Ore grade is one of the main variables that characterise an orebody. Almost every mining project begins with the determination of ore grade distribution in three-dimensional space, a problem often reduced to modelling the spatial variability of ore grade values. So far, this has been achieved following the geostatistical approach, and more precisely, it’s main process of structural analysis. St...

Introduction There are different national approaches to the analysis, processing and presentation of indoor radon data. Some of them were presented at the international workshop in Lausanne (IGAR 2005). EU radon surveys were compiled in JRC Ispra summary report (Dubois 2005). Swiss experience on indoor radon data treatment and mapping using geostatistical approaches (both interpolations and sim...

Meteorological data are used in many studies, especially in planning, disaster management, water resources management, hydrology, agriculture and environment. Analyzing changes in meteorological variables is very important to understand a climate system and minimize the adverse effects of the climate changes. One of the main issues in meteorological analysis is the interpolation of spatial data...

Sciarretta A., Trematerra P. (2014): Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests. Plant Protect. Sci., 50: 97–110. Spatial heterogeneity in agricultural systems is recognised as an important source of variability to be investigated. In the evolution of Integrated Pest Management (IPM), patte...

Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (...

The work deals with development and application of Radial basis functions neural networks (RBFNN) for spatial predictions. Geostatistical tools for spatial correlation analysis (variography) are used to qualify and quantify the estimation results. Geostatistical analysis is performed on the residuals obtained at the training and test sample locations. Variogram of residuals explores spatial cor...

Hybrid geostatistical models aim at mimicking depositional events. The resulting models have the capability to simulate realistic stratigraphic structures for a variety of environments. However, this family of algorithms requires a high degree of parameterization. Therefore, having a good knowledge of the model parameters sensitivity is vital for understanding the behavior of such models. In th...

In this article objective have been made to reviews different geostatistical methods available to estimate and simulate petrophysical properties (porosity and permeability) of the reservoir. Different geostatistical techniques that allow the combination of hard and soft data are taken into account and one refers the main reason to use the geostatistical simulation rather than estimation. Uncert...