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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可觸知

創(chuàng)造性

How large a sample is needed to estimate the regional variogram adequately? few data, but because confidence limits cannot be determined analytically from a single set of data its precision is unknown. Approximate confidence intervals can be found numerically by Monte Carlo simulation. A large field of values is created using a plausible model of the variogram. It is then

Congregate

Depth, Dip and Gradientint . using the depths . and the gradients (?.). known at the experimental points. This procedure is rather demanding insofar as it involves a gradient which implies that the depth formation is twice differentiable. In addition it may not be the most realistic one. The gradient (i.e. a point measure

弄皺

山崩

Conditioning a Gaussian model with inequalitiesssian random functions. In these cases, the conditions at experimental points are expressed in terms of inequalities: 1 ≤ . ≤ ., .. ≤ .. ≤ ... The simulation with composite constraints (equalities and inequalities) is performed in two steps: firstly we ensure that the inequality constraints are vali

exercise

少量

Random Functions and Fracture Statistics Modelst fracture criteria are introduced: the weakest link assumption and a critical damage model. They involve two different kinds of change of support, namely by a convolution and by an inf. operation..Examples of random function models are presented, for which a theoretical calculation of coherent frac

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FOVEA

https://doi.org/10.1007/978-3-658-10478-8d a coding process. Explicit calculations can be derived on substitution random functions. Moreover, non conditional and conditional simulations can be carried out to produce outcomes that respect some prescribed morphology.