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Titlebook: Geostatistics Tróia ‘92; Volume 1 & 2 Amilcar Soares Book 1993 Springer Science+Business Media B.V. 1993 Kriging.Reservoir.classification.d

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樓主: HEIR
41#
發(fā)表于 2025-3-28 18:33:37 | 只看該作者
42#
發(fā)表于 2025-3-28 20:30:59 | 只看該作者
https://doi.org/10.1007/1-4020-3183-1tance-rejection technique but can be used only to validate very few inequalities; in the second, we sample the stationary distribution of a Markov chain. This latter case is illustrated with a few examples.
43#
發(fā)表于 2025-3-29 02:03:49 | 只看該作者
Gary N. Libman,Susan C. MacIntoshsing variety of information, of both structural nature (multiple-event dependence beyond traditional two-point covariances) and local nature (hard and soft data). Last, like recent developments in simulated annealing, these algorithms need not be probabilistic.
44#
發(fā)表于 2025-3-29 05:15:38 | 只看該作者
45#
發(fā)表于 2025-3-29 08:42:19 | 只看該作者
Depth, Dip and Gradiente formula ?(. * .) with the result that it is possible to cokrige the depths using these average dip data..This article sets out to compare these local and regional procedures. The influence of the support size on the mapping results will be addressed.
46#
發(fā)表于 2025-3-29 12:31:48 | 只看該作者
47#
發(fā)表于 2025-3-29 17:54:04 | 只看該作者
Conditioning a Gaussian model with inequalitiestance-rejection technique but can be used only to validate very few inequalities; in the second, we sample the stationary distribution of a Markov chain. This latter case is illustrated with a few examples.
48#
發(fā)表于 2025-3-29 22:09:25 | 只看該作者
49#
發(fā)表于 2025-3-30 03:44:10 | 只看該作者
Book 1993e a comprehensive account of the currentstate of the art of geostatistics, including recent theoreticaldevelopments and new applications. In particular, readers will finddescriptions and applications of the more recent methods of stochasticsimulation together with data integration techniques applied
50#
發(fā)表于 2025-3-30 05:54:17 | 只看該作者
https://doi.org/10.1007/978-3-322-93565-6tor covariances. Higher order sample statistics such as three, four, multi-point covariances, as obtained, for example, from a training image, would improve considerably stochastic images if they could be reproduced.
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