Handbook : Unit informationEdith Cowan University

Faculty of Health, Engineering and Science

School: Engineering

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

• Unit Title

  Geostatistical Methods

• Unit Code

  MAT5218

• Year

  2015

• Enrolment Period

  1

• Version

  1

• Credit Points

  20

• Full Year Unit

  N

• Mode of Delivery

  Online

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Description

This unit is designed to provide students with a variety of geostatistical techniques used in estimation and simulation from spatial data. Applications will be mainly in the areas of mining, petroleum and environmental geostatistics

Prerequisite Rule

Students must pass 1 unit from MAT3106, MAT3485, MAT5106, MAT5215

Learning Outcomes

On completion of this unit students should be able to:

1. Carry out various forms kriging and cokriging.
2. Understand the random function model for the analysis of spatial data.
3. Use variograms and crossvariograms to model spatial continuity.

Unit Content

1. Block kriging; change of support techniques.
2. Indicator transform, indicator variography, indicator kriging, checking the biGaussian assumption.
3. Kriging with a trend, kriging with external drift, factorial kriging.
4. Multivariate variography, cokriging, probability kriging.
5. Review of the random function model, univariate variography, simple and ordinary kriging.

Additional Learning Experience Information

Equivalent to three hours of lecture/workshop sessions per week. The unit will be supported by use of a range of geostatistical software packages.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

Students please note: The marks and grades received by students on assessments may be subject to further moderation. All marks and grades are to be considered provisional until endorsed by the relevant Board of Examiners.

Text References

• ^ Goovaerts, P. (1997). Geostatistics for natural resources estimation. Oxford University Press.
• Cressie, N. (1991). Statistics for spatial data. Wiley.
• Chiles, J-P., & Delfiner, P. (1999). Geostatistics: modelling spatial uncertainty. Wiley.
• Deutsch, C., & Journal, A. (1997). GSLIB2: Geostatistical software library and user's guide. Oxford University Press.
• Dimitrakopoulos, D. (Ed.). (1994). Geostatistics for the next century. Kluwer Academic Publisher.
• Wackernagel (1995). Multivariate geostatistics. Springer.
• Isaaks, E., & Srivastava, R. (1989). An introduction to applied geostatistics. Oxford University Press.
• Journel, A., & Huijbregts, C. (1978). Mining geostatistics. Academic Press.
• Olea, R.A. (1999). Geostatistics for engineers and earth scientists. Kluwer.
• Panatier, Y. (1996). Variowin software for spatial data analysis in 2D. Springer.
• Soares, A. (Ed.). (1993). Troia `92. Kluwer Academic Publisher.
• Armstrong, M. (1999). Basic linear geostatistics. Springer.
• Gomez-Hernandez et al (Eds.). (1999). geoENVII - geostatistics for environmental applications. Kluwer.

^ Mandatory reference

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