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
Introduction to data analysis and geostatistics. Types and strategies for data analysis. Data types, variables and preparation of data files. Categorical and numerical variable types. Georeferenced data. Sampling strategies in Earth Sciences. Sample locations and mapping.
Exploratory data analysis. Univariate analysis: summary measurements (first, second and third moment tools and position statistics) and graphical representations. Bivariate analysis: correlation measurements (covariance, Pearson and Spearsman), bi-histograms and graphical representations.
Multivariate analysis: principal components (PCA), correspondence analysis (CA) and classification.
Distribution laws in earth sciences for common data. Random variables. Normal, lognormal and uniform distribution laws.
Regionalized variables theory. Characteristics of regionalized variables. Random functions. Restrictive hypothesis. 2nd order stationary. Intrinsic stationary.
Spatial analysis: calculation of H-scattergrams, spatial covariance and variograms. Variogram surfaces. Modelling of experimental variograms. Calculation of variograms for regular and irregular sample datasets.
Geostatistical kriging estimation: linear estimator properties, solution of the kriging equations system, and kriging variance. Use kriging for the estimation of points, areas and volumes.
Case studies of application in environmental and earth sciences.
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. The practice includes solving 5 problems plus reports. 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 five problemas are resolved partially in class. They are solved in groups of two students and may have small differences between groups. 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.