Autorentext

Dr Konstantinos Modis is a Mining Engineer with more than 30 years of academic and industrial experience in Earth Sciences. He obtained a PhD in geostatistics studying mixed sulphide ore deposits in Greece. In his career, he has fulfilled different roles in research, exploration and mining geostatistics, including resources engineering in Aegean Metallurgical Industries (METBA SA), where mixed sulphide ores were mined and the Olympias Gold Project was designed and permitted, and also consulting in the Hellenic Industrial and Mining Investment Corporation (HIMIC S.A.) which owned mineral deposits in Greece and Cyprus. He was also a pioneer in the development of mining software for desktop computers with advanced graphical interface. For these achievements he was granted the Geological Sciences Award Konstantinos Ktenas” of the Academy of Athens. As a Professor of Mineral Exploration and Geostatistics at the National Technical University of Athens, he has championed innovative educational methods, integrating interactive multimedia approaches. His research interests encompass the geostatistical estimation of mineral reserves and the formulation of stochastic models that incorporate natural laws in areas such as geothermal energy, rock mechanics, groundwater flow, and related fields.

Dr George Valakas is a senior researcher at the School of Mining and Metallurgical Engineering in National Technical University of Athens, where he earned his Ph.D. specializing in geostatistics and optimization methods. Within this institution, he now serves as a dedicated lecturer and actively engages in various education projects supported by EU's EIT Raw Materials programme. His educational journey began with an undergraduate degree in Statistics from Athens University of Economics and Business, and he further expanded his knowledge by obtaining a M.Sc. degree in Environmental Analysis of Terrestrial Systems from the University of Sheffield in the UK. His primary areas of expertise encompass geostatistics, optimization methods, mineral exploration, and the innovative development of interactive educational tools that adeptly integrate complex concepts and theories within the overarching framework of sustainable development principles. He is the recipient of a prestigious postdoctoral fellowship, awarded by the State Scholarships Foundation of Greece. The primary objective of this fellowship was to develop an R package for applying Plurigaussian simulation and co-simulation between facies and continuous variables, further demonstrating his commitment to advancing this field.

Klappentext

Introduction to Mining Geostatistics: Intuitive Applications with Excel and R is a practical and accessible guide to geostatistical techniques in mineral exploration, with a strong focus on reserves estimation. Designed for students, researchers, and industry professionals, this book blends fundamental concepts of theory with hands-on applications, using Excel and R to simplify complex analyses.

Key topics include:
Essential Statistical Foundations - Master core data analysis techniques for ore reserves estimation.
Sampling Strategies & Error Analysis - Minimize uncertainty and improve data reliability.
Spatial Analysis & Kriging - Use variograms, covariance functions, and Kriging algorithms to estimate unknown values from borehole data.
Multivariate Geostatistics - Model interdependent variables to enhance accuracy and predictive power.
Stochastic Simulation - Explore alternative estimation methods for risk assessment and scenario analysis.
Reserve Classification & Reporting - Understand global classification systems and key reserve estimation parameters.

Filled with real-world case studies and practical examples, this book bridges theory and application, making geostatistics intuitive and approachable. Whether you're optimizing exploration projects, improving resource estimates, or conducting economic risk assessments, this guide equips you with the tools to make informed decisions.

Inhalt

1. Introduction to ore reserves estimation
2. Essential statistics and exploratory data analysis
3. Introduction to sampling and relevant errors
4. The stochastic model of estimation
5. Variograms and the structural analysis of a Random Function
6. Fitting theoretical models of variograms
7. Estimation of in situ resources
8. Verifying the accuracy of the estimation model
9. Multivariate geostatistics
10. Simulation of a Random Function
11. Classification schemes
12. Case studies