Secondary data are important in geostatistical simulation of continuous variables. Seismic data and geological trends are used for porosity modeling. Porosity is used for permeability and residual water saturation modeling. Multiple mineral or contaminant concentrations must often be modeled for mining and environmental applications. Sequential Gaussian simulation (or some other variant of Gaus...

Our main objective in the present study was to assess the spatial variation of chemical and physical water properties. Prior to the design of groundwater quality monitoring networks, it is essential to investigate the spatial structure of the groundwater quality variables. A case study is presented which used ground water quality observations from groundwater domestic wells in the Dameghan, Ira...

Rapid transmission of contaminants in groundwater can occur alluvial gravel aquifers that are permeated by highly conductive small-scale open framework gravels (OFGs). This structure and the associated distribution hydraulic properties is complex, so assessments contamination risks these uncertain. Geostatistical models, based on lithological data, be used to quantitatively characterize this st...

Post-mineralization activities may cause difficulties in the process of ore modeling in porphyry deposits. Sungun, NW Iran, is one of the porphyry copper deposits, in which dyke intrusions have made ore modeling more complicated than expected. Among different kinds of dykes, two types were chosen and the consequent geostatistical analyses were applied on. In this study, simple directional vario...

Stochastic seismic inversion is a family of inversion algorithms in which the inverse solution was carried out using geostatistical simulation. In this work, a new 3D stochastic seismic inversion was developed in the MATLAB programming software. The proposed inversion algorithm is an iterative procedure that uses the principle of cross-over genetic algorithms as the global optimization techniqu...

We explore and compare different methods for the spatial clustering of geostatistical data. A new methodology based on the likelihood is proposed and compared to the approach by Allard and Monestiez (1999). Both methods are compared on a heavy metal concentration data set in the Swiss Jura.

Raster based digital elevation models (DEM) are the basis of some of the most important GIS workflows: hydrologic modeling, site suitability, and cost path analysis. While there are several techniques for generating digital elevation models (DEMs), none of them can produce a true elevation surface. Locally varying measurement error and the inexactness of the interpolation methods contribute to ...

This paper focuses on an overview of kernel density estimation especially for what concerns the choice of bandwidth and intensity parameters according to local conditions. A case study inherent seismic risk analysis of the old town centre of Potenza hilltop town has been discussed, with particular attention to the evaluation of the possible local amplifying factors. This first integrated applic...

Reconciling data from different scales is a longstanding problem in reservoir characterization. Data from core plugs, well logs of different types, and seismic data must all be accounted for in the construction of a geostatistical reservoir model. These data are at vastly different scales and it is inappropriate to ignore the scale difference when constructing a geostatistical model. Geostatist...