Insights into Magma Storage Beneath a Frequently Erupting Arc Volcano (Villarrica, Chile) from Unsupervised Machine Learning Analysis of Mineral Compositions

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: http://doi.org/10.1029/2022GC010333. This is version 1 of this Preprint.

Downloads

Download Preprint

Authors

Felix Boschetty, David Ferguson, Joaquín Cortés, Eduardo Morgado, Susanna Ebmeier, Daniel Morgan, Jorge Romero, Carolina Silva Parejas

Abstract

A key method to investigate magma dynamics is the analysis of the crystal cargoes carried by erupted magmas. These cargoes may comprise crystals that crystallize in different parts of the magmatic system (throughout the crust) and/or different times. While an individual eruption likely provides a partial view of the sub-volcanic plumbing system, compiling data from multiple eruptions builds a picture of the whole magmatic system. In this study we use machine learning techniques to analyze a large (>2000) compilation of mineral compositions from a highly active arc volcano: Villarrica, Chile. Villarrica's post-glacial eruptive activity (14 ka–present) displays large variation in eruptive style (mafic ignimbrites to Hawaiian effusive eruptions) yet its eruptive products have a near constant basalt-basaltic andesite bulk-rock composition. What, therefore, is driving explosive eruptions at Villarrica and can differences in storage dynamics be related to eruptive style? We used hierarchical cluster analysis to detect previously undetected structure in olivine, plagioclase and clinopyroxene compositions, revealing the presence of compositionally distinct clusters. Using rhyolite-MELTS thermodynamic modeling we related these clusters to intensive magmatic variables: temperature, pressure, water content and oxygen fugacity. Our results provide evidence for the existence of multiple discrete (spatial and temporal) magma reservoirs beneath Villarrica where melts differentiate and mix with incoming more primitive magma. The compositional diversity of an erupted crystal cargo strongly correlates with eruptive intensity, and we postulate that mixing between primitive and differentiated magma drives explosive activity at Villarrica.

DOI

https://doi.org/10.31223/X54W78

Subjects

Geochemistry, Geology, Volcanology

Keywords

Unsupervised Machine Learning, Crystal Cargoes, Thermodynamic Modeling, Magma Mixing, Large Mafic Ignimbrites, Villarrica, Crystal Cargoes, Thermodynamic Modelling, magma mixing, Large Mafic Ignimbrites, Villarrica

Dates

Published: 2022-01-12 04:28

Last Updated: 2022-01-12 12:28

License

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.