Earthquake Forecasting Using Machine Learning and Statistics

Although the occurrence time of individual earthquakes cannot be predicted exactly, statistical models are able to give relatively accurate forecasts for the long-term probability of large earthquakes occurring in particular geographic regions. Such forecasts are useful for risk management, as well as for allowing insurance companies to accurately price their models.

Many statistical forecasting approaches treat earthquakes as a point process which is then fitted to particular earthquake regions. The most well known model is the ETAS (Epidemic Type Aftershock Sequence model). This project will explore and extend the use of ETAS-type models to specific earthquake forecasting scenarios. There are many open problems in this area that include the opportunity to develop new methodology for point processes, and the application of such point processes to a rich variety of spatial datasets.

Entry requirements:

Essential:

• A Bachelor’s degree in Statistics, Mathematics, Physics, Geophysics, Computer Science or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university);

• Strong verbal and written communication skills in English.

Application Procedure:

A formal application for this project can be made here: https://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&edition=2020&id=516

You are advised to contact the supervisor before applying, although this is not essential.