The distribution of ore grades within a deposit is of mixed character, being partly structured and partly random. On one hand, the mineralizing process has an overall structure and follows certain laws, either geological or metallogenic; in particular, zones of rich and poor grades always exist, and this is possible only if the variability of grades possesses a certain degree of continuity. Depending upon the type of ore deposit, this degree of continuity will be more or less marked, but it will always exist; mining engineers can indeed be thankful for this fact because, otherwise, no local estimation and, consequently, no selection would be possible. However, even though mineralization is never so chaotic as to preclude all forms of forecasting, it is never regular enough to allow the use of a deterministic forecasting technique . This is why a scientific (at least, simply realistic) estimation must necessarily take into account both features - structure and randomness inherent in any deposit. Since geologists stress the first of these two aspects, and statisticians stress the second, I proposed, over 15 years ago, the name geostatistics to designate the field which synthetizes these two features and opens the way to the solution of problems of evaluation of mining deposits <...>

Proceedings of the seventh European conference on geostatistics for environmental applications / Материалы Седьмой Европейской конференции по геостатистике для применения в окружающей среде

Characterising spatial and temporal variation in environmental properties, generatingmaps from sparse samples, and quantifying uncertainties in the maps, are key concerns across the environmental sciences. The body of tools known as geostatistics offers a powerful means of addressing these and related questions. This volume presents recent research in methodological developments in geostatistics and in a variety of specific environmental application areas including soil science, climatology, pollution, health, wildlife mapping, fisheries and remote sensing, amongst others.

In the fall of 1988 the Society of Petroleum Engineers (SPE) held a forum on reservoir characterization in Grindenwald, Switzerland. Many of the authors who have contributed to this volume were at that forum discussing their ideas on stochastic methods for reservoir characterization. All of these ideas were then still quite new and largely untested; indeed, some of them had not been reduced to practice, but were merely the wild imaginings of creative and curious minds. At that time, there was still a fair bit of controversy over whether stochastic methods had any relevance to the practice of modeling petroleum reservoirs.*

Nowadays, many surficial mineral deposits are being mined out, leaving only deep-seated mineral deposits for feeding raw materials into the industry. Therefore techniques applied to mineral exploration need to be revisited for discovering new mineral resources, which may be located in harsh and remote regions. Over the past decades, remote sensing technology and geographic information system (GIS) techniques have been incorporated into several mineral exploration projects worldwide. This aim is to bridge the knowledge gap for the geospatial-based discovery of buried, covered, and blind mineral deposits. This book details the main aspects of the state-of-the-art remote sensing imagery, geochemical data, geophysical data, geological data, and geospatial toolbox required to explore ore deposits. It covers advances in remote sensing data processing algorithms, geochemical data analysis, geophysical data analysis, and machine learning algorithms in mineral exploration. It also presents approaches on recent remote sensing and GIS-based mineral prospectivity modeling, which offer a piece of excellent information to professional earth scientists, researchers, mineral exploration communities, and mining companies <...>

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 <...>

Электронный учебно-методический комплекс (ЭУМК) создан для возможности самостоятельного изучения теоретических основ, методов и технологий современных геоинформационных систем, используемых в горном деле, а также для приобретения умений использовать ГИС-пакеты в работе по построению цифровых карт и последующему анализу цифровых моделей массива горных пород.
ЭУМК предназначен как для самостоятельной подготовки студентов очной и заочной формы обучения, так и для проведения практических занятий со студентами, имеющими различный уровень подготовки.

Mathematical methods have been employed by a few geologists since the earliest days of the profession. For example, mining geologists and engineers have used samples to calculate tonnages and estimate ore tenor for centuries. As Fisher pointed out (1953, p. 3), Lyell’s subdivision of the Tertiary on the basis of the relative abundance of modern marine organisms is a statistical procedure. Sedimentary petrologists have regarded grain-size and shape measurements as important sources of sedimentological information since the beginning of the last century. The hybrid Earth sciences of geochemistry, geophysics, and geohydrology require a firm background in mathematics, although their procedures are primarily derived from the non-geological parent. Similarly, mineralogists and crystallographers utilize mathematical techniques derived from physical and analytical chemistry. <...>

The geodatabase supports a model of topologically integrated feature classes, similar to the coverage model. It also extends the coverage model with support for complex networks, relationships among feature classes, and other object-oriented features. The ESRI® ArcGIS™ applications (ArcMap™, ArcCatalog™, and ArcToolbox™) work with geodatabases as well as with coverages <...>

Рассматриваются теоретические основы создания геофизических и геоинформационных систем и технологий, а также исследуется применение пространственного анализа для решения геолого-геофизических задач. Содержатся пошаговые инструкции выполнения практических заданий с использованием программного обеспечения ArcGIS 10
Учебное пособие предназначено для проведения лабораторных работ по курсу «Геофизические информационные системы» для студентов бакалавриата геологического факультета, обучающихся по направлению «Геология» 05.03.01, направленности «Геофизика». Пособие может использоваться бакалаврами других направленностей при изучении курса «Геоинформационные системы в геологии» и студентами специальности «Технология геологической разведки» 21.05.03, профиль «Геофизические методы поисков и разведки месторождений полезных ископаемых» в процессе изучения курса «Геоинформационные системы в геофизике».