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.
1. Chapter 1. Introduction to ore reserves estimation - p. 1 2. Chapter 2. Essential statistics and exploratory data analysis - p. 35 3. Chapter 3. Introduction to sampling and relevant errors - p. 83 4. Chapter 4. The stochastic model of estimation - p. 99 5. Chapter 5. Variograms and the structural analysis of a random function - p. 121 6. Chapter 6. Fitting theoretical models of variograms - p. 151 7. Chapter 7. Estimation of in situ resources - p. 179 8. Chapter 8. Verifying the accuracy of the estimation model - p. 217 9. Chapter 9. Multivariate geostatistics - p. 245 10. Chapter 10. Simulation of a random function - p. 291 11. Chapter 11. Classification schemes - p. 337 12. Chapter 12. Case studies - p. 355 13. Index - p. 405
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.
1. Chapter 1. Introduction to ore reserves estimation - p. 1 2. Chapter 2. Essential statistics and exploratory data analysis - p. 35 3. Chapter 3. Introduction to sampling and relevant errors - p. 83 4. Chapter 4. The stochastic model of estimation - p. 99 5. Chapter 5. Variograms and the structural analysis of a random function - p. 121 6. Chapter 6. Fitting theoretical models of variograms - p. 151 7. Chapter 7. Estimation of in situ resources - p. 179 8. Chapter 8. Verifying the accuracy of the estimation model - p. 217 9. Chapter 9. Multivariate geostatistics - p. 245 10. Chapter 10. Simulation of a random function - p. 291 11. Chapter 11. Classification schemes - p. 337 12. Chapter 12. Case studies - p. 355 13. Index - p. 405
