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Benchmarking models for sulfur saturation

This notebook benchmarks the models for sulfur saturation in VolFe where possible.

Python set-up

import pandas as pd
import VolFe as vf
import math

Version of VolFe

vf.__version__

'0.4.1'

Composition and conditions used for benchmarking

Average of high-SiO2 pillow-rim glasses in HSDP from Mauna Kea volcano from Brounce et al. (2017).

my_analysis = {
        "Sample": "Hawaiian basalt",
        "T_C": 1200.0,  # Temperature in 'C
        "SiO2": 51.29,  # wt%
        "TiO2": 2.50,  # wt%
        "Al2O3": 13.70,  # wt%
        "FeOT": 11.04,  # wt%
        "MnO": 0.02,  # wt%
        "MgO": 6.70,  # wt%
        "CaO": 11.03,  # wt%
        "Na2O": 2.27,  # wt%
        "K2O": 0.43,  # wt%
        "P2O5": 0.21,  # wt%
        "H2O": 3.,  # wt%
        "CO2ppm": 1000.,  # ppm
        "STppm": 0.,  # ppm
        "Xppm": 0.0,  # ppm
        "Fe3FeT": 0.1,
    }

my_analysis = pd.DataFrame(my_analysis, index=[0])

PT = {"P":1000.}
PT["T"]=1200.

melt_wf=vf.melt_comp(0.,my_analysis)
melt_wf['CO2'] = my_analysis.loc[0.,"CO2ppm"]/1000000.
melt_wf["H2OT"] = my_analysis.loc[0,"H2O"]/100.
melt_wf['ST'] = my_analysis.loc[0.,"STppm"]/1000000.
melt_wf['CT'] = (melt_wf['CO2']/vf.species.loc['CO2','M'])*vf.species.loc['C','M']
melt_wf['HT'] = (melt_wf['H2OT']/vf.species.loc['H2O','M'])*(2.*vf.species.loc['H','M'])
melt_wf['XT'] = my_analysis.loc[0.,"Xppm"]/1000000.
melt_wf["Fe3FeT"] = my_analysis.loc[0.,"Fe3FeT"]

Models for Sulfide Content at Sulfide Saturation (S2-CSS)

option = SCSS, function = SCSS

Models calculated using PySulfSat have been benchmarked in PySulfSat:

• ‘Fortin15_pss’ Fortin et al. (2015) using PySulfSat by Wieser & Gleeson (2023)

• ‘Liu21_pss’ Liu et al. (2021) using PySulfSat by Wieser & Gleeson (2023)

• ‘ONeill22_pss’ O’Neill & Mavrogenes (2022) using PySulfSat by Wieser & Gleeson (2023)

• ‘ONeill21_pss’ O’Neill (2021) using PySulfSat by Wieser & Gleeson (2023)

• ‘Smythe17_pss’ Smythe et al. (2017) using PySulfSat by Wieser & Gleeson (2023)

• ‘Li22_pss’ Li and Zhang (2022) using PySulfSat by Wieser and Gleeson (2023)

• ‘Blanchard21eq11_pss’ Eq. (11) from Blanchard et al. (2021) using PySulfSat by Wieser and Gleeson (2023)

• ‘Blanchard21eq12_pss’ Eq. (12) from Blanchard et al. (2021) using PySulfSat by Wieser and Gleeson (2023)

The following models do not have material available in the original papers for benchmarking:

• ‘ONeill21hyd’ Eq. (10.34, 10.43, 10.45, 10.46, 10.49) from O’Neill (2021)

• ‘ONeill21dil’ Eq. (10.34, 10.43, 10.45, 10.46) including water dilution from O’Neill (2021)

• ‘Liu07’ Eq. (9) in Liu et al. (2007)

‘ONeill21’ Eq. (10.34, 10.43, 10.45, 10.46) excluding water dilution from O’Neill (2021)

Supplementary spreadsheet (sulfide,SCSS=ONeill21.xlsx)

Cell AR14: 7.437

Matches to 3 decimal places: Note spreadsheet uses +273 to convert to K, rather than 273.15 used in VolFe so T in spreadsheet = 1200.15 ‘C

my_models = [["SCSS", "ONeill21"],["sulfide", "ONeill21"]]
my_models = vf.make_df_and_add_model_defaults(my_models)
math.log(vf.SCSS(PT,melt_wf,models=my_models))

7.4369918428928115

Models for Sulfate Content at Anhydrite Saturation (S6+CAS)

option = SCAS, function = SCAS

Models calculated using PySulfSat have been benchmarked in PySulfSat:

• ‘Chowdhury19_pss’ Chowdhury & Dasgupta (2019) using PySulfSat by Wieser and Gleeson (2023)

• ‘Zajacz19_pss’ Zajacz and Tsay (2019) using PySulfSat by Wieser and Gleeson (2023)

The following models do not have material available in the original papers for benchmarking:

• ‘Liu23’ Eq. (4) Liu et al. (2023)

There are no other models to benchmark in VolFe.