# Geostatistics and Data Analysis

## 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.

## General characterization

##### Code

10666

##### Credits

6.0

##### Responsible teacher

José António de Almeida

##### Hours

Weekly - 6

Total - 68

##### Teaching language

Português

### Prerequisites

Elementary knowledge of probability and statistics.

### 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 includes theoretical and pratice components.

The theoretical component may be of two tests or final examination (60% of the final grade). Each test last for about 2 hours and has 30% of the final grade. This component can be replaced by the final examination on the scheduled date.

The practice includes solving a set of questions / problems in all practical classes. They are sended by email to the teacher at the end of each practical class, and are solved individualy or in groups of two students. In all practical classes there are a form to solve, and all of them has the same weight. The set of problems is 40% of the final grade.

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.