Spring 2022 - EASC 305 D100
Quantitative Methods for the Earth Sciences (3)
Class Number: 1324
Delivery Method: In Person
Overview
-
Course Times + Location:
Jan 10 – Apr 11, 2022: Thu, 10:30 a.m.–12:20 p.m.
Burnaby -
Exam Times + Location:
Apr 20, 2022
Wed, 7:00–10:00 p.m.
Burnaby
-
Instructor:
Glyn Williams-Jones
glynwj@sfu.ca
1 778 782-3306
Office: TASC 1 Room 7205
-
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:
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 codes for data analysis. Simple analysis can sometimes be carried out using Excel spreadsheets, but many problems will require the use of Python programming language. Geospatial analysis and 3D modelling of various data sets will involve the use of ArcGIS and Leapfrog Geo, respectively. 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 and modelling.
Provisional course topics:
- Matrices and data organization
- Univariate/bivariate statistics, tests of statistical significance
- Principal component analysis
- Analysis of spatial data: interpolation, variograms and kriging
- Introductory computing with Python
- Introductory GIS and 3D modelling
Grading
- Laboratory Exercises 20%
- Integrated GIS & 3D modelling Term project 35%
- Midterm exam 15%
- Final exam (cumulative) 30%
NOTES:
Python, Leapfrog 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:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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
TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.