2024
Applied Geostatistics
Name: Applied Geostatistics
Code: GEO12520M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Geological Engineering
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Presentation
Introduction to geostatistical tools applied to the analysis, evaluation, and spatial prediction of mineral resources and soil resources, with special emphasis on interpolation methods based on kriging and on free software developed in R.
Sustainable Development Goals
Learning Goals
The main objective is to give the students the ability to use several geostatistical tools to analyze and
interpret spatial data. It is expected that students acquire a good understanding of the theoretical basis
underlying different geostatistical techniques, as well as a good proficiency in applying these techniques
in practical cases. To achieve this, students must acquire some basic skills in R language, which is the
main software tool adopted. Using R language and spatial data sets from case studies, students must
perform exploratory data analysis, make transformations of variables, prepare and analyze the
experimental variogram, determine the variogram model, and evaluate different predictions based on the
kriging estimator. Students should also integrate the knowledge of geostatistical methodologies with the
specificities of different geodynamic processes in order to achieve a proper interpretation of the analytical
results obtained.
interpret spatial data. It is expected that students acquire a good understanding of the theoretical basis
underlying different geostatistical techniques, as well as a good proficiency in applying these techniques
in practical cases. To achieve this, students must acquire some basic skills in R language, which is the
main software tool adopted. Using R language and spatial data sets from case studies, students must
perform exploratory data analysis, make transformations of variables, prepare and analyze the
experimental variogram, determine the variogram model, and evaluate different predictions based on the
kriging estimator. Students should also integrate the knowledge of geostatistical methodologies with the
specificities of different geodynamic processes in order to achieve a proper interpretation of the analytical
results obtained.
Contents
Introduction to different types of Geosciences data and to its collection and preparation.
Introduction to Geostatistics. R language as a tool for application in Geostatistics. Exploratory data
analysis. Spatial data prediction. Theory of regionalized variables. Analysis of the spatial data structure:
experimental variogram and modeling of the variogram. Geostatistical estimation or prediction: kriging.
Basics of multivariate data analysis. Main types of kriging: general characterization and exercises in R.
Geostatistical Simulation: general characterization and exercises
in R.
Introduction to Geostatistics. R language as a tool for application in Geostatistics. Exploratory data
analysis. Spatial data prediction. Theory of regionalized variables. Analysis of the spatial data structure:
experimental variogram and modeling of the variogram. Geostatistical estimation or prediction: kriging.
Basics of multivariate data analysis. Main types of kriging: general characterization and exercises in R.
Geostatistical Simulation: general characterization and exercises
in R.
Teaching Methods
Course held in mixed classes, including theory and computational exercises. Presentation of theoretical
issues is followed by real-world case studies, related primarily to natural resources. Exercises are started
in the and usually are completed after the period. Exercises performed by each student are
compiled in an individual report which is evaluated at the end of the semester. Data used in the exercises
can be based on real world examples provided by enterprises collaborating with the graduation.
The final grade is the average of two components, in both evaluation regimes (continuous and final): an individual report (75%) and a test (25%). The final evaluation regime requires also the discussion of the individual report. The report should be structured to include all exercises performed, R scripts, results obtained and their interpretation. It is also valued the application of additional methodologies related to the course, though not explored in classes.
issues is followed by real-world case studies, related primarily to natural resources. Exercises are started
in the and usually are completed after the period. Exercises performed by each student are
compiled in an individual report which is evaluated at the end of the semester. Data used in the exercises
can be based on real world examples provided by enterprises collaborating with the graduation.
The final grade is the average of two components, in both evaluation regimes (continuous and final): an individual report (75%) and a test (25%). The final evaluation regime requires also the discussion of the individual report. The report should be structured to include all exercises performed, R scripts, results obtained and their interpretation. It is also valued the application of additional methodologies related to the course, though not explored in classes.
Teaching Staff (2023/2024 )
- Carlos Alberto de Jesus Alexandre [responsible]
