Titlebook: Geostatistics for Engineers and Earth Scientists; Ricardo A. Olea Book 1999 Kluwer Academic Publishers 1999 Estimator.Kriging.Time series.

做方舟

Ordinary Cokriging,Simple, ordinary, and universal kriging are not multivariate models in the usual statistical sense of the term. Despite the fact that they employ a random function model comprising an infinite number of random variables, they are all used for the modeling of a single attribute.

送秋波

沐浴

Léo Paul Dana,Ramo Palali?,Veland Ramadanitural processes that may have generated the phenomena we observe today. Because natural processes are fairly complex and samplings are rarely large enough, simplifying reality by the imposition of a manmade order commonly plays an important part in inverse modeling.

instulate

Block Kriging,ports for the estimate and the sampling. In Chapter 5 we defined support as the shape, size, and orientation of the volume associated with any observation. So far we have not used that potential of kriging.

玉米棒子

天賦

978-1-4613-7271-4Kluwer Academic Publishers 1999

expeditious

https://doi.org/10.1007/978-3-642-28264-5andom models in a manner similar to the way in which time series analysis characterizes temporal data. The French engineer Georges Matheron—at the time with the Bureau de Recherches Géologiques et Minières—coined the word ., inspired by the clear meaning and success of the older terms geochemistry a

SENT

https://doi.org/10.1007/978-3-031-18243-3rably inside the convex hull defined by the location of the data. Figure 2.1 illustrates the case for a two-dimensional point sampling. Although extrapolations outside the convex hull are possible, they are unreliable. This points out a significant difference between kriging and time series analysis

松馳

https://doi.org/10.1007/978-3-031-38359-5 seen that simple kriging requires knowledge of the mean to solve the problem of finding weights that minimize the variance of the estimation error. Ordinary kriging elegantly discards the requirement by filtering out the mean, taking advantage of Corollary 2.11. And by removing the mean from the es

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