GEOLOGICAL MODELING
MODELLIZZAZIONE GEOLOGICA
| A.Y. | Credits |
|---|---|
| 2020/2021 | 6 |
| Lecturer | Office hours for students | |
|---|---|---|
| Luca Lanci |
| Teaching in foreign languages |
|---|
|
Course with optional materials in a foreign language
English
This course is entirely taught in Italian. Study materials can be provided in the foreign language and the final exam can be taken in the foreign language. |
Assigned to the Degree Course
| Date | Time | Classroom / Location |
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| Date | Time | Classroom / Location |
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Learning Objectives
The course is aimed at the acquisition of the principles of analysis of regionalized variables and discrete time series, addressed to a geological-stratigraphic and paleoclimatic context. The course also aims at mastering the main numerical analysis tools and provides numerous practical examples with the help of a standard programming language for data analysis. The numerical analysis principles in this course have potential applications in many other fields and are an important tool for quantitative analysis.
Program
The course will have a practical and computer-aided approach to the following topics :
1. Introduction to geostatistics
1.1. Semivariogram.
1.2. Anisotropy
1.3. Ordinary Kriging
1.4. Universal Kriging
2. Introduction to the time series analysis.
2.1. Introduction to discrete time series
2.2. Fourier transform.
2.3. Power spectrum estimate (WOSA) .
2.4. Multi Taper Method (MTM).
2.5. Noise and Signal.
2.6. Evolutive spectrum.
2.7. Wavelets.
2.8. Singular Spectrum Analysis.
Bridging Courses
No compulsory prerequisite, however, to follow profitably the course, the following preliminary knowledge are required:
1. Knowledge of algebra of vectors and matrices.
2. Basic knowledge of programming.
3. Basic knowledge in the use of personal computers.
Learning Achievements (Dublin Descriptors)
· The student must demonstrate the ability to master the numerical analysis methods envisaged by the course program.
· The student must demonstrate the understanding of the concepts and theories provided by the course; be able to independently analyze complex data sets choosing the appropriate tools for specific cases; be able to write short data analysis programs; be able to assess the significance of the results.
· The student must demonstrate possession of the ability to use knowledge and concepts that will allow him/her to reason according to the logical specificity of the discipline. In particular, he/she should be able to identify the analysis method appropriate to the contexts and to propose hypotheses of analysis of non trivial cases.
· The student must show that he is able to communicate his knowledge, ideas and possible problems, clearly and using language properties.
Teaching Material
The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it
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