This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.30909/vol.07.02.471501. This is version 3 of this Preprint.
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Abstract
Quantifying volatile concentrations in magmas is critical for understanding magma storage, phase equilibria, and eruption processes. We present PyIRoGlass, an open-source Python package for quantifying H2O and CO2 species concentrations in the transmission FTIR spectra of basaltic to andesitic glasses. We leverage a database of naturally degassed melt inclusions and back-arc basin basalts to delineate the fundamental shape and variability of the baseline underlying the CO32- and H2Om,1635 peaks, in the mid-infrared region. All Beer-Lambert Law parameters are examined to quantify associated uncertainties. PyIRoGlass employs Bayesian inference and Markov Chain Monte Carlo sampling to fit all probable baselines and peaks, solving for best-fit parameters and capturing covariance to offer robust uncertainty estimates. Results from PyIRoGlass agree with independent analysis of experimental devolatilized glasses (within 6%) and interlaboratory standards (13% for H2O, 9% for CO2). We determine new molar absorptivities for basalts, εH2Ot,3550=63.03±4.47 L/mol·cm and εCO32-1515, 1430=303.44±9.20 L/mol·cm; we additionally update the composition-dependent parameterizations of molar absorptivities, with their uncertainties, for all H2O and CO2 species peaks. The open-source nature of PyIRoGlass ensures its adaptability and evolution as more data become available.
DOI
https://doi.org/10.31223/X55H4N
Subjects
Earth Sciences, Geochemistry, Other Earth Sciences, Physical Sciences and Mathematics, Volcanology
Keywords
volatiles, FTIR, Open-source, python, Bayesian, Markov chain Monte Carlo
Dates
Published: 2023-11-02 08:42
Last Updated: 2024-07-26 15:40
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License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None
Data Availability (Reason not available):
https://github.com/sarahshi/PyIRoGlass
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