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.

Presents an applied approach to the estimation of mineral resources/reserves. It is suitable for any university or mining school that offers courses on mineral resource/reserve estimation. It will also be valuable for professional mining and geological engineers and geologists working with mineral exploration companies.

A novel, practical approach to modeling spatial uncertainty. This book deals with statistical models used to describe natural variables distributed in space or in time and space. It takes a practical, unified approach to geostatistics-integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This unique approach facilitates realistic modeling that accounts for the complexity of natural phenomena and helps solve economic and development problems-in mining, oil exploration, environmental engineering, and other real-world situations involving spatial uncertainty. Up-to-date, comprehensive, and well-written, Geostatistics: Modeling Spatial Uncertainty explains both theory and applications, covers many useful topics, and offers a wealth of new insights for nonstatisticians and seasoned professionals alike. This volume: * Reviews the most up-to-date geostatistical methods and the types of problems they address. * Emphasizes the statistical methodologies employed in spatial estimation. * Presents simulation techniques and digital models of uncertainty. * Features more than 150 figures and many concrete examples throughout the text. * Includes extensive footnoting as well as a thorough bibliography. Geostatistics: Modeling Spatial Uncertainty is the only geostatistical book to address a broad audience in both industry and academia. An invaluable resource for geostatisticians, physicists, mining engineers, and earth science professionals such as petroleum geologists, geophysicists, and hydrogeologists, it is also an excellent supplementary text for graduate-level courses in related subjects.