Spring 2018 - EASC 305 D100

Quantitative Methods for the Earth Sciences (3)

Class Number: 6991

Delivery Method: In Person

Overview

  • Course Times + Location:

    Mo, We 9:30 AM – 10:20 AM
    AQ 5007, Burnaby

  • Exam Times + Location:

    Apr 16, 2018
    12:00 PM – 3:00 PM
    Location: TBA

  • Instructor:

    Gwenn Flowers
    gflowers@sfu.ca
    1 778 782-6638
    Office: TASC 1 Room 7237
  • Prerequisites:

    EASC 101; MATH 152, PHYS 121 or 126 or 102 or 141, and STAT 201 or 270 (all with a grade of C- or better), and six units in any 200 division or higher EASC courses.

Description

CALENDAR DESCRIPTION:

Implementation of mathematical methods and numerical techniques for problem solving in the Earth Sciences. Examples and lab assignments will use Excel spreadsheets and/or Matlab computer programming/display software. Concepts covered include quantitative techniques for field data and error analysis in the geosciences, basic computer programming concepts and numerical modeling of Earth processes.

COURSE DETAILS:

Course Outline
In the Earth sciences, we deal with a variety of data acquired by geological mapping, geochemical analysis, and geophysical surveys. Although such data may be quite different, they can have certain common characteristics; for example, some types of data are recorded as a function of time or geographic location. Consequently, there are certain standard approaches to representing such data, understanding their significance, and analyzing errors. Students will learn how data can be organized, and gain practical experience with some basic techniques that will enable them to develop computer programs for data analysis. Simple analysis can sometimes be carried out using Excel spreadsheets, but most problems will require the use the Matlab programming language. Lectures will provide the background to the various methods, with the computer-based lab assignments being used to teach practical programming and field data analysis.

Provisional course topics:
• Matrices and data organization
• Introductory computing
• Univariate/bivariate statistics, tests of statistical significance
• Principal component analysis
• Analysis of time series data: correlation, filtering, spectral analysis
• Analysis of spatial data: interpolation, variograms and kriging
• Steady-state and time-dependent numerical models (time permitting)

Grading

  • Midterm Exams 10-20%
  • Laboratory Final Exam 10-25%
  • Final Exam (Cumulative) 20-30%
  • Problem Sets/Laboratory Exercises (Equally weighted) 25-50%

NOTES:

NOTE: Matlab software will be available in the computer lab.

Materials

REQUIRED READING:

Davis, J.C., 2003, Statistics and Data Analysis in Geology, 3rd Edition, Wiley, 656 pp
ISBN: 978-0-471-17275-8

Registrar Notes:

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