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

Higgins, Oliver; Sheldrake, Tom; Caricchi, Luca

doi:10.1007/s00410-021-01874-6

Cited by 56 publications

(67 citation statements)

References 109 publications

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“…We have shown that machine learning is a powerful and versatile approach to thermobarometry in agreement with other studies (Higgins et al., 2022 ; Petrelli et al., 2020 ). Through detailed testing, we have determined models that have an SEE comparable to the leading clinopyroxene thermobarometers (SEE of 3.2 kbar, 47.6°C and 4.4 kbar, 76.0°C for the liquid and no liquid models, respectively, as compared to 3.4 kbar and 125°C for the alkaline only liquid‐cpx models of Masotta et al.…”

Section: Discussionsupporting

confidence: 91%

“…( 2020 ) and Higgins et al. ( 2022 ) have resulted in a machine learning random forest approach to thermobarometry. Both studies omitted several pertinent experimental data sets of clinopyroxene and liquid equilibria, which are now included in the model presented here.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the twofold aim of this work is to (a) build and test the performance of a thermobarometer model for clinopyroxenes and (b) provide a comprehensive explanation of how to apply our thermobarometer for applications to natural data. Our regression strategy offers a generalized model that can be tailored for certain settings, applications, or other suitable mineral phases (e.g., amphibole; Higgins et al., 2022 ). We greatly expand the data set of clinopyroxene and liquid equilibria in our calibration data set compared to previous studies, allowing our calibrations to be as globally applicable and adaptable as possible for users.…”

Section: Introductionmentioning

confidence: 99%

“…This typically leads to data overfitting and an underestimated SEE (Nimis & Taylor, 2000;Putirka, 2008). Recent developments in machine learning applications to petrology by Petrelli et al (2020) and Higgins et al (2022) have resulted in a machine learning random forest approach to thermobarometry. Both studies omitted several pertinent experimental data sets of clinopyroxene and liquid equilibria, which are now included in the model presented here.…”

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confidence: 99%

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A Machine Learning‐Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences

et al. 2022

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Thermobarometry is a fundamental tool to quantitatively interrogate magma plumbing systems and broaden our appreciation of volcanic processes. Developments in random forest‐based machine learning lend themselves to a data‐driven approach to clinopyroxene thermobarometry, allowing users to access large experimental data sets that can be tailored to individual applications in Earth Sciences. We present a methodological assessment of random forest thermobarometry using the R freeware package extraTrees. We investigate the model performance, the effect of hyperparameter tuning, and assess different methods for calculating uncertainties. Deviating from the default hyperparameters used in the extraTrees package results in little difference in overall model performance (<0.2 kbar and <3°C difference in standard error estimate, SEE). However, accuracy is greatly affected by how the final value from the distribution of trees in the random forest is selected (mean, median, or mode). Using the mean value leads to higher residuals between experimental and predicted P and T, whereas using median values produces smaller residuals. Additionally, this work provides two scripts for users to apply the methodology to natural data sets. The first script permits modification and filtering of the model calibration data set. The second script contains premade models, where users can rapidly input their data to recover PT estimates (SEE clinopyroxene‐only model: 3.2 kbar, 72.5°C and liquid‐clinopyroxene model: 2.7 kbar, 44.9°C). Additionally, the scripts allow the user to estimate the uncertainty for each analysis, which in some cases is significantly smaller than the reported SEE. These scripts are open source and can be accessed at https://github.com/corinjorgenson/RandomForest-cpx-thermobarometer.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A Machine Learning‐Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences

et al. 2022

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“…30 and Eq. 31 of Putirka (2008); T estimated using Cpx thermometry of Higgins et al (2021)], Mgt-Ilm thermometry (Evans et al, 2016), Ti-in-Qtz barometry (Zhang et al, 2020), rhyolite-MELTS modeling of cotectic conditions (Gualda & Ghiorso, 2013) and fluid saturation barometry (Wallace et al, 1999). EBT: Early Bishop Tuff; LBT: Late Bishop Tuff.…”

Section: Application To Rhyolitementioning

confidence: 99%

Machine Learning Thermobarometry for Biotite‐Bearing Magmas

Zhang

2022

JGR Solid Earth

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Biotite (sensu lato) is a widespread rock‐forming mineral in magmatic rocks that can be stable in a broad range of pressure and temperature, but appropriate biotite thermometers or barometers are lacking. Based on a collected experimental dataset (n = 839, T = 625–1,325°C, P = 1–48 kbar) containing biotites that span a wide compositional range [e.g., Mg/(Mg + Fe) = 0–1, TiO2 = 0–9 wt%], we have trained several machine learning algorithms for calibrating a biotite thermobarometer. Our evaluation on model performance reveals that the thermobarometry derived from extremely randomized trees is the best option, which returns coefficients of determination (R2) ≥0.97 for estimating both temperature and pressure using either biotite‐only or biotite + melt model. The model reliability were evaluated using three different approaches for the biotite‐only and biotite + melt thermobarometers respectively, including Monte Carlo cross‐validation (RMSEs are 65°C and 4.7 kbar, 38°C and 3.2 kbar, respectively), testing with independent test set (RMSEs are 54°C and 4.4 kbar, 35°C and 2.4 kbar, respectively), and error propagation from assumed analytical uncertainty (2*MAD are 54°C and 1.27 kbar, 10°C and 1.26 kbar, respectively). Quantified relative importance of involved components in the thermobarometers supports an intrinsic control of thermodynamics in the stability of biotite as a function of pressure and temperature. We applied the new biotite thermobarometer for biotite‐bearing andesitic, phonolitic and rhyolitic volcanic systems provide reliable constraints of temperature and pressure for magma storage, ascent, and evolution. We also offer a user‐friendly webpage for online performance of the thermobarometers (https://lixiaoyan.shinyapps.io/Biotite_thermobarometer/).

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