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Introduction to Mining Geostatistics
◆日本地質学会第132年学術大会セール 開催中！：2025年10月24日(金)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Title: Introduction to Mining Geostatistics Subtitle: Intuitive Applications With Excel and R Author: Modis, Konstantinos (Professor, National Technical University of Athens, Athens, Greece) / Valakas, George (Senior Researcher, School of Mining and Metallurgical Engineering, National Technical University of Athens (NTUA), Greece) Publisher: Elsevier ISBN: 9780443314803 Cover: PAPERBACK Date: 2025年11月 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. TABLE OF CONTENTS 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
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