比喻好
发表于 2025-3-26 22:19:37
http://reply.papertrans.cn/39/3841/384094/384094_31.png
minimal
发表于 2025-3-27 02:54:35
Reminders on the Conditioning Kriging,ould be already well-known..Simulation methods enable us to construct realizations of RF having a fixed covariance K. In practice, the values z (x.) at the data points x. are known and we dictate that at these points the RF Z and its simulations must coincide. The conditioning method by kriging make
HACK
发表于 2025-3-27 09:17:50
Non Conditional Simulation of Stationary Isotropic Multigaussian Random Functions,asis of the Central Limit Theorem, a possible procedure consists of simulating a large number of independent stationary random functions (not necessarily multigaussian) with covariance .. This procedure raises two questions:
蜿蜒而流
发表于 2025-3-27 10:10:09
Modelling the Karstic Medium: A Geostatistical Approach,ration of a karst model is necessary for the understanding of karstic groundwater flow. Based on the density of conduits, a geostatistical approach is proposed for the modelling of karstic medium. This approach satisfies the constraints required by hydrogeological applications, but some difficulties
正常
发表于 2025-3-27 15:38:21
http://reply.papertrans.cn/39/3841/384094/384094_35.png
Peristalsis
发表于 2025-3-27 18:10:50
The Pros and Cons of the Truncated Gaussian Method, limitations of this method, from a geostatistical point of view and also from a practical one. First we present the method in detail including the hypotheses behind it. In particular we show that this method ensures consistency for the model in terms of the indicators variograms and cross variogram
incisive
发表于 2025-3-27 22:56:01
http://reply.papertrans.cn/39/3841/384094/384094_37.png
insightful
发表于 2025-3-28 02:32:57
978-90-481-4372-6Springer Science+Business Media Dordrecht 1994
珍奇
发表于 2025-3-28 08:00:28
Geostatistical Simulations978-94-015-8267-4Series ISSN 0924-1973 Series E-ISSN 2215-1834
overture
发表于 2025-3-28 11:43:34
Kristin M. Lindahl Ph.D.,Sara Wigdersonof a spatially-varying parameter. Geostatistical simulation algorithms generate realizations of a random field with specified statistical and geostatistical properties. A nonlinear function (called a transfer function) is evaluated over each realization to obtain an uncertainty distribution of a sys