Ellipsoid or geometric anisotropy is a widely used method in geostatistical analysis to obtain variograms with different ranges directions (azimuth) and relatively similar sill variance. indispensable mining when resource geologist intends understand the spatial continuity of variables related any geological controls mineralization. For example, dealing mineralization tabular deposits, porphyri...

Accurate prediction of petroleum reservoir performance requires reliable models of the often complex reservoir heterogeneity. Geostatistical simulation techniques generate multiple realizations of the reservoir model, all equally likely to be drawn. Traditional to geostatistics, geological continuity is represented through the variogram. The variogram is limited in describing complex geological...

for optimum planning, utilization, and management of lands, recognition of spatial variability of soil properties is necessary. this research was conducted to evaluate spatial variability of soil properties in 5000 ha of arsenjan plain in fars province. for this purpose, soil salinity, ph, particle size percentage, sodium, potassium, and organic carbon (oc) were measured on a systematic squared...

In this paper we consider the estimation of the variogram using the method of weighted least squares and in the process we propose two alternative set of weights. We study the asymptotic sampling properties of these estimators and compare their efficiencies both analytically and through simulations. To illustrate the above methods, we consider daily rainfall across Switzerland measured at 467 l...

Up to now, relatively little effort has been dedicated the quantitative assessment of differences in spatial patterns model outputs. In this paper, we employed a variogram-based methodology quantify root-zone soil moisture, net radiation, and latent sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES CHTESSEL) over European geographic domains – namely, UK, France Spa...

Analysis of variogram is a necessary step in 3D stochastic modeling, and different settings variograms can directly affect the final distribution model attribute. How to optimize setting parameters by unit facies type has become key geological modeling. This article focuses on reservoir within sedimentary background fluvial-deltas layer SII7+8 which develops multiple microfacies, such as fluvia...

This paper applies statistical and geostatistical procedures to a soil chronosequence on the Ž . terraces of the Henares River NE Madrid to analyse the spatial distribution of several soil properties and use the contribution of geostatistics to establishing a landscape evolution model of the area. Particle-size distribution, pH, calcium carbonate and organic carbon were analysed. Statistical pr...

A geostatistical perspective on spatial genetic structure may explain methodological issues of quantifying spatial genetic structure and suggest new approaches to addressing them. We use a variogram approach to (i) derive a spatial partitioning of molecular variance, gene diversity, and genotypic diversity for microsatellite data under the infinite allele model (IAM) and the stepwise mutation m...

The classical variogram estimate is convenient but can be unacceptably variable. Improved estimators are possible, especially when the locations of the available data are highly clustered. Using a simple theoretical example, we demonstrate that weighting can dramatically increase the efficiency of classical variogram estimates from clustered data. We give expressions for the weights that lead t...

Sequential Gaussian simulation (sgsim) and sequential indicator simulation (sisim) have emerged as powerful tools for stochastic imaging of Earth Science phenomena and are currently widely accepted fast simulation algorithms. In this paper, we will expand the sequential simulation toolbox by relying on a recent development in geostatistical theory (Journel, 1994) denoted as direct sequential si...