Spring 2019 - EASC 305 D100
Quantitative Methods for the Earth Sciences (3)
Class Number: 2148
Delivery Method: In Person
Overview
-
Course Times + Location:
Jan 3 – Apr 8, 2019: Mon, Wed, 9:30–10:20 a.m.
Burnaby -
Exam Times + Location:
Apr 13, 2019
Sat, 3:30–6:30 p.m.
Burnaby
-
Instructor:
Glyn Williams-Jones
glynwj@sfu.ca
1 778 782-3306
Office: TASC 1 Room 7225
-
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. Quantitative.
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 many problems will require the use the Matlab programming language. Geospatial analysis of various data sets will involve the use of ArcGIS. 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 and ArcGIS 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:
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS