Overview

Mineral resource estimation has changed considerably in the past 25 years: geostatistical techniques have become commonplace and continue to evolve; computational horsepower has revolutionized all facets of numerical modeling; mining and processing operations are often larger; and uncertainty quantification is becoming standard practice. Recent books focus on historical methods or details of geostatistical theory. So there is a growing need to collect and synthesize the practice of modern mineral resource estimation into a book for undergraduate students, beginning graduate students, and young geologists and engineers. It is especially fruitful that this book is written by authors with years of relevant experience performing mineral resource estimation and with years of relevant teaching experience. This comprehensive textbook and reference fills this need.

Full Product Details

Author:   Mario E. Rossi ,  Clayton V. Deutsch
Publisher:   Springer
Imprint:   Springer
Edition:   Softcover reprint of the original 1st ed. 2014
Weight:   9.492kg
ISBN:  

9789401776509

ISBN 10:   9401776504
Pages:   332
Publication Date:   30 April 2017
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1  Introduction 1.1  Objectives and Approach 1.2  Scope of Resource Modeling 1.3  Critical Aspects 1.4  Historical Perspective 1.5 References   2  Statistical Tools and Concepts 2.1  Basic Concepts 2.2  Probability Distributions 2.3  Spatial Data Analysis 2.4  Gaussian Distribution and Data Transformations 2.5  Data Integration and Inference 2.6  Exercises 2.7 References   3  Geological Controls and Block Modeling 3.1  Geological and Mineralization Controls 3.2  Geologic Interpretation and Modeling 3.3  Visualization 3.4  Block Model Setup and Geometry 3.5  Summary of Minimum, Good and Best Practices 3.6  Exercises 3.7 References   4  Definition of Estimation Domains 4.1  Estimation Domains 4.2  Defining the Estimation Domains 4.3  Case Study: Estimation Domains Definition for the Escondida Mine 4.4  Boundaries and Trends 4.5  Uncertainties Related to Estimation Domain Definition 4.6  Summary of Minimum, Good and Best Practices 4.7  Exercises 4.8 References   5  Data Collection and Handling 5.1  Data 5.2  Basics of Sampling Theory 5.3  Sampling Quality Assurance and Quality Control  5.4  Variables and Data Types 5.5  Compositing and Outliers 5.6  Density Determinations 5.7  Geometallurgical Data 5.8  Summary of Minimum, Good and Best Practices 5.9  Exercises 5.10 References   6  Spatial Continuity 6.1  Concepts 6.2  Experimental Variograms and Exploratory Analysis 6.3  Modeling 3-D Variograms 6.4  Multivariate Case 6.5  Summary of Minimum, Good and Best Practices 6.6  Exercises 6.7 References   7  Mining Dilution 7.1  Recoverable vs. In-Situ Resources 7.2  Types of Dilution and Ore Loss 7.3  Volume-Variance Correction 7.4  Information Effect 7.5  Summary of Minimum, Good and Best Practices 7.6  Exercises 7.7 References   8  Recoverable Resources: Estimation 8.1  Goals and Purpose of Estimation 8.2  Kriging Estimators 8.3  CoKriging 8.4  Block Kriging 8.5  Kriging Plans 8.6  Summary of Minimum, Good and Best Practices 8.7  Exercises 8.8 References   9  Recoverable Resources: Probabilistic Estimation 9.1  Conditional Distributions 9.2  Gaussian-based Kriging Methods 9.3  Indicator Kriging 9.4  Summary of Minimum, Good and Best Practices 9.5  Exercises9.6 References   10  Recoverable Resources: Simulation 10.1  Simulation versus Estimation 10.2  Continuous Variables: Gaussian-based Simulation 10.3  Continuous Variables: Indicator-based Simulation 10.4  Simulated Annealing 10.5  Simulating Categorical Variables 10.6  Co-simulation: Using Secondary Information and Joint Conditional Simulations 10.7  Post Processing Simulated Realizations 10.8  Summary of Minimum, Good and Best Practices 10.9  Exercises 10.10 Reference   11  Resource Model Validations and Reconciliations 11.1  The Need for Checking and Validating the Resource Model 11.2  Resource Model Integrity 11.3  Resampling 11.4  Resource Model Validation 11.5  Comparisons with Prior and Alternate Models 11.6  Reconciliations 11.7  Summary of Minimum, Good and Best Practices 11.8  Exercises 11.9 References   12  Uncertainty and Risk 12.1  Models of Uncertainty 12.2  Assessment of Risk 12.3  Resource Classification and Reporting Standards 12.4  Summary of Minimum, Good and Best Practices 12.5  Exercises 12.6 References   13  Short Term Models 13.1  Limitations of Long-term Models for Medium-term Planning 13.2  Medium- and Short-term Modeling 13.3  Selection of Ore and Waste 13.4  Selection of Ore and Waste: Simulation-based Methods 13.5  Practical and Operational Aspects of Grade Control 13.6  Summary of Minimum, Good and Best Practices 13.7  Exercises 13.8 References   14  Case Studies 14.1  The 2003 Cerro Colorado Resource Model 14.2  Multiple Indicator Kriging: São Francisco Gold Deposit 14.3  Modeling Escondida Norte’s Oxide Units with Indicators 14.4  Multivariate Geostatistical Simulation at Red Dog Mine 14.5  Uncertainty Models and Resource Classification: The Michilla Mine Case Study 14.6  Grade Control at the San Cristóbal Mine 14.7 Geometallurgical Modeling at Olympic Dam, South Australia 14.8 References   15  Conclusions 15.1  Building a Mineral Resource Model 15.2  Assumptions and Limitations of the Models Used 15.3  Documentation and Audit Trail Required 15.4  Future Trends 15.5 References   Index

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