Geostatistical algorithms that consider multiple-point statistics are becoming increasingly popular. These methods allow for the reproduction of complicated features beyond the commonly implemented two-point statistic: the covariance or semivariogram. In practice, it is not possible to infer many multiple-point statistics directly from the available data; therefore, it is common to borrow stati...

a simple but novel and applicable approach is proposed to solve the problem of smoothing effect of ordinary kriging estimate which is widely used in mining and earth sciences. it is based on transformation equation in which z scores are derived from ordinary kriging estimates and then rescaled by the standard deviation of sample data and the sample mean is added to the result. it bears the grea...

Groundwater is one the major sources of natural water being exploited excessively for various uses in India.
 Thus, it very essential to monitor spatial and temporal variability groundwater quality. Geo-Statistical Interpolation using GIS has been considered as best most advanced method interpolation prediction studies pollution quality, adopted universally. In this paper, ordinary Kriging...

Geostatistics deals with spatial and temporal data from natural phenomena and involves estimating values of regionalized variables. In this analysis the fundamental tool is the variogram that is a variability measure; for example, geological variability related to distance. The variogram shows the degree of spatial dependence between samples for a specific support. The squared differences betwe...

Many geospatial science subdisciplines analyze variables that vary over both space and time. The space-time autoregressive (STAR) model is one specification formulated to describe such data. This paper summarizes STAR specifications that parallel geostatistical model specifications commonly used to describe space-time variation, with the goal of establishing synergies between these two modeling...

This paper presents both estimation and simulation as optimization problems that differ in the optimization criteria, minimization of a local expected loss for estimation and reproduction of global statistics (semivariogram, histogram) for simulation. An intermediate approach is proposed whereby an initial random image is gradually modified using simulated annealing so as to better match both l...