Machine Learning for Real Time Volcano-Seismic Monitoring.

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Seismic data analysis is central to most volcano monitoring operations, giving insight into the internal structure and physical processes occurring at depth and enabling the identification of potential eruption precursors which are not visible form the surface. A critical challenge in seismic monitoring is detecting seismic signals (events) from background noise and classifying them based on the physical mechanisms that generate them. At most observatories this classification is still undertaken manually by teams of analysts, making it unfeasible to generate comprehensive volcano seismic catalogues in real-time during periods of unrest when 1000's of seismic events can occur each day. Machine learning (ML) and deep learning (DL) methods have received much attention in recent decades for addressing such 'big data' problems, demonstrating remarkable abilities to rapidly extract patterns and classify data in an automated fashion with high accuracy. However, current applications of ML to address volcano seismic event detection and classification are limited by several factors including their inability to maintain high accuracy over time and generalize between locations. This PhD will investigate novel approaches to develop a more robust generalized ML model for volcano seismic event detection and classification which is grounded in geophysical principles, including the development of a multi-volcano training and benchmarking datasets, and physics informed augmentation and synthetic data production.