Mapping geochemical anomaly is essential for prospecting. Kriging is often used to characterize the spatial variability of geochemistry. However, due to its smooth effects, it is incapable of detecting multi-location uncertainty of geochemistry estimate. Sequential gaussian simulation (SGS) can model the spatial uncertainty through generation of several equally probable stochastic realizations....

Three new methods are developed to generate neutral spatial models for pattern recognition on raster data. The first method employs Genetic Programming (GP), the second Sequential Gaussian Simulation (SGS), and the third Conditional Pixel Swapping (CPS) in order to produce sets of ”neutral images” that provide a probabilistic assessment of how unlikely an observed spatial pattern on a target im...

This work is a study of multivariate simulations of pollutants to assess the sampling uncertainty for the risk analysis of a contaminated site. The study started from data collected for a remediation project of a steelworks in northern Italy. The soil samples were taken from boreholes excavated a few years ago and analyzed by a chemical laboratory. The data set comprises concentrations of sever...

Abstract In the mid-1980s, still in his young 40s, André Journel was already recognized as one of giants geostatistics. Many contributions from new research program at Stanford University had centered around indicator methods that he developed: kriging and multiple kriging. But when second crop graduate students arrived Stanford, lacked an approach to conditional simulation not tainted by what ...

Abstract Porosity is one of the main variables needed for reservoir characterization. For this volumetric variable, there are many methods to simulate spatial distribution. In article, porosity was analyzed and modeled in local global simulation, Sequential Gaussian simulation (SGS) Random Function (GRFS) were applied. Also, kriging used estimate at specific locations. The purpose work investig...

background: the area of contaminated lands exposed to the health risk of environmental pollutants is a matter of argument. in this study, a new method was developed to estimate the amount of area that is exposed to higher than normal levels of cr, mn, and v. methods: overall, 170 soil samples were collected from the upper 10 cm of soil in an arid area in central part of iran in semnan province....

hydrograph modeling and prediction of groundwater levels are the main concerns of most hydrogeological calculations and water resource management process. the present study is an application of sequential gaussian simulation (sgsim) method for predicting groundwater levels using recorded monthly data (180 months) related to 21 piezometers of behbahan aquifer, southwest of iran. to generate real...

In geochemical exploration, there are various techniques such as univariate and multivariate statistical methods available for recognition of anomalous areas. Univariate techniques are usually utilized to estimate the threshold value, which is the smallest quantity among the values representing the anomalous areas. In this work, a combination of the Sequential Gaussian Simulation (SGS) and Gap ...

Realistically rough stochastic realizations of subglacial bed topography are crucial for improving our understanding basal processes and quantifying uncertainty in sea-level rise projections with respect to topographic uncertainty. This can be achieved Sequential Gaussian Simulation (SGS), which is used generate multiple non-unique geological phenomena that sample the space. However, SGS very C...

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian o...