Glossary

Best central model

the central model that among all successful tries made during the central model analysis has either the minimum misfit (default) or maximum Bayes score. Whether misfit or Bayes score is used as the selection criterion is specified by the Bayes option.

Central model

an inversion result obtained with all known parameters set to their central values, i.e., most probable values. In MC_fit, the known parameters typically include analytical and thermodynamic data.

Composant

a phase with the same composition as a saturated component. If multiple component saturation constraints have been imposed, then the composant of the n’th saturated component is a phase that is made up of component n, and may contain components 1 to n, but does not contain any additional components.

Excess Oxygen

the amount of oxygen component that must be added or subtracted from an oxide or metallic component in order to to define the actual redox state of the metal represented by the oxide or metallic component (Appendix D).

Forward problem

The problem of predicting observations from a set of model parameters.

Inverse problem

The problem of inferring model parameters from observations.

Inversion parameters

The unknown parameters that are to be inferred from the known parameters of an inverse problem, i.e., the parameters on the left-hand side of Eq 2. In the context of MC_fit, inversion parameters typically include temperature, pressure, and unmeasured compositional information.

Misfit function

A quantitative measure of the difference between observed data and model predictions.

Monte Carlo sampling

A computational technique that uses random sampling to explore a parameter space. In MC_fit, Monte Carlo sampling is used to generate initial conditions for the central model analysis and to propagate analytical and thermodynamic uncertainties through the inversion process during the perturbation analysis.

MPP

in the inversion parameter space, the MPP is the centroid of the specified parameter ranges. If the ranges have been chosen rationally, the MPP represents the prior estimate of the most probable inversion parameter value. The MPP is also the average of the initial conditions generated for the Nelder-Mead optimizations of the central model analysis.

Try

a Nelder-Mead optimization to minimize the misfit between an observed phase assemblage and a thermodynamic model prediction as a function of the inversion parameters. Tries initiate from a random initial guess of the inversion parameters.