The variogram model is one of the most relevant parameters in geostatistical estimation and simulation methods. The sample variogram is inferred from available data, which may be subject to spatial bias and proportional effect. Over this sample variogram, a licit variogram model is fit and is carried through the process of estimation and/or simulation of the random function usually without rega...

Le bel," T. and Bastin, G., 1985. Variogram identification by the mean-squared interpolation error method with application to hydrologic fields. J. Hydro!., 77: 31-56 . . A systematic presentation of the ·~rrre·a~ ;:~~d)interpolation error'' (MSIE) method for variogram identification is given,E;Jihe-presentation involves a theoretical analysis of the MSIE method under the (realistic) assumption...

The variogram is a critical input to geostatistical studies. It is the most widely used tool to investigate and model spatial variability of lithofacies, porosity, and other petrophysical properties. In addition, 90% of geostatistical reservoir characterization studies use variogram-based geostatistical modeling methods. Furthermore, the variogram reflects our understanding of the geometry and ...

In this paper we propose an extended version of a model-based strategy for clustering spatial functional data. The strategy, we refer, aims simultaneously to classify spatially dependent curves and to obtain a spatial functional model prototype for each cluster. The fit of these models implies to estimate a variogram function, the trace variogram function. Our proposal is to introduce an altern...

Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user’s subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discu...

It is shown how, given a fuzzy formulation of the variogram of a soil property, a fuzzy set of grid spacings that will achieve a target kriging variance can be derived. This fuzzy set of grid spacings can then be defuzzified in a more or less conservative way to define a sampling scheme. The method is illustrated in three examples. The first uses a hypothetical fuzzy variogram. The second is a ...

Spatial disagregation is needed when environmental models of climate, air quality, hydrology, etc. require input data at a finer scale than available or when models produce outputs at a coarser scale than required by users. Area-to-Point Kriging is a common geostatistical framework to address the problem of spatial disaggregation from block to point support. Spatial disaggregation using Area-to...

The soil roughness at the field level is an easy visually perceptible notion, but difficult to describe numerically. The objective of this paper is to propose a method to establish quantitative and descriptive soil roughness indices. A measurement system was developed using a laptop-computer and a laser cell. The elevation data are measured on the ground along a square array. They are treated w...

The spatial continuity of the variables we model in geostatistics is dependent on the modelling and reproduction of the variogram. The variogram defines the relationship between variability (or geologic distance) and the lag distance (or Euclidian distance). As the magnitude of the lag separation vector increases, we typically expect the variogram to also increase. This is generally observed. T...