Machine Learning and Point Processes for Insights into Earthquakes and Volcanoes

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Recent advances in the processing of seismic data have resulted in a drastic increase in the size of earthquake catalogs available globally. This data as well as the field of Seismology, however, has gone relatively unnoticed by the machine learning community. In my PhD I aim to bridge this gap through an investigation into what machine learning methods can be applied to the task of earthquake forecasting. More specifically, since earthquake events are generally modelled as a point process, I will look at developing new methodology for the learning of point processes. This will begin with an investigation into whether neural point process models can better capture the real patterns of earthquakes than the most up to date parametric model, the Epidemic-Type Aftershock Sequence model. Further to developing the methodology of point processes, I seek to create software that the seismology community can use in practice in an attempt to reduce the impact on human lives caused seismic hazards.