pyrolite.geochem.magma

Submodule for calculating and modelling melt chemistry. Includes common functions for predicting and accounting for melt evolution.

pyrolite.geochem.magma.FeAt8MgO(FeOT: float, MgO: float) float[source]

To account for differences in the slopes and curvature of liquid lines of descent as a function of parental magma composition [1] [2] (after [3]).

Parameters:
  • FeOT (float) – Iron oxide content.

  • MgO (float) – Magnesium oxide content.

References

pyrolite.geochem.magma.NaAt8MgO(Na2O: float, MgO: float) float[source]

To account for differences in the slopes and curvature of liquid lines of descent as a function of parental magma composition [4] [5] (after [6]).

Parameters:
  • Na2O (float) – Iron oxide content.

  • MgO (float) – Magnesium oxide content.

References

pyrolite.geochem.magma.SCSS(df, T, P, kelvin=False, grid=None, outunit='wt%')[source]

Obtain the sulfur content at sulfate and sulfide saturation [7] [8].

Parameters:
  • df (pandas.DataFrame) – Dataframe of compositions.

  • T (float | numpy.ndarray) – Temperature

  • P (float | numpy.ndarray) – Pressure (kbar)

  • kelvin (bool) – Whether temperature values are in kelvin (True) or celsuis (False)

  • grid (None, 'geotherm', 'grid') – Whether to consider temperature and pressure as a geotherm (geotherm), or independently (as a grid, grid).

Returns:

sulfate, sulfide – Arrays of mass fraction sulfate and sulfide abundances at saturation.

Return type:

numpy.ndarray, numpy.ndarray

Notes

For anhydrite-saturated systems, the sulfur content at sulfate saturation is given by the following:

\[\begin{split}\begin{align} ln(X_S) = &10.07 - 1.151 \cdot (10^4 / T_K) + 0.104 \cdot P_{kbar}\\ &- 7.1 \cdot X_{SiO_2} - 14.02 \cdot X_{MgO} - 14.164 \cdot X_{Al_2O_3}\\ \end{align}\end{split}\]

For sulfide-liquid saturated systems, the sulfur content at sulfide saturation is given by the following:

\[\begin{split}\begin{align} ln(X_S) = &{-1.76} - 0.474 \cdot (10^4 / T_K) + 0.021 \cdot P_{kbar}\\ &+ 5.559 \cdot X_{FeO} + 2.565 \cdot X_{TiO_2} + 2.709 \cdot X_{CaO}\\ &- 3.192 \cdot X_{SiO_2} - 3.049 \cdot X_{H_2O}\\ \end{align}\end{split}\]

References

Todo

  • Produce an updated version based on log-regressions?

  • Add updates from Smythe et al. (2017)?