Statistical analysis of earthquake recurrence and its input into seismic hazard assessments is of interest to both the insurance and disaster management industries. This is typically based on probability models for the expected time between earthquakes in a particular region. Building these statistical models is complicated by the limited number of events in historical records.
Many current approaches resolve this by combining earthquake histories of multiple regions into a single catalogue. However, more sophisticated methods for combining information across multiple regions and coping with limited data in a more principled and flexible manner are needed.
This project will focus on Bayesian hierarchical modelling to allow natural pooling of information across related regions, along with machine learning/artificial intelligence approaches to gain insight into earthquake recurrence and the variability in seismic cycles. (No previous experience in these methods is required).