Acoustic and seismic signature of sustained volcanic eruptions

Abstract

Volcanic eruptions of any size can pose a significant risk to the life and livelihood of humans, as well as to infrastructure and the economy. Understanding the dynamics of an eruption is crucial to providing timely and accurate assessments of the eruption and associated hazard. Ideally the monitoring of volcanic unrest and eruption dynamics is done remotely to minimize exposure to volcanologists and maximize the spatial monitoring coverage of instruments. Another important factor is to have real-time data to facilitate rapid analysis and interpretation. Seismic and acoustic (infrasound) waves have proven useful in terms of remote real-time volcano monitoring. However, accurate interpretation of these signals is a challenge due to the complexity of each volcano and each eruption. In this dissertation I present three projects that aim to improve the interpretation of seismic and acoustic signals, specifically their spectral properties, generated by multiphase flow during an eruption. In Chapter 2 we derive a seismic tremor model for a source underground but above the fragmentation level where the gas and particles rush through the volcanic conduit. This physical model assumes ash particles and gas turbulence impact the conduit wall and exert a force that generates seismic waves and is recorded as eruption tremor. We show that it is possible to model the seismic spectral amplitude and shape of a large sustained volcanic eruption, the eruption of Pavlof Volcano in 2016, with particle impacts and turbulence as seismic sources. Our modeling provides a framework to 1) narrow down the parameters associated with eruption dynamics and source processes, and 2) highlight that seismic amplitude and mass eruption rate are not necessarily correlated. Both findings are crucial for the successful interpretation of seismic data during a sustained eruption. In Chapter 3 we move further up above the vent to investigate the acoustic expression of sustained eruptions. The rapid discharge of the multiphase flow through the relatively small vent has been successfully compared to jetting in the past. We develop an algorithm to automatically fit laboratory-derived jet noise spectral shapes (similarity spectra) to the spectrum of three volcanic eruptions: Mount St. Helens 2005, Tungurahua 2006 and Kīlauea 2018. Our quantitative analysis of the misfit between the jet noise spectra and volcanic eruption shows that: 1) the jet noise spectra show a very good fit during the eruption, so we can assume it produces a volcanic form of jet noise, 2) we can distinguish between non-eruptive noise and eruption by the higher misfit of the former and the lower misfit of the latter, and 3) changes in spectral shape correspond to changes in eruption dynamics, which are highlighted by changes in the misfit in time and frequency space. To further look into how changes in spectral properties correspond to changes in eruption dynamics, Chapter 4 focuses in detail on the eruption of fissure 8 on Kilauea volcano in 2018. With the knowledge that the eruption produced jet noise (Chapter 3) we apply jet noise scaling laws and develop a model that relates the changes in infrasound amplitude and peak frequency to changes in jet velocity and diameter. Our analysis shows that in mid-June the infrasound amplitude peaks and the peak frequency decreases. Our jet noise scaling model explains this change through a decrease in jet velocity and increase in jet diameter. This interpretation fits video observations that show a decrease in lava fountain height and a widening of the fountain base around the same time. Our work demonstrates the potential to estimate lava fountain dimensions from infrasound recordings that could be useful for real-time, remote monitoring.