| Course Unit Code | 541-0602/04 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Choice-compulsory type B |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| STA22 | doc. RNDr. František Staněk, Ph.D. |
| Summary |
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| Basic properties and modeling of natural objects. Introduction to geostatistics. Random function. Regionalized variable. Stationary and intrinsic hypotheses. Variogram. Anisotropies, drift, models for variograms. Experimental variograms. Structural analysis. Dispersion as a function of block size. Local estimation – kriging. Ordinary kriging. Point kriging and block kriging. Simple kriging. Universal kriging. Cokriging. Nonlinear kriging. Soft kriging. Lognormal kriging. Indicator kriging. Probability kriging. Cross validation. Principles of stochastic simulation. Sequential Gaussian simulation (SGSIM) and direct sequential simulation (DSSIM). |
| Learning Outcomes of the Course Unit |
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This course is dedicated to basic properties and modelling of natural objects. These problems can arise from other courses as well as from practice. The main emphasis lays in explanation of fundamental principles of geostatistical methods and of their general properties. The students learn how to decide which geostatistical procedure is a suitable tool for solving a specific problem. An important ingredient of the course is learning how to use existing software specialized for geostatistical computations, too.
The first part of the course deals with basic geostatistical notions and with the way in which to understand these notions from both theoretical and practical points of view.
In the second part of the course the students learn the geostatistical way of thinking as a mean of understanding real-life processes. The basic methods of collecting and analysing geo-data are introduced. The students are taught how to use these general methods to solve the problems arising from other courses of their study and from practice. |
| Course Contents |
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1. Basic concepts of probability theory.
2. Basic concepts of mathematical statistics, mathematical statistics in geology.
3. Basic properties and modeling of geological bodies. Introduction to geostatistics. Development and practical significance. Application of geostatistics in geology, mining industry and other fields.
4. Random function. Spatial variable quantity.
5. Characteristics of a random function.
6. Stationarity of a random function and the intrinsic hypothesis.
7. Empirical (experimental) and theoretical variogram. Types of theoretical variograms.
8. Interpretation and progress of the experimental variogram. Drift. Structural analysis – definition and tasks.
9. Linear geostatistical kriging procedures – principle, type of input data distribution, point and block estimation.
10. Basic and universal kriging.
11. Other types of kriging. Cross validation method.
12. General procedure and examples of kriging application. Global estimates. Limitation of kriging.
13. Principles of stochastic simulation procedures. |
| Recommended or Required Reading |
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| Required Reading: |
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DEUTSCH, C., V.: Geostatistical Reservoir modeling. Oxford, 2002.
DEUTSCH, C., V., JOURNEL, A., G.: GSLIB – Geostatistical Software Library and User's Guide. Oxford, 1998.
GOOVAERTS, P.: Geostatistics for Natural Resourses Evaluation. Oxford, 1997.
REMY, N., BOUCHER, A., WU, J.: Applied geostatistics with SGeMS: a user's guide. New York: Cambridge University Press, 2009. |
STANĚK, F.: Tvorba modelu ložiska uhlí a způsoby jeho hodnocení. Sborník vědeckých prací VŠB-TU Ostrava, řada hornicko-geologická, monografie 14, 2005.
REMY, N., BOUCHER, A., WU, J.: Applied geostatistics with SGeMS: a user's guide. New York: Cambridge University Press, 2009.
ARMSTRONG, M.: Basic Linear Geostatistics. Berlin, 1998.
DEUTSCH, C., V.: Geostatistical Reservoir modeling. Oxford, 2002. |
| Recommended Reading: |
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CLARK, I., HARPER, W.,V.: Practical Geostatistics. Ecosse North America Llc, Columbus Ohio, USA, 2000.
KITANIDIS, P.,K.: Introduction to Geostatistics: Applications to Hydrogeology. New York, 1997.
WEBSTER, R., OLIVER, M. A.: Geostatistics for Environmental Scientists. Wiley, 2007.
LANTUÉJOUL, CH.: Geostatistical Simulation: Models and Algorithms. Springer, 2002. |
JEŽEK, J.: Geostatistika a prostorová interpolace. Karolinum, 2015.
CLARK, I., HARPER, W.,V.: Practical Geostatistics. Ecosse North America Llc, Columbus Ohio, USA, 2000.
KITANIDIS, P.,K.: Introduction to Geostatistics: Applications to Hydrogeology. New York, 1997.
WEBSTER, R., OLIVER, M. A.: Geostatistics for Environmental Scientists. Wiley, 2007. |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials, Project work |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 33 | 17 |
| Examination | Examination | 67 | 18 |