The presented paper describes the geostatistical analysis of PSInSAR data. This analysis was preceded by short description of PSInSAR technique. The geostatistical computations showed in this article were performed with the R open-source software containing the gstat package. The analysis contains variograms computing (directional variograms) and ordinary kriging interpolation. The computationa...

The spatial prediction of point values from areal data of the same attribute is addressed within the general geostatistical framework of change of support; the term support refers to the domain informed by each datum or unknown value. It is demonstrated that the proposed geostatistical framework can explicitly and consistently account for the support differences between the available areal data...

In this article objective have been made to reviews different geostatistical methods available to estimate and simulate petrophysical properties (porosity and permeability) of the reservoir. Different geostatistical techniques that allow the combination of hard and soft data are taken into account and one refers the main reason to use the geostatistical simulation rather than estimation. Uncert...

Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques where large portions of the model space are explored . The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inv...

Data from the acoustic surveys of MV SA ‘‘Agulhas’’ and FRV ‘‘Walther Herwig’’, and the 1981 RRS ‘‘John Biscoe’’ South Georgia acoustic survey were analysed by geostatistical methods. Estimates of mean density (g m) of krill and their variances are compared with published results from statistical analyses based on random sampling theory. A further high-resolution geostatistical analysis of the ...

Most spatial analysis computer programs are available on the ‘World Wide Wait’ leading this framework to a new GIS platform. Ultimately, this world expects fast answers because if it works then it is obsolete. To review and to evaluate the present spatial geostatistical analysis software and withdraw major conclusions regarding its implementation status are the major goal of th...

A variety of methods have been used to make evolutionary inferences based on the spatial distribution of biological data, including reconstructing population history and detection of the geographic pattern of natural selection. This article provides an examination of geostatistical analysis, a method used widely in geology but which has not often been applied in biological anthropology. Geostat...

Reservoir model needs to be constrained by various data, including dynamic production data. Reservoir heterogeneities are usually described using geostatistical approaches. Constraining geologicaljgeostatistical mode! realizations by dynamic data is generally performed through history matching, which is a complex inversion process and requires a parameterization of the geostatistical realizatio...

Being able to provide a quick and accurate pollutant maps from readings at isolated measurement stations is becoming more important today in light of the European norms on air quality and the public’s demand to be informed. Commonly used algorithms for cartography are quick but their accuracy remains to be determined. Firstly, the choice of method is arbitrary and based on user's subjective per...

This paper presents an overview of geostatistical simulation with particular focus on aspects of importance to its application for quantification of risk in the mining industry. Geostatistical simulation is a spatial extension of the concept of Monte Carlo simulation. In addition to reproducing the data histogram, geostatistical simulations also honour the spatial variability of data, usually c...