Linking degassing and mass eruption rates, conduit modelling and petrological records to quantify different eruptive styles and processes at basaltic volcanoes
About the Project
Analysis of remote sensing imagery of SO2 emissions using trajectory methods provides time series of gas emission rates and plume height during explosions, with sub-hour temporal resolution. This provides two estimates of mass eruption rate (MER) at the vent, one from assuming a certain sulphur concentration degassing from the erupting magma, and one from the height of plume, using empirical relationships between eruption rate and plume height. However, the amount of sulphur which is dissolved in ascending magmas may vary due to magma reservoir dynamics, and the plume height can be affected by varying temperature of the emitted gas phase. Investigating where the gas flux derived MER and plume height derived MER agree and disagree between eruptions from the same volcano and different volcanoes provides insights into the dynamics of the eruption and the plume.
Quantified time-dependent mass eruption rates are a key constraint, along with petrological, rheological and volatile information, on the dynamics of magma ascent in conduit models, which link magma reservoirs to the surface. Conduit models therefore allow the dynamics of magma ascent to be quantified, providing direct links to observations of petrological features of the erupted products. Crystal size, composition and zonation all reflect magma storage and ascent processes, allowing comparison between observed crystal features and those predicted by the conduit model. Fully quantitative petrological modelling may be achieved by comparing models and observations and refining petrological and crystal growth models.
This project focusses initially on Etna, where a wealth of data exists for both lava fountain events measured from space and petrological studies of eruption products. Further studies will be conducted on diverse basaltic systems as the project proceeds.
This project is ideally suited to a quantitative scientist, with strong background in numerical approaches, Python programming, physics and chemistry. Experience in volcanology is desirable but not essential, volcanology training will be delivered during the PhD.
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