Developments in Geomathematics, 2: Geostatistical Ore Reserve Estimation focuses on the methodologies, processes, and principles involved in geostatistical ore reserve estimation, including the use of variogram, sampling, theoretical models, and variances and covariances. The publication first takes a look at elementary statistical theory and applications; contribution of distributions to mineral reserves problems; and evaluation of methods used in ore reserve calculations. Concerns cover estimation problems during a mine life, origin and credentials of geostatistics, precision of a sampling campaign and prediction of the effect of further sampling, exercises on grade-tonnage curves, theoretical models of distributions, and computational remarks on variances and covariances. The text then examines variogram and the practice of variogram modeling. Discussions focus on solving problems in one dimension, linear combinations and average values, theoretical models of isotropic variograms, the variogram as a geological features descriptor, and the variogram as the fundamental function in error computations. The manuscript ponders on statistical problems in sample preparation, orebody modeling, grade-tonnage curves, ore-waste selection, and planning problems, the practice of kriging, and the effective computation of block variances. The text is a valuable source of data for researchers interested in geostatistical ore reserve estimation.

Table of Contents



List of Notations

List of Abbreviations

Chapter 1 Elementary Statistical Theory and Applications

1.1 The Vocabulary of Statistics in Mineral Resources Estimation

1.1.1 Universe

1.1.2 Sampling Unit and Population

1.1.3 Characterization of a Population

1.2 a Few Lines of Theory

1.2.1 A Random Variable

1.2.2 Probability Distribution

1.2.3 Characterization of a Distribution

1.3 Theoretical Models of Distributions

1.3.1 The Normal Distribution

1.3.2 The Lognormal Distribution

1.3.3 The Binomial Distribution

1.3.4 The Poisson Distribution

1.3.5 The Negative Binomial Distribution

1.4 Independent Random Variables and Dependent Random Variables

1.4.1 Definition of Independence

1.4.2 Examples

1.4.3 The Covariance of Two Random Variables

1.4.4 Covariance and Correlation Coefficient

1.5 Correlation and Regression

1.5.1 Regression Lines

1.5.2 Normal Regression

1.6 Computational Remarks on Variances and Covariances

1.6.1 Multiplying a Variable by a Constant

1.6.2 Adding Two Random Variables

1.6.3 Taking a Linear Combination of Random Variables

Chapter 2 Contribution of Distributions to Mineral Reserve Problems

2.1 The Precision of a Sampling Campaign and Prediction of the Effect of Further Sampling

2.1.1 The Standard Error of the Mean

2.1.2 Conditions of Use

2.1.3 Example of Use in the Normal Case; Confidence Interval and Risk

2.1.4 Example of Use of Sichel's Tables i


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© 1977
Elsevier Science
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@qu:This book, by a leading geostatistical authority and teacher, is claimed to be the first comprehensive textbook in English on the subject and it certainly fills this need, both for the student and the qualified mining engineer. @source: World Mining