Bayesian Monitoring of Seismo-Volcanic Dynamics

Abstract

Methods for volcano monitoring that are based on analysis of geophysical data often rely on deterministic approaches without considering the complex and dynamic nature of volcanic systems. To detect subtle changes within seismic sequences associated with volcanic unrest, specialized workflows for data classification and analysis are required. Here, we present an inference framework based on Bayesian Deep Learning as a probabilistic proxy, which allows monitoring continuous changes in seismic activity at volcanoes. This architecture has been designed and trained to detect and to classify individual earthquake transients from continuous seismic data recorded in volcanic environments. We tested this new framework by analyzing seismic data associated with eruptions at Bezymianny Volcano (Russia) during 2007. Our results demonstrate efficient signal detection and classification accuracy, and effective detection of changes in the volcanic system in the hours preceding eruptive activity. This approach can be extended to other volcanoes and earthquake-prone areas, and demonstrates a new application of deep learning in the field of seismic monitoring.