Machine learning thermobarometry and chemometry using amphibole and clinopyroxene: a window into the roots of an arc volcano (Mount Liamuiga, Saint Kitts)

Oliver Higgins; Tom Sheldrake; Luca Caricchi

doi:10.1007/s00410-021-01874-6

Machine learning thermobarometry and chemometry using amphibole and clinopyroxene: a window into the roots of an arc volcano (Mount Liamuiga, Saint Kitts)

Contributions to Mineralogy and Petrology ◽

10.1007/s00410-021-01874-6 ◽

2021 ◽

Vol 177 (1) ◽

Author(s):

Oliver Higgins ◽

Tom Sheldrake ◽

Luca Caricchi

Keyword(s):

Machine Learning ◽

Arc Volcano

Download Full-text

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

10.31223/x54w78 ◽

2022 ◽

Author(s):

Felix Boschetty ◽

David Ferguson ◽

Joaquín Cortés ◽

Eduardo Morgado ◽

Susanna Ebmeier ◽

...

Keyword(s):

Machine Learning ◽

Hierarchical Cluster ◽

Machine Learning Techniques ◽

Magmatic System ◽

Rock Composition ◽

Eruptive Style ◽

Volcanic Plumbing System ◽

Mineral Compositions ◽

Compositional Diversity ◽

Arc Volcano

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.

Download Full-text

Machine learning thermobarometry and chemometry using amphibole and clinopyroxene: a window into the volcanic roots of an arc volcano (Mount Liamuiga, Saint Kitts)

10.31223/x5gd0w ◽

2021 ◽

Author(s):

Oliver Higgins ◽

Tom Sheldrake ◽

Luca Caricchi

Keyword(s):

Machine Learning ◽

Single Phase ◽

Chemical Properties ◽

Progressive Increase ◽

Magma Plumbing System ◽

Eruptive Cycle ◽

Arc Volcano ◽

Physical And Chemical ◽

Magma Plumbing ◽

And Chemical Properties

The physical and chemical properties of magma govern the eruptive style and behaviour of volcanoes. Many of these parameters are linked to the storage pressure and temperature of the erupted magma, and melt chemistry. However, reliable single-phase thermobarometers and chemometers which can recover this information, particularly using amphibole chemistry, remain elusive. We present a suite of single-phase amphibole and clinopyroxene thermobarometers and chemometers, calibrated using machine learning. This approach allows us to intimately track the range of pre-eruptive conditions over the course of a millennial eruptive cycle on an island arc volcano (Saint Kitts, Eastern Caribbean). We unpick the story of Mount Liamuiga, a stratovolcano that pops its upper-crustal (2 kbar), dacitic cork at the beginning of the Lower Mansion Series eruptive sequence. This permits a progressive increase in the thermal maturity of the magma arriving at the surface from the middle to upper crust (2 – 5.5 kbar) through time. The temperature increase correlates well with matrix plagioclase chemistry, which itself displays a remarkable progression to less evolved (more anorthitic) composition in time. We find that amphibole is a reliable themobarometer (SEE = 1.4 kbar; 40 ˚C), at odds with previous studies. We suggest it is the regression strategy, as opposed to the abject insensitivity to pressure, that has hindered previous calibrations of amphibole only thermobarometers. By recognising this, we have constructed a high-resolution, quantitative picture of the magma plumbing system beneath an arc volcano.

Download Full-text

Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Download Full-text