Volcanoes are dangerous and complex with processes coupled to both the subsurface
and atmosphere. Effective monitoring of volcanic behavior during and in between periods
of crisis requires a diverse suite of instruments and processing routines. Acoustic
microphones and video cameras are typical in long-term deployments and provide important
constraints on surficial and observational activity yet are underutilized relative to
their seismic counterpart. This dissertation increases the utility of infrasound and video
datasets through novel applications of computer vision and machine learning algorithms,
which help constrain source dynamics and track shifts in activity. Data analyzed come from
infrasound and camera installations at Stromboli Volcano, Italy and Villarrica Volcano, Chile
and are diverse in terms of the recorded activity. At Villarrica, a computer vision algorithm
quantifies video data into a set of characteristic features that are used in a multiparametric
analysis with seismic and infrasound data to constrain activity during a period of crisis in
2015. Video features are also input into a machine learning algorithm that classifies data into
five modes of activity, which helps track behavior over weekly and monthly time scales. At
Stromboli, infrasound signals radiating from the multiple active vents are synthesized into
characteristic features and then clustered via an unsupervised learning algorithm. Time histories
of cluster activity at each vent reveal concurrent shifts in behavior that suggest a linked plumbing
system between the vents. The algorithms presented are general and modular and can be implemented
at monitoring agencies that already collect acoustic and video data.
Volcano video data characterized and classified using computer vision and machine learning algorithms
