Titre :	Geostatistics for natural resources evaluation
Type de document :	texte imprimé
Auteurs :	Goovaerts, Pierre, Auteur
Editeur :	Oxford university press
Année de publication :	1997
Importance :	XIV-483 p.
Format :	24 cm
ISBN/ISSN/EAN :	978-0-19-511538-3
Langues :	Anglais (eng)
Résumé :	The book is built around the study goals, giving to data analysis and data integration the lead they deserve. Never mistake the tools for the goals, this is the recipe for a good geo-statistical study, and Pierre has demonstrated that it also works for a good book. His illustration of every concept, every kriging system, by an example from the same data set (heavy metal contamination in the Swiss Jura) is an enchantment and a necessary breather after some tough reading. Characteristically, exploratory data analysis (chapter 2) precedes the introduction of the random function (chapter 3). The chapter on inference and modeling of a multivariate model (chapter 4) is, in my opinion, the best ever written on the subject. Pierre could have been bolder by giving precedence to uncertainty assessment over estimation, but he chose to present first the estimation tools (chapters 5 and 6). This is the most complete yet cohesive expose of all the various flavors of kriging and co-kriging. The emphasis is not on the illusory kriging variance but rather on building estimators that can account for the large diversity of information type's characteristic of earth sciences. The very reason for geostatistics and the future of the discipline lie in the modeling of uncertainty, at each node through conditional distributions (chapter 7) and globally through stochastic images (conditional simulations, chapter 8). In modern geo-statistics, which is driven by conditional simulations, kriging is an engine, and not the only one, to build models of conditional probability distributions. Kriging estimates and kriging variances have lost their original luster: the former because of their uneven smoothing and the latter because of their data independence. The practice of geo-statistics has always been ahead of academic publications. This book finally may have caught up with the use of random function models in the earth sciences, but geo-statistics has already freed itself from the frame of such models. It befits that the door of an era be closed by a man of the future.

Geostatistics for natural resources evaluation [texte imprimé] / Goovaerts, Pierre, Auteur . - Oxford university press, 1997 . - XIV-483 p. ; 24 cm.
ISBN : 978-0-19-511538-3
Langues : Anglais (eng)

Résumé :

The book is built around the study goals, giving to data analysis and data integration the lead they deserve. Never mistake the tools for the goals, this is the recipe for a good geo-statistical study, and Pierre has demonstrated that it also works for a good book. His illustration of every concept, every kriging system, by an example from the same data set (heavy metal contamination in the Swiss Jura) is an enchantment and a necessary breather after some tough reading. Characteristically, exploratory data analysis (chapter 2) precedes the introduction of the random function (chapter 3). The chapter on inference and modeling of a multivariate model (chapter 4) is, in my opinion, the best ever written on the subject. Pierre could have been bolder by giving precedence to uncertainty assessment over estimation, but he chose to present first the estimation tools (chapters 5 and 6). This is the most complete yet cohesive expose of all the various flavors of kriging and co-kriging. The emphasis is not on the illusory kriging variance but rather on building estimators that can account for the large diversity of information type's characteristic of earth sciences. The very reason for geostatistics and the future of the discipline lie in the modeling of uncertainty, at each node through conditional distributions (chapter 7) and globally through stochastic images (conditional simulations, chapter 8). In modern geo-statistics, which is driven by conditional simulations, kriging is an engine, and not the only one, to build models of conditional probability distributions. Kriging estimates and kriging variances have lost their original luster: the former because of their uneven smoothing and the latter because of their data independence. The practice of geo-statistics has always been ahead of academic publications. This book finally may have caught up with the use of random function models in the earth sciences, but geo-statistics has already freed itself from the frame of such models. It befits that the door of an era be closed by a man of the future.