Mineral Resource Estimation

ISBN-13: 9789401776509 / Angielski / Miękka / 2017 / 332 str.

Mario E. Rossi; Clayton V. Deutsch

Opis

This book collects and synthesizes the practice of modern mineral resource estimation, taking on geostatistical techniques, advances in computing power and numerical modeling, larger mining and processing operations and uncertainty quantification.

Spis treści

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  Exercises

9.6 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

Blurb

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.