EOSC 522 · Methods and Modeling in Petrology and Geochemistry
This course is not eligible for Credit/D/Fail grading. [3-0-2]
Alternate year courseOdd year start – Term 1
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:
- concepts related to the collection, analysis and interpretation of mineralogical and geochemical datasets,
- matrix algebraic methods for exploring the structure of these same datasets,
- principles of optimization for systems that are underdetermined, determined and overdetermined,
- forward modelling methods (thermodynamic systems, finite difference models of heat flow, and coupled systems)
- inverse modelling methods and philosophy
- Fitting models to data and evaluating their "correctness"
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.
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
SELECT TOPICS FROM WHICH LECTURES ARE DRAWN
- 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,
Minimization of G strategies