Real-Time Detection of Volcanic Unrest and Eruption at Axial Seamount using Machine Learning

This is the authors' abstract. We don't add key points for this paper.

Axial Seamount, an extensively instrumented submarine volcano, lies at the intersection of the Cobb-Eickelberg hot spot and the Juan de Fuca Ridge. Since late 2014, the Ocean Observatories Initiative (OOI) has operated a seven-station cabled ocean bottom seismometers (OBS) array that captured Axial's last eruption in April 2015. This network streams data in real-time, facilitating seismic monitoring and analysis for volcanic unrest detection and eruption forecasting. In this study, we introduce a machine learning (ML) based real-time seismic monitoring framework for Axial Seamount. Combining both supervised and unsupervised ML and double-difference techniques, we constructed a comprehensive, high-resolution earthquake catalog and effectively discriminated between various seismic and acoustic events. These signals include earthquakes generated by different physical processes, acoustic signals of lava-water interaction, and oceanic sources such as whale calls. We first built a labeled ML-based earthquake catalog that extends from November 2014 to the end of 2021, and then implemented real-time monitoring and seismic analysis starting in 2022. With rapid determination of high-resolution earthquake locations and the capability to track potential precursory signals and co-eruption indicators of magma outflow, this system may improve eruption forecasting by providing short-term constraints on Axial's next eruption. Furthermore, our work demonstrated an effective application that integrates unsupervised learning for signal discrimination in real-time operation, which could be generalized to other regions for volcanic unrest detection and enhanced eruption forecasting.