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Автор(ы):Jones I.
Издание:12 стр.
Язык(и)Английский
A case study using indicator kriging - the Mount Morgan gold-copper deposit, Queensland / Тематическое исследование с использованием индикаторного кригинга - золото-медное месторождение Маунт Морган, Квинсленд

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.

Автор(ы):Hengl T.
Издание:JRC Scientific and Technical Reports, 2007 г., 164 стр., ISBN: 978-92-79-06904-8
Язык(и)Английский
A practical guide to geostatistical mapping of environmental variables / Практическое руководство по геостатистическому картированию переменных окружающей среды

Geostatistics is a subset of statistics specialized in analysis and interpretation of geographically referenced data (Goovaerts, 1997; Webster and Oliver, 2001; Nielsen and Wendroth, 2003). In other words, geostatistics comprises statistical techniques that are adjusted to spatial data. Typical questions of interest to a geostatistician are:

how does a variable vary in space?

what controls its variation in space?

where to locate samples to describe its spatial variability?

how many samples are needed to represent its spatial variability?

what is a value of a variable at some new location?

what is the uncertainty of the estimate?

Издание:Wiley, 2008 г., 323 стр., ISBN: 978-1-84821-060-8
Язык(и)Английский
Advanced mapping of environmental data. Geostatistics, machine learning and bayesian maximum entropy / Расширенное картографирование экологических данных. Геостатистика, машинное обучение и байесовская система максимальной энтропии

In this introductory chapter we describe general problems of spatial environmental data analysis, modeling, validation and visualization. Many of these problems are considered in detail in the following chapters using geostatistical models, machine learning algorithms (MLA) of neural networks and Support Vector Machines, and the Bayesian Maximum Entropy (BME) approach. The term “mapping” in the book is considered not only as an interpolation in two- or threedimensional geographical space, but in a more general sense of estimating the desired dependencies from empirical data.

Автор(ы):Isaaks E.H., Srivastava R.M.
Издание:Oxford university press, Oxford, 1989 г., 577 стр.
Язык(и)Английский
An introduction to applied geostatistics / Введение в прикладную геостатистику

This began as an attempt to write the book that we wish we had read when we were trying to learn geostatistics, a task that turned out to be much more difficult than we originally envisaged. To the many people who provided encouragement, support, and advice throughout the writing of this book, we are very grateful.

We owe a lot to Andre Journel, without whom this book would never have been written. In addition to providing the support necessary for this project, he has been an insightful technical reviewer and an energetic cheerleader.

Автор(ы):Bertoli O., Jackson S., Vann J.
Издание:12 стр.
Язык(и)Английский
An overview of geostatistical simulation  for quantifying risk / Обзор геостатистического моделирования для количественной оценки риска

This paper presents an overview of geostatistical simulation with particular focus on aspects of importance to its application for quantification of risk in the mining industry. Geostatistical simulation is a spatial extension of the concept of Monte Carlo simulation. In addition to reproducing the data histogram, geostatistical simulations also honour the spatial variability of data, usually characterised by a variogram model. If the simulations also honour the data themselves, they are said to be ‘conditional simulations’.

Автор(ы):Mallet J.-L.
Издание:Oxford, 2002 г., 615 стр., ISBN: 0-19-514460-0
Язык(и)Английский
Applied geostatistics series. Geomodeling / Серия прикладной геостатистики. Геомоделирование

This introductory chapter presents a discrete approach specially designed for modeling the geometry and the properties of natural objects such as those encountered in biology and geology.

Автор(ы):Herzfeld U.C.
Издание:Springer, 2004 г., 343 стр., ISBN: 978-3-642-62418-6
Язык(и)Английский
Atlas of Antarctica. Topographic maps from geostatistical analysis of satellite radar altimeter data / Атлас Антарктики. Топографическая карта по результатам геостатистического анализа спутниковой радарной высотной съемки

Although it is generally understood that the Antarctic Ice Sheet plays a critical role in the changing global system, there is to date still a lack of generally available information on the subject. Climatic change and the role of the polar areas are often discussed in the media.

Автор(ы):Oliver M.A., Webster R.
Издание:Springer, 2015 г., 105 стр., ISBN: 978-3-319-15864-8
Язык(и)Английский
Basic steps in geostatistics: The variogram and kriging / Основные этапы геостатистики: вариограмма и кригинг

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. <...>

Издание:Mining & Resource Technology, 1998 г., 139 стр.
Язык(и)Английский
Beyond ordinary kriging: Non-linear geostatistical methods in practice / За пределами ординарного кригинга: Нелинейные геостатистические методы на практике

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.

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