Abstract
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. The purpose of this new science was to achieve an optimal evaluation of mining ore bodies. 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. 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. To help any data analysts during geostatistical analysis process, an innovative geostatistical software was created. 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. 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. Reported data showed that KA minimizes interpolation errors and, for this reason, provides better interpolation results.
Highlights
Geostatistics was born during the last century in the mining field
Type “III” couples contribute to the definition of the semivariogram shape within the range being tied by the law of the phenomenon of which the semivariogram is a graphical representation
Novel technique developed and already validated for the semivariogram modelling and variographic parameters estimation are based on the construction of a “filtered” experimental semivariogram
Summary
Georges Matheron, on the basis of Danie Krige’s and Herbert Sichel’s theories [1,2,3,4,5], created new tools for the evaluation of mineral deposits; Bertil and Gandin provided the same tools in meteorological and forestal fields [6, 7] This new approach was based on the “regionalized variables” theory [8]: a new type of variable influenced by its position within a mineralized “region.” According to this theory, a “regionalized variable,” schematically represented, could be defined by z (x) = m (x) + k (x) ,.
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