A Specific Earthquake Processing Workflow for Studying Long-Lived, Explosive Volcanic Eruptions With Application to the 2008 Okmok Volcano, Alaska, Eruption

By providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high-noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single-channel template matching combined with machine-learning and fingerprint-based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high-resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.

Automated summary

Volcano seismicity provides unparalleled resolution to chronicle and interpret eruptions, but standard earthquake detection methods are often insufficient due to continuous seismic waves produced by eruptions. To overcome this problem, we developed a specific earthquake processing workflow for high-noise volcanic environments and applied it to the 2008 Okmok Volcano eruption. By using single-channel template matching, machine learning, and fingerprint-based techniques, we expanded the existing earthquake catalog, detected and located more earthquakes, determined magnitudes, and classified events. This new high-resolution earthquake catalog increased observations by a factor of 10 and enabled detailed spatiotemporal seismic analysis during a large eruption.