Titlebook: Geostatistics Oslo 2012; Petter Abrahamsen,Ragnar Hauge,Odd Kolbj?rnsen Book 2012 Springer Science+Business Media Dordrecht 2012 Geostatis

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書目名稱Geostatistics Oslo 2012影響因子(影響力)




書目名稱Geostatistics Oslo 2012影響因子(影響力)學(xué)科排名




書目名稱Geostatistics Oslo 2012網(wǎng)絡(luò)公開度




書目名稱Geostatistics Oslo 2012網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Geostatistics Oslo 2012讀者反饋




書目名稱Geostatistics Oslo 2012讀者反饋學(xué)科排名

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Misnomer

Event-Based Geostatistical Modeling: Description and Applicationsa continuum of rule complexity from very simple geometric models to complicated dynamics. Even the application of simple rules, including few conceptual interactions based on an understanding of stratigraphic relationships and parametric geometries for event scale depositional and erosion features,

閑逛

Fortify

五行打油詩

Revisiting the Linear Model of Coregionalizationriables as linear combinations of sets of independent (or at least uncorrelated) underlying variables. The popularity of the LCM stems mostly from the ease of modeling and verification of the admissibility of the model. However, LCM has strong limitations as it implies symmetrical cross-covariances

五行打油詩

Modeling Nonlinear Beta Probability Fields are not classic p-fields. Instead, the shape of the local probability density function (pdf) for the proportion .(.) random variable at each location . changes with the Beta distribution parameters, . and . which are in turn functions of the local proportions themselves. In addition, Beta random va

ACRID

MAPLE

Multiple-Point Geostatistical Simulation Based on Genetic Algorithms Implemented in a Shared-Memory a training image. The most widely used algorithm is based on solving a single normal equation at each location using the conditional probabilities inferred during the training process. Simulated annealing offers an alternative implementation that, in addition, allows to incorporate additional stati

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The Edge Effect in Geostatistical Simulationsave significant effects on simulations, especially for situations in which the variogram range is relatively large compared to the size of the simulation area (volume). Failure to account properly for the issue can bias simulations by generating values with ranges of correlation and statistical char