Forecasting geophysical hazards using machine learning and VLF radio data

Project description

Very low frequency radio waves from standard radio transmitters can travel vast distances around the Earth. On their way, they collect the information about the medium they propagate through. This information includes noise, regular variations, such as day-night, or seasonal changes, as well as the transient information on large-scale geophysical anomalies and events, such as earthquakes, volcano eruptions, tsunamis. A network of receivers has been created around the Earth to receive those radio waves. Currently, the big question is how to understand and analyse the very low frequency radio wave propagation data in a hope to better understand and forecast geophysical hazards. In this project, we will be looking at ways to answer this big question. We will be using a variety of machine-learning methods for time series analysis on already existing time series in order to find the best ways to post-predict known large-scale geophysical events. Our aim will be to find the signatures of those events in the data before they occurred at the Earth surface. A publication is expected on successful completion of the project. The project would also serve as a solid background for a PhD project.

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You must also contact each supervisor directly to discuss both the project details and your suitability to undertake the project.