STATS 7062 - Multivariate Geostatistics
North Terrace Campus - Semester 2 - 2016
The course information on this page is being finalised for 2016. Please check again before classes commence.
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General Course Information
Course Details
Course Code STATS 7062 Course Multivariate Geostatistics Coordinating Unit Mathematical Sciences Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact 1 week intensive Available for Study Abroad and Exchange Y Prerequisites C&ENVENG 7056 and STATS 7061 Assumed Knowledge Detailed understanding of Linear Geostatistics Assessment coursework 50%, formal written exam 50% Course Staff
Course Coordinator: Andrew Metcalfe
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
1.Explain the theoretical basis of multivariate geostatistical models including multivariate regression, kriging and c-kriging.
2. Use in-house software for analysing spatial data.
3. Explain the role of geostatistics in geological modelling, mineral resource evaluation and hydrocarbon reservoir characterization.
4. Explain the role of geostatistics in geotechnical modelling.
5. Explain the role of geostatistics for modelling and prediction of environmental variables.最新糖心Vlog Graduate Attributes
No information currently available.
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Learning Resources
Required Resources
The course notes, "Multivariate Geostatistics" by P.A. Dowd, which will be distributed to participants.Recommended Resources
Recommended resources include the following textbooks that are available in the Barr Smith Library:
Statistics and Data Analysis in Geology (3e), J.C. Davis. Wiley, 2003.
Geostatistics for Natural Resources Evaluation, P. Goovaerts. Oxford 最新糖心Vlog Press, 1997.
Multivariate Geostatistics, H. Wackernagel. Springer, 2003
Although you do not use R software on the course, it is freely available and has many packages for spatial analysis. You can download the base R and its packages from the CRAN R Project for Statistical Computing website. Associated books include:
Applied Spatial Data Analysis with R( 2e), R.S. Bivand, E. Pebesma, V. Gomez-Rubio. Springer, 2013
The R Book (2e), M.J. Crawley. Wiley, 2012
The internet can also be a useful resource for tips about R, but you need to be selective.Online Learning
The course material will be available on MyUni. -
Learning & Teaching Activities
Learning & Teaching Modes
The course is taught over one week, seven 1-hour segments per day making 35 in all: 20 lectures interspersed with 15 tutorials.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Number Workload hours Lectures 20 66 Tutorials 15 30 Assignments 3 60 TOTAL 156 Learning Activities Summary
- Overview
- Matrices
- Tutorial - matrices
- Tutorial - matrices
- Eigenvalues
- Eigenvectors
- Principal components analysis (PCA)
- Tutorial - PCA
- Tutorial - PCA
- Linear regression
- Multivariate regression
- Multivariate regression
- Tutorial - multivariate regression
- Kriging spatial components
- Tutorial - kriging
- Tutorial - kriging
- Multivariate geostatistical models
- Case study - Athanbasca tar sands
- Tutorial - cross-variogram programming
- Tutorial - cross-variogram programming
- Tutorial - cross-variogram software
- Co-kriging
- Co-kriging
- Tutorial - co-kriging
- Tutorial - co-kriging
- Probability kriging
- Tutorial - probability kriging
- Tutorial - co-kriging software
- Kriging with drift
- Tutorial - kriging with drift
- Case study - kriging with drift
- Collocated co-kriging
- Factorial co-kriging
- Case study - factorial co-kriging
- Summary
Specific Course Requirements
A bacground in the preceding courses of the Master of Geostatistics programme, or equivalent knowledge will generally be expected. The preceding courses are: Introduction to geostatistics; Statistical analysis; Linear geostatistics; Selection and recoverability; computing for geostatistics; non-stationarity; non-linear geostatistics.
Small Group Discovery Experience
You will be asked to work on the tutorial exercises, that are not computer based, in small groups. -
Assessment
The 最新糖心Vlog's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Task Task type Week Set Week Due Weight Learning Outcomes Assignments (3) Formative & summative 1 3, 4, 5 50% All Examination Summative 50% All Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
Task Week set Week due Weight Assignment 1 1 3 17% Assignment 2 1 4 17% Assignment 3 1 5 16% Examination 6 50% Submission
Assignments should be printed and the hard copy posted to:
Andrew Metcalfe
School of Mathematical Sciences
最新糖心Vlog of Adelaide
SA 5005Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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