Quantifying the Timing and Controls of Magmatic Processes Associated with Volcanic Eruptions

Volcanic eruptions can be serious geologic hazards, and have the potential to effect human life, infrastructure, and climate. Therefore, an understanding of the evolution and conditions of the magmas stored beneath volcanoes prior to their eruption is crucial for the ability to monitor such systems and develop effective hazard mitigation plans. This dissertation combines classic petrologic tools such as mineral chemistry and thermometry with novel techniques such as diffusion chronometry and statistical modeling in order to better understand the processes and timing associated with volcanic eruptions. By examining zoned crystals from the fallout ash of Yellowstone’s most recent supereruption, my work shows that the rejuvenation of magma has the ability to trigger a catastrophic supereruption at Yellowstone caldera in the years (decades at most) prior to eruption. This provides one of the first studies to thoroughly identify a specific eruption trigger of a past eruption using the crystal record. Additionally, through experimental investigation, I created a novel diffusion chronometer with application to determine magmatic timescales in silicic volcanic systems (i.e., rhyolite/dacite). My results show that Mg-in-sanidine diffusion operates simultaneously by both a fast and slow diffusion path suggesting that experimentally-derived diffusion chronometers may be more complex than previously thought. When applying Mg-in-sanidine chronometry to zoned sanidine from the same supereruption at Yellowstone, the timing between rejuvenation and eruption is further resolved to as short as five months, providing a greater understanding of the timing of supereruption triggers. Additionally, I developed a new statistical model to examine the controls on a single volcano’s distribution of eruptions through time, therefore the controls on the timing between successive eruptions, or repose time. When examining six Cascade volcanoes with variable distribution patterns through time, my model shows these distributions are not result of sampling bias, rather may represent geologic processes. There is a robust negative correlation between average repose time and average magma composition (i.e., SiO2), suggesting this may be a controlling factor of long-term repose time at Cascade volcanoes. Together, my work provides a better vision for forecasting models to mitigate potential destruction.