Semivariogram, whose equation is shown in ( 2), is the geostatistical tool for studying the relationship between collected data in function of distance and direction, Geostatistical analytic approach is mainly based on two different operative steps: (i) semivariogram calculus and modelling (ii) kriging interpolation technique. Presently, geostatistics supplies a collection of powerful techniques that address the study of spatial correlation between experimental values of a specific variable in order to estimate values in unknown points inside the phenomenon existing domain. For this reason, geostatistics has become an extremely powerful tool for studying and evaluating space- and/or time-related phenomena, and in the present days, its own techniques are implemented in all of the most popular data analysis softwares. Since then a lot of progress has been made in the development of geostatistics techniques of data analysis and interpolation. Regionalized variable schematic representation. Where is the regionalized component and represents the random component that explains the local effects. This new approach was based on the “regionalized variables” theory : a new type of variable influenced by its position within a mineralized “region.” According to this theory, a “regionalized variable,” schematically represented in Figure 1, could be defined by Georges Matheron, on the basis of Danie Krige’s and Herbert Sichel’s theories, created new tools for the evaluation of mineral deposits Bertil and Gandin provided the same tools in meteorological and forestal fields. Geostatistics was born during the last century in the mining field. Reported data showed that KA minimizes interpolation errors and, for this reason, provides better interpolation results.
A comparison between kriging results obtained by KA and two of the most common data analysis softwares (Golden Software Surfer and ESRI Geostatistical Analyst for ArcMap) is presented in this paper. This new software, named “Kriging Assistant” (KA) and developed within the Department of Geoengineering and Environmental Technologies University of Cagliari, is able, with a marginal support of the user, to produce 2D and 3D grids and contour maps of sampled data. To help any data analysts during geostatistical analysis process, an innovative geostatistical software was created. This paper presents an overview of the geostatistics approach in data analysis and describes each operative step from experimental semivariogram calculation to kriging interpolation, focusing and underlining the experimental semivariogram modeling step. The interest in geostatistical tools has grown, and nowadays its techniques are applied in many branches of engineering where data analysis, interpolation, and evaluation are necessary. The purpose of this new science was to achieve an optimal evaluation of mining ore bodies. Geostatistics was created during the second half of 20th century by Georges Matheron, on the basis of Danie Krige’s and Herbert Sichel’s theories.