TY - JOUR
T1 - Insights into Magma Storage Beneath a Frequently Erupting Arc Volcano (Villarrica, Chile) from Unsupervised Machine Learning Analysis of Mineral Compositions.
AU - Boschetty, Felix
AU - Ferguson, David
AU - CORTES CARRILLO, JOAQUIN ALBERTO
AU - Morgado, Eduardo
AU - Ebmeier, Susanna
AU - Morgan, Daniel
AU - Romero, Jorge
AU - Silva Parejas, Carolina
N1 - Publisher Copyright:
© 2022. The Authors.
PY - 2022/4/6
Y1 - 2022/4/6
N2 - 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 at different times. While an individual eruption likely provides a partial view of the subvolcanic plumbing system, compiling data from multiple eruptions can build a picture of a whole magmatic system. In this study, we use machine learning techniques to analyze a large (>2,000) compilation of mineral compositions from a highly active arc volcano: Villarrica, Chile. Villarrica's postglacial eruptive activity (14 ka–present) displays large variation in eruptive style (mafic ignimbrites to Hawaiian style 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? Here, we use a hierarchical cluster analysis to detect previously unseen structure in the composition of olivine, plagioclase and clinopyroxene crystals erupted at Villarrica, revealing the presence of compositionally distinct clusters within each crystal population. 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 within an erupted crystal cargo strongly correlates with eruptive intensity, and we postulate that mixing between primitive and differentiated magma drives explosive activity at Villarrica.
AB - 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 at different times. While an individual eruption likely provides a partial view of the subvolcanic plumbing system, compiling data from multiple eruptions can build a picture of a whole magmatic system. In this study, we use machine learning techniques to analyze a large (>2,000) compilation of mineral compositions from a highly active arc volcano: Villarrica, Chile. Villarrica's postglacial eruptive activity (14 ka–present) displays large variation in eruptive style (mafic ignimbrites to Hawaiian style 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? Here, we use a hierarchical cluster analysis to detect previously unseen structure in the composition of olivine, plagioclase and clinopyroxene crystals erupted at Villarrica, revealing the presence of compositionally distinct clusters within each crystal population. 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 within an erupted crystal cargo strongly correlates with eruptive intensity, and we postulate that mixing between primitive and differentiated magma drives explosive activity at Villarrica.
KW - Villarrica
KW - crystal cargoes
KW - large mafic ignimbrites
KW - magma mixing
KW - thermodynamic modeling
KW - unsupervised machine learning
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U2 - 10.1029/2022GC010333
DO - 10.1029/2022GC010333
M3 - Article (journal)
SN - 1525-2027
VL - 23
SP - 1
EP - 24
JO - Geochemistry, Geophysics, Geosystems
JF - Geochemistry, Geophysics, Geosystems
IS - 4
M1 - e2022GC010333
ER -