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| Version 2013-2014 |
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| GEOL0097-1 | Geostatistics
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| Duration : | 30h Th, 30h Labo., 30h Proj. |
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| Number of credits : |
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| Lecturer : | Eric Pirard |
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Language(s) of instruction :
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| English language |
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Course contents :
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| 1. General principles of spatial modeling
1.1. Probabilistic vs. Deterministic Modelling
1.2. Standard techniques of deterministing modelling (recap.)
2. Introduction to regionalized variables
2.1. Random variable and random function.
2.2. Random function and regionalized variable
2.3. Characterization of the spatial law
2.4. The covariance function
2.5. The theoretical variogram
2.6. Ergodic and stationarity hypotheses
3. Modelling the variogram
3.1. The theoretical and the experimental variogram
3.2. Variogram models
3.3. Omnidirectional variogram modelling
3.4. Modelling the anisotropy
4. Spatial multivariate analysis
4.1. Bivariate statistics (correlation)
4.2. Multivariate statistics (correlation matrix)
4.3. Cross-Variogram and co-regionalisation
5. Local grade estimation
5.1. Conditions for optimization of a non-biased linear estimator
5.2. Simple Kriging
5.3. Ordinary Kriging
5.4. Kriging in presence of a spatial drift
5.5. Influence of the geometry of the neighbourhood and variogram shape on the estimation
5.6. Cross-validation
6. Kriging and secondary information
6.1. Stratified Kriging
6.2. Co-kriging
7. Local uncertainty and local distribution estimation
7.1. Kriging and estimation error
7.2. MultiGaussian approach
7.3. Indicator kriging
8. Spatial uncertainty and geostatistical simulations
9. Mining applications
9.1. Kriging and change of support (bloc kriging)
9.2. Classification of ore resources.
9.3. Tonage/grade curves
9.4. Open Pit Optimization
10. Petroleum applications and reservoir modelling
11. Geo-environmental applications |
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Learning outcomes of the course :
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| 1) To present the main geostatistical inference tools (advantages and drawbacks)
2) To acquire a good mastership of the most utilised concepts
3) To provide the basis for understanding themost advanced papers on spatial inference
4) To learn about the most common professional geostatistical applications |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| « Analyse spatiale des données géo-environnementales». Prof. E. PIRARD |
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Planned learning activities and teaching methods :
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| All partical sessions will be organized using free softwares such as VARIOWIN and SGEMS. |
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Mode of delivery (face-to-face ; distance-learning) :
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| 2h théorie suivies de 2h TP dirigés ou libre (selon matière) |
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Recommended or required readings :
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| Copy of all PPT used for teaching.
Main reference :
Goovaerts P., 1997, Geostatistics for natural resources estimation, Oxford Univ. Press
Recommended Lectures :
Isaaks E. & Srivastava M., 1989, Introduction to applied geostatistics, Oxford Univ. Press
Cressie N., 1993, Statistics for Spatial Data, Wiley |
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Assessment methods and criteria :
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| L'évaluation comportera un projet personnel de géostatistique et un examen oral.
Chaque étudiant recevra un ensemble de données dont il devra réaliser la caractérisation et la modélisation spatiale au moyen des outils vus au cours. Le travail sera remis avant l'examen oral et fera éventuellement l'objet de questions complémentaires lors de celui-ci.
Oral examination will bear on theoretical principles seen during the course and will include the lecture/understanding of three research papers.
L'examen oral portera sur les principes théoriques vus au cours.
The final notation will be a weighted average : 75% (oral examination) + 25% (personal work). None of these two notes being inferior ro 7/20.
If the oral examination is < 10/20 only this last note will be taken into account. |
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Work placement(s) :
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| None |
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Organizational remarks :
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| Tuesday 08h30-12h30
B52 -1/426 |
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Contacts :
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| Mlle Nadia ELGARA
Secrétariat GeMMe
Bât B52
Tél. : 366.37.99
nelgara@ulg.ac.be
Arnaud CALIFICE, Bât B52, Tél. : 366.95.25 - arnaud.califice@ulg.ac.be |
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