# Geostatistics and Data Analysis

### Code

10666

### Academic unit

Faculdade de Ciências e Tecnologia

### Department

Departamento de Ciências da Terra

### Credits

6.0

### Teacher in charge

José António de Almeida

### Weekly hours

6

### Total hours

68

### Teaching language

Português

### Objectives

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.

### Prerequisites

Elementary knowledge of probability and statistics.

### Subject matter

Data types and data analysis strategies. Categorical and continuous variables. Georeferenced information. Map of samples location. 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) and hierarchical classification and k-means. Parametric statistical analysis. Univariate distribution laws frequently used variables in the Earth Sciences. Distribution laws (normal, lognormal, uniform). One factor ANOVA. 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.

### Bibliography

[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 of the theoretical component may be of continuous type (two tests) or examination. Each test last for about 2 hours and has 30% of the final grade. This component can be replaced by examination on the scheduled date (60% of grade). The practice includes solving problems in classes plus reports. They are solved in groups of two students. The set of problems is 40% of the final grade. Assigning top grades or equal to 14 on the problems is dependent on oral discussion. There are no minimum grades for each component being required for final approval average higher than 9.5.