Description
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.- Includes templated spreadsheet examples and exercises in Excel and R for accessible understanding- Provides geometric instead of algebraic representation wherever possible- Detailed visualization of geostatistics theory throughout the chapters
Table of Contents
1. Introduction to ore reserves estimation2. Essential statistics and exploratory data analysis3. Introduction to sampling and relevant errors4. The stochastic model of estimation5. Variograms and the structural analysis of a Random Function6. Fitting theoretical models of variograms7. Estimation of in situ resources8. Verifying the accuracy of the estimation model9. Multivariate geostatistics10. Simulation of a Random Function11. Classification schemes12. Case studies
