epub Geostatistics: Modeling Spatial Uncertainty download
by Jean-Paul Chilès,Pierre Delfiner
Only 9 left in stock (more on the way). Computers &Geosciences, 1 February 2013). Jean-Paul Chilès is Deputy Director of the Center ofGeosciences and Geoengi?neering at MINES ParisTech, France. Pierre Delfiner is Principal of PetroDecisions, a consultingfirm based in Paris, France.
Geostatistics: Modeling Spatial Uncertainty by Jean-Paul Chiles; Pierre Delfiner. Geostatistics: Modeling Spatial Uncertainty by . P. January 2000 · Journal of the American Statistical Association. Chilès and P. Delfiner publishedin 1999 has been one of the most cited reference book in Geostatistics and spatialstatistics in the last decade
Jean-Paul Chilès is Deputy Director of the Center of Geosciences and Geoengi?neering at MINES ParisTech, France.
Jean-Paul Chilès is Deputy Director of the Center of Geosciences and Geoengi?neering at MINES ParisTech, France. Pierre Delfiner is Principal of PetroDecisions, a consulting firm based in Paris, France.
Geostatistics: Modeling Spatial Uncertainty, Jean-Paul Chilès, Pierre Delfiner.
Geostatistics: Modeling Spatial Uncertainty, Jean-Paul Chilès, Pierre Delfiner April 2013 · Computers & Geosciences. Any geostatistical prediction is built on a prior.
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. 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
Geostatistics: Modeling Spatial Uncertainty by . Delner published in 1999 has been one of the most cited reference book in Geostatistics and spatial statistics in the last decade.
Geostatistics: Modeling Spatial Uncertainty by . There are very good reasons for this: it is, in my opinion, the most comprehensive book on geostatistics, with an in-depth coverage of all as-pects, from random eld theory and spectral representation to virtually all practical issues arising when modeling spatial data, including variogram tting, (co) kriging, estimating nonlinear quantities in presence of a change of support, or when perform-ing
JEAN-PAUL CHILÈS BRGM and MINES ParisTech. PIERRE DELFINER PetroDecisions. Epistemology The quantiﬁcation of spatial uncertainty requires a model specifying the mechanism by which spatial randomness is generated.
JEAN-PAUL CHILÈS BRGM and MINES ParisTech. 28 January 2012; 13:1:56. The simplest approach is to treat the regionalized variable as deterministic and the positions of the samples as random, assuming for example that they are selected uniformly and independently over a reference area, in which case standard statistical rules for independent random variables apply, such as that for the variance of the mean.
Geostatistics: Modeling Spatial Uncertainty Hardcover – 19 March 2012
Geostatistics: Modeling Spatial Uncertainty Hardcover – 19 March 2012. by Jean-Paul Chiles (Author), Pierre Delfiner (Author). Computers & Geosciences, 1 February 2013). Jean-Paul Chiles is Deputy Director of the Center of Geosciences and Geoengi?neering at MINES ParisTech, France.
Possibly due to the characteristic modesty of the author, two papers written by C. Lantuéjoul on this topic are not cited in the book. Chilès, P. Delfiner: Geostatistics: Modeling Spatial Uncertainty
Possibly due to the characteristic modesty of the author, two papers written by C. References are given below. Bacro JN, Bel L, Lantuéjoul C (2010) Testing the independence of maxima: from bivariate vectors to spatial extreme fields. Bel L, Bacro JN, Lantuéjoul C (2008) Assessing extremal dependence of environmental spatial fields. Environmetrics 19:163–182. Delfiner: Geostatistics: Modeling Spatial Uncertainty. Math Geosci 45, 377–380 (2013) doi:10.
Praise for the First Edition
". . . a readable, comprehensive volume that . . . belongs onthe desk, close at hand, of any serious researcher orpractitioner." Mathematical Geosciences
The state of the art in geostatistics
Geostatistical models and techniques such as kriging andstochastic multi-realizations exploit spatial correlations toevaluate natural resources, help optimize their development, andaddress environmental issues related to air and water quality, soilpollution, and forestry. Geostatistics: Modeling SpatialUncertainty, Second Edition presents a comprehensive, up-to-datereference on the topic, now featuring the latest developments inthe field.
The authors explain both the theory and applications ofgeostatistics through a unified treatment that emphasizesmethodology. Key topics that are the foundation of geostatisticsare explored in-depth, including stationary and nonstationarymodels; linear and nonlinear methods; change of support;multivariate approaches; and conditional simulations. The SecondEdition highlights the growing number of applications ofgeostatistical methods and discusses three key areas of growth inthe field:
New results and methods, including kriging very large datasets;kriging with outliers; nonse??parable space-time covariances;multipoint simulations; pluri-gaussian simulations; gradualdeformation; and extreme value geostatistics
Newly formed connections between geostatistics and otherapproaches such as radial basis functions, Gaussian Markov randomfields, and data assimilation
New perspectives on topics such as collocated cokriging, krigingwith an external drift, discrete Gaussian change-of-support models,and simulation algorithms
Geostatistics, Second Edition is an excellent book for courseson the topic at the graduate level. It also serves as an invaluablereference for earth scientists, mining and petroleum engineers,geophysicists, and environmental statisticians who collect andanalyze data in their everyday work.