Study Programmes 2016-2017
| GEOL0097-2 | ||||||||
| Geostatistics | ||||||||
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Duration :
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| 30h Th, 30h Labo. | ||||||||
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Number of credits :
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Lecturer :
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| Thomas Hermans, Eric Pirard | ||||||||
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Coordinator :
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| N... | ||||||||
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Language(s) of instruction :
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| English language | ||||||||
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Organisation and examination :
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| Teaching in the first semester, review in January | ||||||||
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Units courses prerequisite and corequisite :
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| Prerequisite or corequisite units are presented within each program | ||||||||
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Learning unit contents :
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| 1. Computers and Geosciences
2. Statistical terminology and data typology 3. Principles of geological monitoring and spatial sampling 4. Exploratory Data Analysis Univariate Visualisation (histograms, box plot) Univariate analysis (percentiles, mean, variance,...) Identification of outliers Principles of data levelling Bivariate Visualisation (scatterplots) Bivariate Analysis (covariance, correlation,...) 5. Spatial Exploratory Data Analysis Terminology an Notations Data posting Local Analysis - Moving window Declustering Spatial Correlation Analysis The experimental variogram Variogram Modeling 6. General principles of spatial modeling Probabilistic vs. Deterministic Modelling Validation of a model Spatial deterministic inference 7. Introduction to regionalized variables Random variable, random function and regionalized variable Joint random variables The covariance and variogram function Stationarity The theoretical and the experimental variogram 8. Kriging The kriging problem Ordinary Kriging equations An intuitive look at ordinary kriging weights Spatial contuinity model influence on kriging weight Properties of the kriging estimate Simple kriging Kriging with trend 9. Change of support and block kriging Importance of the support on statistics Effect of the support on estimates Affine and indirect lognormal corrections Total variance and variance within block Block kriging 10. Estimation with secondary data Secondary information Kriging within strata Kriging with local varying mean Kriging with external drift Cross-covariance and cross-variogram Co-kriging 11. Uncertainty of the estimation Cross-validation Uncertainty of the local estimate Confidence interval Multi-Gaussian approach Indicator kriging 12. Simulations Kriging limitations Simulations and spatial uncertainty Sequential simulation algorithm Sequential Gaussian simulations Sequential Indicator simulations 13. Multiple-point statistics The limitations of the variogram The training image to describe higher order statistics Sequential simulation with TI: snesim algorithm Modeling non-stationary trend Using secondary data Direct sampling algorithm Distance-based vs probability-based Integration of secondary data Pattern-based simulation : Image quilting 14. Applications of geostatistics in Inverse problems Stochastic inversion Importance of the prior distribution Sampling the posterior distribution Optimization problem |
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Learning outcomes of the learning unit :
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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 the most advanced papers on spatial inference 4) To learn about the most common professional geostatistical applications |
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Prerequisite knowledge and skills :
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| Probability and Statistics (basic course) | ||||||||
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Planned learning activities and teaching methods :
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| All partical sessions will be organized using programming in MATLAB, PYTHON and SGEMS. | ||||||||
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Mode of delivery (face-to-face ; distance-learning) :
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| 2h theory followed by 2h of supervised practice | ||||||||
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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 Mariethoz G. & Caers J., 2014, Multiple-point statistics : Stochastic Modeling with Training Images, Wiley-Blackwell |
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Assessment methods and criteria :
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| Evaluation will bear on a personal project performed by the student and an oral examination.
Each student will receive a set of data and will habe to characterize and spatial model the data set with the tools seen during the course. The written work will be submitted before the oral examination and will be subject to additional questions/oral presentation during the exam. The oral examination will bear on the theoretical principles. The final notation will be a weighted average : 75% (oral examination) + 25% (personal work). None of these two notes being inferior to 8/20. 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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Organizational remarks :
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| Full English | ||||||||
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Contacts :
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| Mlle Nadia ELGARA Secrétariat GeMMe Bât B52 Tél. : 366.37.99 nelgara@ulg.ac.be | ||||||||