Statistically plausible eruption scenarios determined from geochemical data
I’m from Mexico where I lived close enough to Popocatepetl to see it in action, which made me fascinated about volcanoes. I studied a Bachelor’s Degree in Actuarial Science at University La Salle in Mexico City and a Master’s Degree in Statistics at Massey University, New Zealand. I always wanted to apply my skills to a topic that would benefit people and after hearing about my current project in a topic that amazed me, I couldn’t let it go.
It is generally agreed that the magma composition and monitoring signals are linked to eruption explosivity. However, quantitative linkages between geochemical and monitoring data and eruption style remain ambiguous. The purpose of my PhD project at Massey University is to identify these possible quantitative relationships and key parameters, and the robustness of their correlations. For this I am using machine learning models and statistical techniques.
By identifying these key parameters, I’m hoping that the development of forecasting models that include the style and size of eruption will greatly improve and that ultimately people will benefit from this work through decisions based on better-informed forecasts. Currently, I’m working on my first paper “Geochemistry: What can it tell us about eruption explosivity?” and looking through monitoring data from volcanoes across the world to be able to code them in a way that is meaningful, but still useful in a quantitative way.