The ideas in this book have been developed over the past three or four years while I was working at the Institute of Geological Sciences and later for Golder Associates. During that time all of the geological modelling and resource estimation studies I participated in had data that were non-ideal in one respect or another (or just plain 'dirty'): the standard ways of handling the data with kriging or with simpler parametric methods gave reason able results, but always there were nagging doubts and some lack of confidence because of the corners that had to be cut in generat ing a model. The bimodal distribution that was assumed to be 'close enough' to normal; the pattern of rich and poor zones that was not quite a trend yet made the data very non-stationary; and the many plotted variograms that would not fit any standard model variogram: these all contributed to the feeling that there should be something that statistics could say about the cases where hardly any assumptions could be made about the properties ofthe parent population.
Geostatistics is a branch of mathematics that deals with the sampling, analysis, interpretation and display of phenomena that fluctuate in space. Although not all geostatistical methods are probabilistic in nature, the most important accomplishments in the field have been in estimation and forecasting, extending probabilistic methods of stochastic processes and time series analysis to the spatial domain. This book gives the only available comprehensive collection of definitions of geostatistical terms. It lists more than 600 entries selected from the book and journal literature through the end of 1989. Where appropriate, multiple meanings have been included for specific terms. Cross-references abound to help the reader when the definitions of a word leads to the search for other terms. Although all the definitions are in English, equivalent terms are provided in alphabetized lists in Chinese, French, German, Greek, Portuguese, Russian and Spanish. This volume will provide a useful reference for students, scientists, engineers, and others concerned with the meaning of terms found in the geostatistical literature and heard in professional practice. Definitions and entries have been prepared with multiple levels of rigor and complexity, in order to provide help to a readership that may range from inexperienced practitioners to advanced researchers.
ACKNOWLEDGEMENTS xvii LIST OF PARTICIPANTS xix PLENARY SESSIQNS KRIGE D.G., GUARASCIO M. and CAMISANI-CALZOLARI F.A. Early South African qeostatistical techniques in today's perspective ... 1 MATHERON G. The internal consistency of models in qeostatistics ... 21 MONESTIEZ P., HABIB R. and AUDERGON J.M. Estimation de la covariance et du varioqramme pour une fonction aleatoire a support arborescent : application a l'etude des arbres fruitiers ... 39 CHILES J.P. Modelisation qeostatistique de reseaux de fractures ... 57 BRUNO R. and RASPA G. Geostatistical characterization of fractal models of surfaces 17 RIVOIRARD J. Models with orthoqonal indicator residuals ... 91 OMRE H., HALVORSEN K.B. and BERTEIG V.A Bayesian approach to kriqinq ... 109 THEQRY I SWITZER P. Non-stationary spatial covariances estimated from monitorinq data ... 127 CHAUVET P. Quelques aspects de l'analyse structurale des FAI-k a 1 dimension ... 139 vi TABLE OF CONTENTS DOWD P.A. Generalised cross-covariances ... 151 CRESSIE N. The many faces of spatial prediction ..-- ... - ... --.-.-..-. 163 OBLED C. & BRAUD I. Analogies entre geostatistique et analyse en composantes principales de processus ou analyse EOFs ... 177 THEORY II JEULIN D. Sequential random functions models ... 189 CHAUTRU J.M. The use of Boolean random functions in geostatistics -.--.-- ... 201 SOARES A.O. Use of a mathematical morphology tool in characterizing covariance & of indicator data ... 213 ALLISON H.J. Regularization in geostatistics and in ill-posed inversed problems ... - . . - . - . . - ... - - ... 225 DONG A.
Geostatistical Error Management Geostatistical modeling conceptsand techniques have become daily practice in mining operations.That's because these precise analytical tools help professionalsquantify uncertainty and make objective decisions in the face ofthorny "real world" challenges. Geostatistical Error Management isthe first book to apply these proven quantitative tools toenvironmental challenges. The centerpiece of this working guide isan innovative decision-making framework, known as geostatisticalerror management (GEM). GEM integrates the related areas of DataQuality Objectives, Sampling Theory & Practice, andGeostatistical Appraisal to create an entirely new set of toolsthat help you more accurately assess resources for collectingenvironmental data, analyze sources of error in sampling, andquantify the extent and levels of contamination at environmentallyimpacted sites needing remediation. This practical,results-oriented resource * Focuses on the environmental applications of geostatisticaltechniques and how they fit into today's regulatory, legal, andengineering environments * Provides step-by-step explanations for applying error managementtools at every stage of an environmental site assessment * Points the way to applying GEM to environmental work beyond siteevaluation and characterization Geostatistical Error Management will enable environmentalspecialists to perform assessments of hazardous waste andenvironmentally impacted sites more accurately and to confidentlymanage uncertainty and error at every phase of a remediationproject.
An introduction to geostatistics stressing the multivariate aspects for scientists, engineers and statisticians. The book presents a brief review of statistical concepts, a detailed introduction to linear geostatistics, and an account of three basic methods of multivariate analysis. Applications from very different areas of science, as well as exercises with solutions, are provided to help convey the general ideas. In this second edition, the chapters regarding normal kriging and cokriging have been restructured and the section on non-stationary geostatistics has been entirely rewritten.
When interpreting environmental data, scientists and engineers first must select the correct statistical tool to use for their analysis. By doing this they will be able to make sound decisions in their efforts to solve environmental problems. They need a detailed reference that points out the subtle differences between statistical procedures, making clear what procedure to use when trying to find the answer to a specific problem. Statistical Tools for Environmental Quality Measurement provides a detailed review of statistical tools used in analyzing and addressing environmental issues. This book examines commonly-used techniques found in USEPA guidelines and discusses their potential impact on decision-making. The authors are not constrained by statistical formalism; they advise when to go outside of standard statistical models when making difficult decisions. The content is presented in a practical style that prioritizes methods that work, based upon the authors' extensive experience. The text points out that simplicity facilitates effective communication of an analysis and decision to a "consumer" of statistics. The book emphasizes the exact question that each procedure addresses, so that environmental scientists and engineers can clearly identify precisely the question they want to ask, and correctly interpret the results. Features
The Wiley Classics Library consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Spatial statistics — analyzing spatial data through statistical models — has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work is very difficult. Designed exclusively for scientists eager to tap into the enormous potential of this analytical tool and upgrade their range of technical skills, Statistics for Spatial Data is a comprehensive, single-source guide to both the theory and applied aspects of spatial statistical methods. The hard-cover edition was hailed by Mathematical Reviews as an "excellent book which will become a basic reference." This paper-back edition of the 1993 edition, is designed to meet the many technological challenges facing the scientist and engineer. Concentrating on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, revealing how design, inference, and diagnostics are an outgrowth of that link. It then explores new methods to reveal just how spatial statistical models can be used to solve important problems in a host of areas in science and engineering. Discussion includes: Exploratory spatial data analysis Spectral theory for stationary processes Spatial scale Simulation methods for spatial processes Spatial bootstrapping Statistical image analysis and remote sensing Computational aspects of model fitting Application of models to disease mapping Designed to accommodate the practical needs of the professional, it features a unified and common notation for its subject as well as many detailed examples woven into the text, numerous illustrations (including graphs that illuminate the theory discussed) and over 1,000 references. Fully balancing theory with applications, Statistics for Spatial Data, Revised Edition is an exceptionally clear guide on making optimal use of one of the ascendant analytical tools of the decade, one that has begun to capture the imagination of professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology.
The book summarizes research work on the Wajid Sandstone, which provides integrated field and laboratory data to enable a detailed description of this unit including a facies analysis, porosity data, as well as permeability data to establish aquifer models. Detailed facies analysis at outcrop scale are supported by vertical and lateral sedimentological sections, facies and environmental analysis and supplemented by detailed laboratory petrographical and petrophysical data. The analysis and interpretation of the outcrop analog models include the reconstruction of the stratigraphic architecture at outcrop scale. Moreover, the results were described statistically, analyzed and eventually establish an outcrop-based aquifer model analogue. The book benefits undergraduate, graduate and researchers working on applied sedimentological studies, hydrogeology, statistical and geostatistical analysis and modeling.
High levels of uncertainty are a trademark of geological investigations, such as the search for oil, diamonds, and uranium. So business ventures related to geology, such as mineral exploration and mining, are naturally associated with higher risks than more traditional entrepreneurial ventures in industry and economy. There are also a number of dangerous natural hazards, e.g. earthquakes, volcanic activities, and inundations, that are the direct result of geological processes. It is of paramount interest to study them all, to describe them, to understand their origin and - if possible - to predict them. While uncertainties, geological risks and natural hazards are often mentioned in geological textbooks, conferences papers, and articles, no comprehensive and systematic evaluation has so far been attempted. This book, written at an appropriately sophisticated level to deal with complexity of these problems, presents a detailed evaluation of the entire problem, discussing it from both, the geological and the mathematical aspects.
