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| GEOL0097-1 | Geostatistics
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| Duration : | 20h Th, 20h Pr |
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| Credits/ECTS : |
| Master in Geological and Mining Enginneering, in-deph approach, 1st year | | Toute l'année | | 3 |
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| Master in Geological and Mining Enginneering, in-deph approach, 1st year | | Second semester | | 3 |
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| Master in Geological and Mining Enginneering, in-deph approach, 2nd year | | Second semester | | 3 |
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| Master in Geological and Mining Engineering, specialized approach, 1st year | | Second semester | | 3 |
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| Master in Geological and Mining Engineering, specialized approach, 1st year | | Toute l'année | | 3 |
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| Holder(s) : | Eric Pirard |
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| Language : | French language |
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| Course contents : | 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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| Course objective : | 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 : | « Analyse spatiale des données géo-environnementales». Prof. E. PIRARD |
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| Workshops : | All partical sessions will be organized using free softwares such as VARIOWIN and SGEMS. |
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| Organization : | Le mardi matin 8h30-12h30 du premier semestre.
2h théorie suivies de 2h TP dirigés ou libre (selon matière) |
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| Written notes : | 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 : | 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.
L'examen oral portera sur les principes théoriques vus au cours. |
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| Contacts : | 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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