This is a Preprint and has not been peer reviewed. This is version 1 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 H$_2$O and CO$_2$ 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 $\mathrm{CO_3^{2-}}$ and $\mathrm{H_2O_{m, 1635}}$ 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 H$_2$O, 9\% for CO$_2$). 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 03:42
Last Updated: 2023-11-02 05:18
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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