In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...

Mapping data using geostatistics can be a time-consuming process because of the many parameters to define. automap, a geostatistical package written in R, was developed to define automatically a spatial correlation model, a step that is considered to be the biggest obstacle for automating the spatial interpolation process with geostatistical algorithms. The implementation of automap into a Serv...

Predicting the spatial distribution of braided fluvial facies reservoirs is paramount significance for oil and gas exploration development. Given that seismic materials enjoy an advantage in dense sampling, many methods have been proposed to predict reservoir based on different attributes. Nevertheless, attributes sensitivities reservoirs, informational redundancy between them makes it difficul...

groundwater quality management is one of the most important issues in many arid and semi-arid regions, including iran.nitrate (no3-) is one of the most common anions contaminating groundwater. this study aimed to range nitrateconcentrations in water resources in bahabad plain in yazd province. to evaluate the nitrate data in this descriptive study,260 nitrate samples from 13 wells in bahabad we...

Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Stre...

The trend in interactive entertainment is towards scenes with massive numbers of characters, and requiring huge amounts of motion data, which must be compactly and efficiently stored without sacrificing quality or controllability of the motions. Multilinear algebra is a powerful tool for efficiently representing multivariate data, including human motion data, through the analysis of multimodal ...

Overutilization of groundwater resources can put their quality under stress. It could be base on rapid declination in groundwater level leading to salt water intrusion in the coastal aquifers. The objective of this study was to map the vulnerability of salt water intrusion of the Urmia Lake, Iran to nearby coastal aquifer (Azarshahr) using GALDIT method. The GALDIT index based on six parameters...

Two robust approaches to principal component analysis and factor analysis are presented. The different methods are compared, and properties are discussed. As an application we use a large geochemical data set which was analyzed in detail by univariate (geo-)statistical methods. We explain the advantages of applying robust multivariate methods.