The fourth edition of the European Conference on Geostatistics for Environmental Applications (geoENV IV) took place in Barcelona, November 27-29, 2002. As a proof that there is an increasing interest in environmental issues in the geostatistical community, the conference attracted over 100 participants, mostly Europeans (up to 10 European countries were represented), but also from other countries in the world. Only 46 contributions, selected out of around 100 submitted papers, were invited to be presented orally during the conference. Additionally 30 authors were invited to present their work in poster format during a special session.

This book grew out of class notes for a course that served two purposes:
1.    To familiarize graduate students in hydrogeology and environmental engineering with some practical methods for solving interpolation and related estimation problems. The course emphasized geostatistical methods.
2.    To illustrate how one may use data to develop empirical models, that is, to provide an introduction to applied statistical modeling.

Горная геология является специализированной областью прикладных геологических наук, которая исторически развивалась как поддержка эксплуатации рудников и оценки горных проектов. Основной задачей горной геологии является предоставление подробной геологической информации, а также проведение техникоэкономических исследований для оценки проекта горных работ. Когда начинается добыча, горные геологи обеспечивают геологическое сопровождение работы, обеспечивая экономически эффективную добычу ценных минералов и их точное отделение от пустых пород.

Subsurface fluid flow is critically dependent on the 3D distribution of petrophysical properties in rocks. In sequences of sedimentary rocks these properties are strongly influenced by lithology and facies distribution that stem from the geologic processes that generated them.

Petroleum resource evaluations have been performed by geologists, geophysicists, geochemists, engineers, and statisticians for many decades in an attempt to estimate resource potential in a given region. Because of differences in the geological and statistical methods used for assessment, and the amount and type of data available, resource evaluations often vary. Accounts of various methods have been compiled by Haun (1975), Grenon (1979), Masters (1985), Rice (1986), and Mast et al. (1989). In addition, Lee and Gill (1999) used the Michigan reef play data to evaluate the merits of the log-geometric method of the U.S. Geological Survey (USGS); the PETRIMES method developed by the Geological Survey of Canada (GSC); the Arps and Roberts method; Bickel, Nair, and Wang’s nonparametric fi nite population method; Kaufman’s anchored method; and the geo-anchored method of Chen and Sinding–Larson. <...>

Пространство, структура и случайность. Вклад в честь Джорджа Матерона в области геостатистики, случайных множеств и математической морфологии

Personal Reminiscences of Georges Matheron Dietrich Stoyan
A few words about Georges Matheron (1930-2000) Jean Serra
Introduction From the editors
Part I Geostatistics

Optimal interpolation based on regression against observed z values of surrounding data points, weighted according to spatial covariance values. <...>

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.