The natural resources on the earth seem to be randomly distributed but their variations over space and time are not all random. They exhibit a spatial correlation. This spatial correlation can be captured by geostatistics. Geostatistics deals with the analysis and modelling of geo-referenced data. The point observations are analyzed and interpolated to create spatial maps. For geostatistical interpolation, first the spatial correlation structures of the parameter of interest are quantified and then spatial interpolation is done using the quantified spatial correlation and optimal predictions at unobserved locations to create a map.

This reader-friendly introduction to geostatistics provides a lifeline for students and researchers across the Earth and environmental sciences who until now have struggled with statistics. Using simple and clear explanations for both introductory and advanced material, it demystifies complex concepts and makes formulas and statistical tests easy to understand and apply.

Since October 1975 and the first NATO ASl "Geostat - Roma 1975", there has not been an advanced workshop on geostatistics where experts from throughout the world could meet, teach and debate without the pressure of an ordinary professional conference. This second NATO ASI "Geostat - Tahoe 1983" was intended as a high-level teaching activity yet opened to all new ideas and contributions to the discipline of geostatistics. It was expected that the institute would bridge the gap since "Geostat - Roma 1975" and establish the state of the art of the discipline as of 1983 <...>

The organization of this book follows the typical mining sequence that starts with the exploration phase aimed at delineating the mineral deposit and detailing its geology.

Geological objects can be singular, like the inverted cone-shaped diatreme of the El Teniente copper mine; more generally, they can be domains with given lithological, mineralogical, structural or alteration properties, which will be designated throughout this book under the name of ‘facies’, commonly used in geosciences. Within each facies, the metal grades have their own distribution and variability. This peculiarity will lead directly to the prediction or simulation of the grades (quantitative variables) taking into account the statistical and spatial characteristics of each facies (categorical variable).

We emphasize these four terms, which will be found throughout this book: prediction, simulation, quantitative variable and categorical variable <...>