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Geostatistics: Modeling Spatial Uncertainty (Wiley Series in Probability & Mathematical Statistics)

Geostatistics: Modeling Spatial Uncertainty (Wiley Series in Probability & Mathematical Statistics) Cover

 

Synopses & Reviews

Publisher Comments:

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.

Book News Annotation:

Deals with statistical models used to describe natural variables distributed in space or in time and space, integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This 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 situations involving spatial uncertainty. Of interest to geostatisticians, physicists, mining engineers, and earth science professionals. Annotation c. by Book News, Inc., Portland, OR (booknews@booknews.com)

Synopsis:

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.

Synopsis:

Die Geostatistik beschreibt Beziehungen natürlicher Größen wie den Metallgehalt von Erzen, die Dicke von Bäumen in einem Waldbestand oder Eigenschaften von Bodenproben bestimmter Regionen. Die enorme Komplexität dieser Variablen verbietet eine Behandlung mit Hilfe mathematischer Standardfunktionen - unumgänglich ist die Berücksichtigung der sogenannten räumlichen Unbestimmtheit und des Unterschiedes zwischen Beschreibung und Interpretation. Wie verschiedene statistische Methoden diesen Anforderungen gerecht werden, untersucht dieses Buch. (04/99)

About the Author

JEAN-PAUL CHIL?S is a senior research scientist with the Bureau de Recherches G?ologiques et Mini?res (the French Geological Survey) in Orleans, France.

PIERRE DELFINER is a scientific advisor in geosciences with Total Exploration Production in Paris, France.

Table of Contents

Preliminaries.

Structural Analysis.

Kriging.

Intrinsic Model of Order k.

Multivariate Methods.

Nonlinear Methods.

Conditional Simulations.

Scale Effects and Inverse Problems.

Appendix.

References.

Index.

Product Details

ISBN:
9780471083153
Subtitle:
Modeling Spatial Uncertainty
Other:
Chiles, Jean-Paul
Author:
&
Author:
Chilhs, Jean-Paul
Author:
egrave
Author:
Jean-Paul Chil
Author:
s, Jean-Paul
Author:
Chiles, Jean-Paul
Author:
Chil
Author:
232
Author:
Delfiner, Pierre
Author:
S
Author:
Chil?s, Jean-Paul
Author:
Chils, Jean-Paul
Author:
Delfiner, Paul
Author:
Delfiner, Paul
Publisher:
Wiley-Interscience
Location:
New York :
Subject:
Earth Sciences
Subject:
Statistics
Subject:
Statistical methods
Subject:
Probability
Subject:
Earth sciences -- Statistical methods.
Subject:
Earth Sciences - General
Subject:
Probability & Statistics - General
Subject:
biometrics
Copyright:
Edition Description:
WOL online Book (not BRO)
Series:
Wiley Series in Probability and Statistics
Series Volume:
344
Publication Date:
20090925
Binding:
Online electronic file accessible through online networks
Grade Level:
General/trade
Language:
English
Illustrations:
Yes
Pages:
720
Dimensions:
9.58x6.51x1.56 in. 2.44 lbs.

Related Subjects

Science and Mathematics » Geology » General

Geostatistics: Modeling Spatial Uncertainty (Wiley Series in Probability & Mathematical Statistics)
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Product details 720 pages Wiley-Interscience - English 9780471083153 Reviews:
"Synopsis" by , 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.
"Synopsis" by , Die Geostatistik beschreibt Beziehungen natürlicher Größen wie den Metallgehalt von Erzen, die Dicke von Bäumen in einem Waldbestand oder Eigenschaften von Bodenproben bestimmter Regionen. Die enorme Komplexität dieser Variablen verbietet eine Behandlung mit Hilfe mathematischer Standardfunktionen - unumgänglich ist die Berücksichtigung der sogenannten räumlichen Unbestimmtheit und des Unterschiedes zwischen Beschreibung und Interpretation. Wie verschiedene statistische Methoden diesen Anforderungen gerecht werden, untersucht dieses Buch. (04/99)
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