Preface; Acknowledgments; Organizing Committee; Scientific Committee; Contents; Part I: In Honor of Professor Danie Krige; Professor Danie Krigeś First Memorial Lecture: The Basic Tenets of Evaluating the Mineral Resource Assets of Mining Companies, as Observed in Professor Danie Krige's Pioneering Work Over Half a Century; 1 Introduction; 2 The Great Man: Professor Danie Krige; 2.1 Family and Faith; 2.2 Career, Achievements and Awards; 2.2.1 Career; 2.2.2 Dedicated Service; 2.2.3 Achievements and Awards

3 Professor Danie Krigeś Work on Essential Tenets in Evaluating the Mineral Resource Assets of Mining Companies3.1 Historical Background and Motivation: The Capital Intensiveness of Mining; 3.2 Essential Tenets in Evaluating Mineral Resource Assets of Mining Companies Based on Over Half a Century of Professor Krige...; 3.2.1 Data Integrity; 3.2.2 Geology Models; 3.2.3 Geostatistics Technology: Technique Selection and Optimal Application; 3.2.4 Frequency Distribution; 3.3 Spatial Concepts and the Birth of Geostatistics and Kriging; 3.4 Spatial Structure and Variograms

3.5 Conditional Unbiasedness3.6 Conditional Biases; 3.6.1 What Contributes to Conditional Biases; 3.6.2 Practical Examples of Outcomes of Conditional Biases; 3.6.3 Conditional Biases: Testing Tools; 3.6.4 The Efficiency of Block Evaluations; 3.6.5 Critical Control Limit Test for Kriged Block Evaluations; 3.6.6 Smoothing Effect of Kriging; 4 Conclusion; 4.1 Professor Danie Krigeś Basic Points of Advice for the Practitioner; 4.2 Final Thoughts; Bibliography; Part II: Theory; Functional Decomposition Kriging for Embedding Stochastic Anisotropy Simulations; 1 Introduction; 2 Theory

2.1 Functional Decomposition of the Random Field Z(x)2.2 Functional Decomposition Kriging (FDK); 2.3 Kriging Data from Functions; 2.4 Stochastic Models for Nonlinear Client Algorithms; 3 Practical Modeling Accounting for Heterogeneous Anisotropy; 3.1 Geostatistics with Input Anisotropy Tensor Fields; 3.2 Predicted Stochastic Anisotropy Fields for Structural Uncertainty; 4 Discussion and Conclusion; Bibliography; Can Measurement Errors Be Characterized from Replicates?; 1 Introduction; 2 Three Models of Measurement Errors; 2.1 Additive Error, Not Correlated with Grade

2.2 Additive Error Correlated with Grade2.3 Measurement Error of Multiplicative Type; 2.4 Contribution and Limits of the Variogram Analysis; 3 Kriging Within a Model of Additive Error Not Correlated with Grade; 4 Conclusion; Bibliography; Modelling Asymmetrical Facies Successions Using Pluri-Gaussian Simulations; 1 Introduction; 2 Methodology; 2.1 Context and Notations; 2.2 Relation Between the Indicators and Gaussian Functions; 2.3 The Spatial Shift Applied to the Linear Model of Co-regionalization; 3 Results; 3.1 Analytical Study of the Asymmetry