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| MDML7202 | Mining Geostatistics (Eng.) | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of MINING ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | -- | | Co-Lecturer | None | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | The course is designed for students to apply geostatistics to practical problems occurring in the lifetime of a mine. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | know the main data collection methods used in resource estimation | 1,2,10 | 1 | | PO - 2 : | learn and apply traditional manipulation techniques to determine resource tonnages and grades | 1,2,10 | 1,3 | | PO - 3 : | perform statistical and geostatistical estimation techniques | 1,2,10 | 1,3 | | PO - 4 : | have good knowledge of how industry uses software for the more complex modelling and grade interpolation situations | 6,10 | 1,3 | | PO - 5 : | provide a coherent rationale for the use of variography and geostatistics in resource estimation | 6,10 | 1,3,4 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Geostatistical tools, data mining, spatial modelling, linear methods of estimation, distribution modelling, non-linear methods of estimation, simulations: for continuous variables, for categorical variables-facies, other geostatistical methods (kriging, etc.), mining applications: at the exploration/pre-feasibility stage, at the feasibility stage, in production. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction | | | Week 2 | Data collection/manipulation | | | Week 3 | Traditional estimation techniques | | | Week 4 | Variography in resource estimation | | | Week 5 | Local estimation techniques | | | Week 6 | Block modelling | | | Week 7 | Geostatistical estimation (variography and kriging) | | | Week 8 | Mid-term exam | | | Week 9 | Resource classification | | | Week 10 | Distribution modelling | | | Week 11 | Simulations for continuous variables | | | Week 12 | Simulations for categorical variables-facies | | | Week 13 | Mining applications at the exploration/pre-feasibility stage | | | Week 14 | Mining applications at the feasibility stage | | | Week 15 | Mining applications in production | | | Week 16 | End-of-term exam | | | |
| 1 | Large, D., 1998, Statistical Evaluations in Exploration for Mineral Deposits, Springer, p. 379. | | | |
| 1 | Wellmer, F.W., 1986, Economic Evaluations in Exploration, Springer, p.163 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 8/04/2016 | 1,5 sa | 30 | | In-term studies (second mid-term exam) | 13 | 6/05/2016 | 1.5 sa | 20 | | End-of-term exam | 15 | 27/05/2016 | 1,5 sa | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Sınıf dışı çalışma | 4 | 14 | 56 | | Ödev | 5 | 1 | 5 | | Total work load | | | 61 |
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