Machine Learning for Geophysical Hazard Sequences

Many geophysical hazards (volcanic eruptions, large earthquakes, landslides) have sequences of observable events that precede them. Experts often use these precursors to make forecasts, but many precursors are not recognised until after the hazardous event.
As ML techniques grow in popularity and capability, they are increasingly applied to a diverse range of problems. With population growth and climate change, more people are at risk from extreme geophysical hazards. This puts pressure on scientists and authorities to devise and implement more effective mitigation actions. Forecasting is a major part of mitigation strategies. It is time to investigate the benefits and insights that ML techniques can bring.
The student will familiarise themselves with geohazard monitoring data, instrument networks and event sequences through literature review and discussions with experienced scientists within and beyond the supervisory team. They will become accustomed to ML algorithms such as: time-series classification and neural networks to identify patterns; dimensionality reduction algorithms to identify the most important data; and ensemble methods to evaluate the best monitoring networks. The student will train algorithms with historic data sets and compare these to expert opinions. Algorithms will be important resources for the community. All outputs will be new data.
Risks for the project are low. GNS Science has one of the most advanced and comprehensive monitoring networks in the world, coupled with the abundance of geohazardous events in NZ, provides an abundance of data. All data are available from geonet.org.nz. There are already examples of successful ML use in this area, and all results will be novel and interesting, e.g. If the project confirms that a certain known pattern is useful for forecasting, finds a new pattern, or suggests that there are no useful patterns, these results will be useful for the discipline, and for risk mitigation.
This project includes supervisors from four different schools/institutes. This is a strength of the project as the student will learn from a range of experts, and experience different working and learning environments, as well as making contacts with a wider range of professionals. The student will be based in UEA ENV, where they will be part of a strong geohazards group. The student will benefit from cutting-edge, diverse, and cross-disciplinary geohazards research. The student will also take part in research group activities in the other institutions included in the supervisory team, broadening their support network further. At UEA CMP they will be trained in the design, implementation, and use of ML techniques. The project will include time at GNS Science, NZ, where the student will experience working within a government agency, real-time monitoring of geophysical hazards, and learn from experts who have lifetime's' worth of tacit knowledge.
The supervisors represent expertise from all aspects of the project, providing the student with opportunity to explore and specialise in the parts of the project that interest them most. The multidisciplinary nature of the supervisory team will provide the student with the ability to think across discipline boundaries, preparing them for any future career.