Geostatistics and Data Analysis


On completion of this module, students should be able to understand concepts of data analysis and geostatistics, namely statistical analysis of geological data, and integrate project teams for this subject.
In particular students should be able to:
- Develop processing and interpretation approaches for preliminary statistical data analysis and summarise results;
- For each particular data set (categorical/numerical variables), select the most adequate statistical tools;
- Analyse data redundancy and representativeness;
- Evaluate spatial patterns of correlation between samples;
- Produce estimated maps of a numerical variable and validates results;
- Report and comment results in technical language.

General characterization





Responsible teacher

José António de Almeida


Weekly - 4

Total - 68

Teaching language



Elementary knowledge of probability and statistics.


[1] Richard A. Johnson & Dean W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, 2002, ISBN: 0-13-092553-5 (paperback).
[2] Amílcar Soares. Geoestatistica para as Ciências da Terra e do Ambiente. IST Press, 2014 (2ª edição), 232p.
[3] Edward H. Isaaks, R. Mohan Srivastava. Applied Geostatistics. Oxford University Press, 1989, ISBN: 0-195050134 (paperback).
[4] Pierre Goovaerts. Geostatistics for Natural Resources Evaluation. Oxford University Press, 1997. ISBN: 0-195115384 (hardcover).

Teaching method

Exposure with Powerpoint and board and practical classes where students solve problems devoted to each main topic: (1) univariate analysis; (2) bivariate analysis; (3) multivariate analysis; (4) variography; (5) kriging.

Evaluation method

The evaluation includes theoretical and pratice components.

The evaluation of the theoretical component can be by two tests or a final exam (60% of the final grade). Each test lasts about 2 hours and counts for 30% of the final grade. The test is done with consultation. This component can be replaced by exam on a scheduled date also with consultation.

The practical evaluation includes the compilation of the proposed problem solving in some practical classes plus comments. They are delivered in a PDF document at the end of the semester, individually, or in groups of 2 students. The set of problems is 40% of the final grade. For reports with a grade greater than or equal to 14, there will be an oral discussion.

The minimum grade for tests/ exams is 8 values in 20.

Subject matter

Data types and data analysis strategies. Categorical and continuous variables. Georeferenced information. Map of samples location. Parametric statistical analysis. Univariate distribution laws frequently used variables in the Earth Sciences. Distribution laws (normal, lognormal, uniform). Exploratory data analysis. Univariate analysis: summary statistics and graphical representations. Bivariate analysis: correlation measures, contingency tables and graphical representations. Multivariate analysis: principal component analysis (PCA), hierarchical classification and k-means. Random variables. Theory of regionalized variables. Some characteristics of the regionalized variables. Spatial covariance and variogram. Modeling of experimental variograms. Variography practice. The kriging estimator. Properties. Deduction of the kriging system. Kriging variance. Practice of Kriging: estimation of point and block grids.


Programs where the course is taught: