Computational Geology

Recommended Prerequisites

Introduction to Geology, Geomaths, Introduction to Maths I, Introduction to Mapping and GIS.

Teaching Methods

The curricular unit will be characterized by the presentation of several common problems in geosciences. A brief theoretical introduction will be made followed by a practical demonstration of the resolution possibilities, either using a programming language (Python) or by using programs available on the market (eg Surfer, RockWorks, XBeach, PAST, R, etc.) (theoretical-practical component) . In the practical component, problems will be proposed that will be solved by the students (in a tutored way). The model allows partial or total e-learning.

Learning Outcomes

This course intends to provide some computational skills for the acquisition and analysis of data in geosciences.

It is intended to develop a specialization in some digital aspects of geological work using and developing geological computing tools (e.g. Python), geological data processing software (e.g. Surfer, Rockworks, XBeach, etc.) and statistical analysis (e.g. PAST, R).

By the end of the course students should be able to:

1) Develop conceptual (simplified) models in Geosciences;

2) Solve concrete problems in geosciences using programming tools;

3) Apply statistical concepts to interpret geological data;

4) Use the fundamental resources of the Python programming language;

5) Represent geological data using specific programs (e.g. Surfer);

The acquisition of these skills by students will be reflected in a better integration in the job market in the area of geosciences.

Work Placement(s)

No

Syllabus

In this course, students will be led to understand how computer tools can be used to read, create, compile, analyze and visualize data obtained in Earth Sciences.

Example of the syllabus of this curricular unit:

1) Modeling. Development of conceptual models.

2) Solving problems using programming languages. Algorithms and data structures.

3) Introduction to numerical methods applied to geosciences.

4) Python. Justification for using this programming language.

5) The fundamental features of the Python programming language.

6) Numpy, matplotlib and scipy modules.

7) Procedural, object-oriented and functional Python programs;

8) Other programs for manipulating geological data, e.g. Surfer, Rockworks, XBeach, among others.

9) Statistical tools in the interpretation of geological data (e.g. PAST, R).

Head Lecturer(s)

Pedro Miguel Berardo Duarte Pina

Assessment Methods

Assessment
Resolution Problems: 25.0%
Research work: 25.0%
Frequency: 50.0%

Bibliography

Downey, A., (2015). Think Python, 2ed, Green Tea Press, 222p.

Linge, S., Langtangen, H.P., (2016). Programming for Computations - Python, Springer Open, 232p.

Davis, J.C., (2003). Statistics and Data Analysis in Geology, 3rd Edition, Wiley, 656 p. ISBN: 978-0-471-17275-8.

Manuais do software usado

Burden, R., Faires, J., Numerical Analysis, Brooks Cole, 9ed., 2011, 877p.

Introduction to Quantitative Geology 2019: https://introqg.github.io/site/ (acesso 19-05-2020).