abs/2002.01244v1
• Date: Tue, 4 Feb 2020 12:05:44 GMT
• Title: Machine Learning Techniques to Detect and Characterise Whistler Radio Waves
• Authors: Othniel J.E.Y. Konan, Amit Kumar Mishra, Stefan Lotz
• Abstract summary: VLF antenna receivers can be used to detect whistler waves generated by lightning strokes. The identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real-time. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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