Overview of conventional ore reserve estimation methods and accuracy of the results.
Data accumulation and storage, descriptions of drill hole samples, geological model
and domaining. Exploration data analysis: statistical and graphical analysis of drilling
data as a whole and per geological unit, correlation between variables, definition of
selective unit of estimation (block), development of block model.
Non-parametric statistics of one, two or more Random Variables (RV). RV sequences,
vector representation of RV. The concept of Random Function (RF), simplified RF
models. Spatial correlation and variogram function, variogram models. The
projection methods with known and unknown mean (simple and ordinary Kriging
algorithms).
Structural analysis of the deposit: calculation of experimental variogram function,
isotropy and analysis of its characteristics. Reserves estimation: determination of
estimation neighborhood, application of kriging algorithm, calculation of content of
each block, calculation of errors, cross – validation of the numerical model with
graphical overview and statistical comparison, grade- tonnage curves. Classification
of ore reserves according to the estimation errors.
- Teacher: Κωνσταντίνος Μόδης
ECTS : 4
Language : el, en
Learning Outcomes : • Has understood the basic stages and the desired outcome of the Geostatistical Study of an orebody, as well as its connection with the broader economic and operational objectives it is part of. • Has knowledge of the tools and techniques of Geostatistics and how they are applied to achieve the respective goals. • Analyzes and processes the available research data of the study area and can integrate the results into a unified numerical block model, aiming to calculate mining reserves. • Implements the numerical block model on a PC using Excel, the R programming language, as well as specialized commercial software.