This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: http://doi.org/10.1007/s00410-021-01874-6. This is version 2 of this Preprint.

Machine learning thermobarometry and chemometry using amphibole and clinopyroxene: a window into the roots of an arc volcano (Mount Liamuiga, Saint Kitts)
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Abstract
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... more
DOI
https://doi.org/10.31223/X5GD0W
Subjects
Earth Sciences, Geochemistry, Mineral Physics, Stratigraphy, Volcanology
Keywords
stratigraphy, anorthite, single-phase, Mansion Series, compositional gap
Dates
Published: 2021-07-05 02:46
Last Updated: 2021-07-05 09:47
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License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None
Data Availability (Reason not available):
Upon request to the author
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