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This text provides an advanced introduction to the theory and applications of geostatistics, including tools for description, modeling spatial continuity, spatial prediction, assessment of local uncertainty, and stochastic simulation.
The papers in this volume provide a comprehensive account of the current methods and work in geostatistics, including recent theoretical developments and applications. Topics featured include: stochastic simulations, space-time modelling, and Bayesian framework.
These proceedings of the IAMG 2014 conference in New Delhi explore the current state of the art and inform readers about the latest geostatistical and space-based technologies for assessment and management in the contexts of natural resource exploration, environmental pollution, hazards and natural disaster research. The proceedings cover 3D visualization, time-series analysis, environmental geochemistry, numerical solutions in hydrology and hydrogeology, geotechnical engineering, multivariate geostatistics, disaster management, fractal modeling, petroleum exploration, geoinformatics, sedimentary basin analysis, spatiotemporal modeling, digital rock geophysics, advanced mining assessment and glacial studies, and range from the laboratory to integrated field studies. Mathematics plays a key part in the crust, mantle, oceans and atmosphere, creating climates that cause natural disasters, and influencing fundamental aspects of life-supporting systems and many other geological processes affecting Planet Earth. As such, it is essential to understand the synergy between the classical geosciences and mathematics, which can provide the methodological tools needed to tackle complex problems in modern geosciences. The development of science and technology, transforming from a descriptive stage to a more quantitative stage, involves qualitative interpretations such as conceptual models that are complemented by quantification, e.g. numerical models, fast dynamic geologic models, deterministic and stochastic models. Due to the increasing complexity of the problems faced by today’s geoscientists, joint efforts to establish new conceptual and numerical models and develop new paradigms are called for.
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.
A novel, practical approach to modeling spatial uncertainty. This book deals with statistical models used to describe natural variables distributed in space or in time and space. It takes a practical, unified approach to geostatistics-integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This unique approach facilitates realistic modeling that accounts for the complexity of natural phenomena and helps solve economic and development problems-in mining, oil exploration, environmental engineering, and other real-world situations involving spatial uncertainty. Up-to-date, comprehensive, and well-written, Geostatistics: Modeling Spatial Uncertainty explains both theory and applications, covers many useful topics, and offers a wealth of new insights for nonstatisticians and seasoned professionals alike. This volume: * Reviews the most up-to-date geostatistical methods and the types of problems they address. * Emphasizes the statistical methodologies employed in spatial estimation. * Presents simulation techniques and digital models of uncertainty. * Features more than 150 figures and many concrete examples throughout the text. * Includes extensive footnoting as well as a thorough bibliography. Geostatistics: Modeling Spatial Uncertainty is the only geostatistical book to address a broad audience in both industry and academia. An invaluable resource for geostatisticians, physicists, mining engineers, and earth science professionals such as petroleum geologists, geophysicists, and hydrogeologists, it is also an excellent supplementary text for graduate-level courses in related subjects.
This unique book presents a learn-by-doing introduction to geostatistics. Geostatistics provides the essential numerical tools for addressing research problems that are encountered in fields of study such as geology, engineering, and the earth sciences. Illustrating key methods through both theoretical and practical exercises, Solved Problems in Geostatistics is a valuable and well-organized collection of worked-out problems that allow the reader to master the statistical techniques for modeling data in the geological sciences. The book's scope of coverage begins with the elements from statistics and probability that form the foundation of most geostatistical methodologies, such as declustering, debiasing methods, and Monte Carlo simulation. Next, the authors delve into three fundamental areas in conventional geostatistics: covariance and variogram functions; kriging; and Gaussian simulation. Finally, special topics are introduced through problems involving utility theory, loss functions, and multiple-point geostatistics. Each topic is treated in the same clearly organized format. First, an objective presents the main concepts that will be established in the section. Next, the background and assumptions are outlined, supplying the comprehensive foundation that is necessary to begin work on the problem. A solution plan demonstrates the steps and considerations that have to be taken when working with the exercise, and the solution allows the reader to check their work. Finally, a remarks section highlights the overarching principles and noteworthy aspects of the problem. Additional exercises are available via a related Web site, which also includes data related to the book problems and software programs that facilitate their resolution. Enforcing a truly hands-on approach to the topic, Solved Problems in Geostatistics is an indispensable supplement for courses on geostatistics and spatial statistics a the upper-undergraduate and graduate levels.It also serves as an applied reference for practicing professionals in the geosciences.
Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes – such as the distribution of pollution – vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner’s repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.