Thermobar: An Open-Source Python3 Tool for Thermobarometry and Hygrometry

Wieser, Penny; Petrelli, Maurizio; Lubbers, Jordan; Wieser, Eric; ozaydin, Sinan; Kent, Adam J.R.; Till, C. B.

doi:10.31223/x5fd0k

Cited by 3 publications

(3 citation statements)

References 51 publications

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“…The temperature distribution within each layer was calculated using one‐dimensional re‐sampling methods. Next, we used our estimated P ‐ T data from the mantle xenoliths to calibrate and determine the optimal surface heat flux and geothermal gradient values (Hasterok & Chapman, 2011; Wieser et al., 2022). Lastly, we calculated the depth at which the lithospheric geothermal gradient curve intersects the mantle adiabat with a potential temperature of 1,350°C (Katsura, 2022; Katsura et al., 2010).…”

Section: Methodsmentioning

confidence: 99%

Mapping Global Lithospheric Mantle Pressure‐Temperature Conditions by Machine‐Learning Thermobarometry

Qin,

Ye,

Liu

et al. 2024

Geophysical Research Letters

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Comprehending the temperature distribution within Earth's lithospheric mantle is of paramount importance for understanding the dynamics of Earth's interior. Traditional mineral‐based thermobarometers effectively constrain temperature and pressure for particular compositions, but their application is limited at the global scale. Here, we trained machine‐learning (ML) algorithms on 985 published high‐temperature and high‐pressure experiments for use as thermometers and barometers to overcome the limitations of classic methods. We compared our ML models to classic thermobarometers to assess the accuracies of predicted pressures and temperatures. The comparison shows that the ML models outperform classic methods and better fit various mineral pairs. Global application of the ML models unveils mantle conditions beneath cratons. Furthermore, depths to the lithosphere‐asthenosphere boundary (LAB) calibrated based on the ML thermobarometry results are generally deeper by ∼40 km than those derived geophysically, implying the existence of melt‐bearing or hydrated mineral zones at the LAB.

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Section: Methodsmentioning

confidence: 99%

Mapping Global Lithospheric Mantle Pressure‐Temperature Conditions by Machine‐Learning Thermobarometry

Qin,

Ye,

Liu

et al. 2024

Geophysical Research Letters

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“…parameters are taken from alphaMELTS for MATLAB (that is, the density of the different phases) with the exception of the melt viscosity 57,58 (Fig. 5).…”

Section: Volumetric Flux Calculationsmentioning

confidence: 99%

Porosity evolution of mafic crystal mush during reactive flow

Gleeson¹,

Lissenberg²,

Antoshechkina³

2023

Preprint

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The emergence of the “mush paradigm” has raised several questions for conventional models of magma storage and extraction: how are melts extracted to form eruptible liquid-rich domains? What mechanism controls melt transport in mush-rich systems? Recently, reactive flow has been proposed as a major contributing factor in the formation of high porosity, melt-rich regions. Yet, owing to the absence of accurate geochemical simulations, the influence of reactive flow on the porosity of natural mush systems remains under-constrained. Here, we use a thermodynamically constrained model of melt-mush reaction to simulate the chemical, mineralogical, and physical consequences of reactive flow in a multi-component mush system. Our results demonstrate that reactive flow within troctolitic to gabbroic mushes can drive large changes in mush porosity. For example, primitive magma recharge drives an increase in the system porosity and could trigger melt channelization or mush destabilization, aiding rapid melt transfer through low-porosity mush reservoirs.

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“…Some users may have estimates of Fe 3+ /Fe T from techniques such as XANES, but did not measure S 6+ /S T , so want to use the model of Jugo et al [2010]. The python package Thermobar (Wieser et al [2022]) can be used to convert between f O 2 values, buffer positions and Fe3FeT_Liq ratios. For example, the Petrolog3 output in figure 3 The different buffers stored in the Buffer_calc dataframe can then be input into the PySulfSat function:…”

mentioning

confidence: 99%

PySulfSat: An Open-Source Python3 Tool for modelling sulfide and sulfate saturation

Wieser¹,

Gleeson²

2023

Preprint

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We present PySulfSat, a new Open-Source Python3 tool for modelling sulfide and sulfate saturation in magmas. Accurately predicting the onset of sulfide or sulfate saturation during fractional crystallization, and/or identifying melt compositions as saturated or undersaturated, is vital to understand and model the behavior of S-loving chalcophile elements during mantle melting, crustal storage and shallow degassing. PySulfSat can calculate the sulfide content at sulfide content at sulfide saturation (SCSS) and the sulfate content at anhydrite saturation (SCAS) using a number of the most recent models. It is extremely fast, performing calculations for each composition in ~1 ms on a standard laptop (16 GB RAM) meaning it can be applied to very large datasets with ease. PySulfSat also supports a variety of different input structures (spreadsheets, Petrolog3 outputs, MELTS tbl files, etc.), without requiring extensive formatting by the user. It can also be integrated with MELTS for python infrastructure, allowing calculations of sulfur solubility during fractional crystallization over a wide range of conditions within a single Jupyter Notebook. Importantly, PySulfSat allows mixing and matching methods, so the SCSS could be calculated with one model using the sulfide composition predicted by a different SCSS model. PySulfSat also contains functions for calculating the proportion of S6+ using popular expressions, along with other common workflows (e.g., calculating the mass proportion of fractionated sulfide). Worked examples are available on the Read The Docs page: https://bit.ly/PySulfSatRTD

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Thermobar: An Open-Source Python3 Tool for Thermobarometry and Hygrometry

Cited by 3 publications

References 51 publications

Mapping Global Lithospheric Mantle Pressure‐Temperature Conditions by Machine‐Learning Thermobarometry

Mapping Global Lithospheric Mantle Pressure‐Temperature Conditions by Machine‐Learning Thermobarometry

Porosity evolution of mafic crystal mush during reactive flow

PySulfSat: An Open-Source Python3 Tool for modelling sulfide and sulfate saturation

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