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...

In recent years, methods of fuzzy reasoning have been successfully developed for land evaluation. The accuracy of such land evaluation depends on the quality of weighing land characteristics with respect to their effects on crop production. This paper presents a spatially-based model of land suitability analysis. The main purposes were to (1) establish land suitability indices for irrigated whe...

Covariance and variogram functions have been extensively studied in Euclidean space. In this article, we investigate the validity of commonly used covariance and variogram functions on the sphere. In particular, we show that the spherical and exponential models, as well as power variograms with 0 < α ≤ 1, are valid on the sphere. However, two Radon transforms of the exponential model, Cauchy mo...

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...

A number of optimization approaches regarding monitoring network design and sampling optimization procedures have been reported in the literature. Cokriging Estimation Variance (CEV) is a useful optimization tool to determine the influence of the spatial configuration of monitoring networks on parameter estimations. It was used in order to derive a reduced configuration of a nitrate concentrati...

Geostatistical models often require a variogram or covariance model for kriging and krigingbased simulation. Next to the initial decision of stationarity, the choice of an appropriate variogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental variograms with a nested combination of proven models such as the spherical, exponential, ...

introduction the ardabil plain aquifer, with area about 900 km2, has high concentration amounts of nitrate in some parts. nowadays, nitrate pollution in groundwater due to the widespread application of fertilizers and increasing of drinking water demand, has encountered consumers with problem. the adverse health effects of high nitrate levels in drinking water have been well documented. in the ...

It is not simple to model cross and auto-variograms to describe the covariation of two or more soil properties, since the models that are fitted must meet certain constraints. These constraints are most readily met by fitting a linear model of coregionalization (LMCR). This presents practical problems. Not all combinations of authorized variogram functions constitute a LMCR. This paper presents...

We investigate the accuracy and resolution of the variogram analysis method (Maus, 1999) on syn thetic and real data. Syn thetic magnetic ight line data are generated forbasemen t models of idealized geological setups. Comparing variogram depths of the data with true model depths shows that variogram depths are accurate and un biased as long as the data analysis window is larger than 10 times t...