EOSC 522 · Methods and Modeling in Petrology and Geochemistry

This course is not eligible for Credit/D/Fail grading. [3-0-2]

Course Availability & Schedule

 Alternate year course

Odd year start – Term 1

Learning Goals

Methods & Models provides an introduction to the basic calculational tools and concepts used in research related to the mineralogical sciences, petrology, geochemistry and ore deposits. The course introduces problem-solving philosophies and computational strategies using MATLAB. However, the main focus is on applying these methods to quantitatively test ideas about rock-forming processes based on geochemical and petrological datasets.

For example, the course introduces:

  1. concepts related to the collection, analysis and interpretation of mineralogical and geochemical datasets,
  2. matrix algebraic methods for exploring the structure of these same datasets,
  3. principles of optimization for systems that are underdetermined, determined and overdetermined,
  4. forward modelling methods (thermodynamic systems, finite difference models of heat flow, and coupled systems)
  5. inverse modelling methods and philosophy
  6. Fitting models to data and evaluating their "correctness"


Kelly Russell


There is no textbook but we draw on material from:

  • Draper & Smith (1966, 1981) Applied Regression Analysis, J. Wyllie
  • Greenwood, H.J., 1989. On models and modeling. Canad. Mineral. 1-14.
  • Meyer, S.L. (1975) Data Analysis for Scientists & Engineers, J. Wyllie, reprinted
  • Popper KR (1968) The logic of scientific discovery. Harper and Row, New York, 479 p
  • Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1986) Numerical Recipes: the Art of Scientific Computing. Cambridge University Press, Cambridge, 818 p. 

Course Content


a) Lectures are given once a week to introduce new content and applications.
b) Tutorial time is scheduled each week and problem sets are discussed or presented.
c) MATLAB is used to solve problem-sets (previous experience in not essential).

RESEARCH PROJECTS: Students complete an individual research project comprising an Oral Presentation and a Written Paper

  • projects are on topics and datasets approved by instructor to ensure some level of success in the time available
  • projects are built around actual datasets
  • involve analysis and modelling of datasets
  • include developed Matlab code & graphical presentation of data

Lecture Topics


  • Data Analysis: Measurement error and treatment
  • Geochemical data in matrix format
  • Modelling data: Fitting of data, model optimization, evaluation of models
  • Matrix Algebraic Methods for Geochemistry
  • Linear (Mass balance) and non-Linear (Transport Properties) systems of equations
  • Forward Models: Analytical & Numerical solutions
  • Applications to heat flow and transport
  • Inverse Models: Philosophy & rationale
  • Inverse Methods
  • Equilibrium Thermodynamics:
    Gibbs Free Energy,
    Activity-composition models,
    Phase saturation
    Minimization of G strategies