Handbook : Unit informationEdith Cowan University

School: Science

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

  Introduction to Geostatistics

• Unit Code

  MAT5215

• Year

  2016

• 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 an introduction to the geostatistical techniques used in estimation from spatial data. Applications will be mainly in the areas of mining, petroleum, soil science and environmental management.

Learning Outcomes

On completion of this unit students should be able to:

1. Calculate a range of descriptive statistics and carry out a variety of methods for exploratory data analysis.
2. Carry out simple and ordinary kriging.
3. Understand the random function model for the analysis of spatial data.
4. Use variograms and covariance functions to model spatial continuity.

Unit Content

1. Descriptive statistics and exploratory data analysis.
2. Estimation methods: simple kriging; ordinary kriging; kriging variance.
3. Modelling spatial continuity: experimental variogramscovariance functions; correlograms and madograms; variogram and covariance function models; isotropy and anisotropy.
4. Random function model.
5. Random variable; moments; probability distributions; normal and lognormal distributions.

Additional Learning Experience Information

Equivalent to three hours of lecture/workshop sessions per week. The course will be supported by the use 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.
• Armstrong, M. (1999). Basic linear geostatistics. Springer.
• Chiles, J-P., & Delfiner, P. (2012). Geostatistics: Modelling Spatial Uncertainty (2nd ed.). Wiley.
• Clark, I., & Harper, W. (2000). Practical geostatistics 2000. Ecosse Geostatistical Sales, Alloa, Scotland.
• Cressie, N. (1991). Statistics for spatial data. Wiley.
• Coombes, J. (2008). The art and science of resource estimation: a practical guide for geologists and engineers. Coombes Capability.
• Haining, R. (2003). Spatial data analysis: theory and Practice. Cambridge University Press.
• Isaaks, E., & Srivastava, R. (1989). An introduction to Applied Geostatistics. Oxford University Press.
• Journel, A., & Huijbregts, C. (1978). Mining Geostatistics. Academic Press.
• Olea, R. (1999). Geostatistics for Engineers and Earth Scientists. Kluwer.
• Webster, R & Oliver, M (2007). Geostatistics for environmental scientists (2nd ed.). Wiley.
• Wackernagel, H. (2003). Multivariate Geostatistics: an itroduction with applications (3rd ed.). Springer.
• Deutsch, C., & Journel, A. (1998). GSLIB: Geostatistical Software Library and User's Guide (2nd ed.). Oxford University Press.

^ Mandatory reference

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