In August 1882, the Morgan brothers recognised a mineral deposit, now known as the Mount Morgan Gold-Copper Deposit. The final production figures for the mine were 250 tonnes of gold and 360,000 tonnes of copper from 50 million tonnes of ore, making the average grades 4.99g/t gold and 0.72% copper.

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator krigingo is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into  pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used. <...>

Geostatistics (also known as kriging) was developed for the mining industry during the 1960s and 1970s to estimate changes in ore grade. The principles of geostatistics are now applied to many applications that require statistically based interpolation techniques. Geostatistics provides a data value estimate for locations that cannot be sampled directly by examining data taken at locations that can be sampled.

Many geostatistical variables have sample distributions that are highly positively skewed. Because of this, significant deskewing of the histogram and reduction of variance occurs when going from sample to block support, where blocks are of larger volume than samples. When making estimates in both mining and non-mining applications we often wish to map the spatial distribution on the basis of block support rather than sample support.

The discipline which is now known as geostatistics began to develope over thirty years ago for mining evaluation and since has extended to other fields of activity. Around 1960 in particular, G. Matheron built linear geostatistics. Some of its tools (variogram, kriging) are widely used nowadays. Linear geostatistics makes it possible for instance to evaluate the metal content of a mining block or panel by estimating the mean of the grades of the points in it from samples. The reader of this book is supposed to be familiar with linear geostatistics. <...>

GSLIB is the name of a directory containing the geostatistical software developed at Stanford. Generations of graduate students have contributed to

this constantly changing collection of programs, ideas, and utilities. Some of the :nost widely used public-domain geostatistical software [58, 62, 721 and many more in-house programs were initiated from GSLIB. It was decided to open the GSLIB directory to a wide audience, providing the source code to seed new developments, custom-made application tools, and, it is hoped, new theoretical advances <...>

Geostatistics, developed originally in the mining industry from the 1950s onwards, is now being applied widely in environmental science for mapping, monitoring and management. It is based on the theory of random spatial processes. There are numerous examples in soil science, meteorology, agronomy, hydrology, ecology and some aspects of marine science. By taking into account and modelling spatial correlation, geostatistics provides unbiased predictions of environmental variables with minimum and known variance in ways that no other method does. The general technique of prediction is known as kriging. It requires a mathematical model to describe the spatial covariance, usually expressed as a variogram, which in its parameterized form has become the central tool of geostatistics. Successful kriging and estimation of the variogram depend on sampling adequately without bias and with suitable spatial configurations and supports. These differ somewhat from design-based estimation with its emphasis on random sampling. <...>

Крайгинг является геостатическим способом оптимизации средне-блочных оценок геолого-промышленных параметров месторождении мощности рудных тел, содержаний, метропроцентов и запасов металлов. Дополнительная по сравнению с другими методами точность оценки достигается на основе привлечения информации о структуре ространственной изменчивости подсчетных параметров, геометрии разведочной сети, системы опробования и оцениваемых объемов недр.

В обзоре на основе наиболее популярной теоретической модели стационарной случайной функции - рассматривается основная задача крайгинга и методы ее решения. Одна из наименее изученных проблем заключается в исследовании влияния особенностей геологического строения месторождений на результаты крайгинга. Возможности этого подхода демонстрируются на примере группы стратиформных колчедан ных месторождений.

В качестве структурной характеристики оцениваемых при помоад крайгинга рудных объектов используется специальная дистанционная геостатистическая функция - вариограмма.Приводится обэор надежных способов оценки вариограмм-по данным разведочного опробования месторождений. Структурный анализ вариограмм включает выявление общих характеристик изменчивости оцениваемой пространственной переменной: общий уровень изменчивости, ее анизотропию, характер, состав и степень коррелированности пространственных вариаций, непрерывность и однородность оруденения, размер зоны влияния проб. Рассмотрены возможности использования различных теоретических моделей вариограмм - сферической, "затухающего" синуса, экопоненциально-косинусной - для оценки стратиформных колчеданных месторождений.

Обратимся собственно к проблеме оценивания значения непрерывной переменной V в произвольной точке х пространства S. Основанием для этого будет служить набор из и измерений, сделанных в точках х1,x2 ,...,хn пространства. Базовой моделью оценивания в геостатистике является кригинг. Термин кригинг был взят на вооружение для обозначения семейства алгоритмов пространственной регрессии. Само слово кригинг происходит от имени ученого Д. Крита (D. Krige). в честь которого Г. Матерон и назвал базовые геостатистические модели.