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Journal of Engineering
Volume 2013, Article ID 960105, 10 pages
Research Article

The Importance of the Model Choice for Experimental Semivariogram Modeling and Its Consequence in Evaluation Process

1Department of Social Science, University of Cagliari, 09123 Sardinia, Italy
2Department of Civil and Environmental Engineering and Architecture, University of Cagliari, 09123 Sardinia, Italy

Received 8 January 2013; Revised 14 June 2013; Accepted 24 July 2013

Academic Editor: Sergio Nardini

Copyright © 2013 Alessandro Mazzella and Antonio Mazzella. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.