STATS 7062 - Multivariate Geostatistics
North Terrace Campus - Semester 2 - 2018
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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.University 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 University 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 University'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
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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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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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