STATS 7062 - Multivariate Geostatistics

North Terrace Campus - Semester 2 - 2015

Review of matrix algebra, eigenvalues and eigenvectors. Principal Components Analysis. Multivariate regression. Kriging spatial components; filtering spatial components. Multivariate geostatistical models. Co-kriging and co-kriging variances. Comparison of co-kriging and kriging. Kriging with an external drift. Collocated kriging. Factorial co-kriging.

  • General Course Information
    Course Details
    Course Code STATS 7062
    Course Multivariate Geostatistics
    Coordinating Unit Statistics
    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 Course Planner.

  • 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

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 3,4,5
    Skills of a high order in interpersonal understanding, teamwork and communication. 3,4,5
    A proficiency in the appropriate use of contemporary technologies. 1,2
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,2,3,4,5
    A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 3,4,5
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 3,4,5
  • 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.

    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
    1. Overview
    2. Matrices
    3. Tutorial - matrices
    4. Tutorial - matrices
    5. Eigenvalues
    6. Eigenvectors
    7. Principal components analysis (PCA)
    8. Tutorial - PCA
    9. Tutorial - PCA
    10. Linear regression
    11. Multivariate regression
    12. Multivariate regression
    13. Tutorial - multivariate regression
    14. Kriging spatial components
    15. Tutorial - kriging
    16. Tutorial - kriging
    17. Multivariate geostatistical models
    18. Case study - Athanbasca tar sands
    19. Tutorial - cross-variogram programming
    20. Tutorial - cross-variogram programming
    21. Tutorial - cross-variogram software
    22. Co-kriging
    23. Co-kriging
    24. Tutorial - co-kriging
    25. Tutorial - co-kriging
    26. Probability kriging
    27. Tutorial - probability kriging
    28. Tutorial - co-kriging software
    29. Kriging with drift
    30. Tutorial - kriging with drift
    31. Case study - kriging with drift
    32. Collocated co-kriging
    33. Factorial co-kriging
    34. Case study - factorial co-kriging
    35. 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:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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%
    Assignments should be printed and the hard copy posted to:
    Andrew Metcalfe
    School of Mathematical Sciences
    University of Adelaide
    SA 5005
    Course 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 Access Adelaide.

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    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 ( 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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