INTRODUCTION A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented. METHODS A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill. RESULTS Parameters Range...

Spatial dependence exists when the variation between observations is dependent on spatial location. In the present study, geostatistical methods were used to examine spatial dependence in adolescents’ perceptions of their neighborhoods: whether adolescents living in close proximity perceived their neighborhoods more similarly than adolescents living further apart. Participants included 343 adol...

Seismic post-stack inversion is one of the best techniques for effective reservoir characterization. This study intends to articulate application Model-Based Inversion (MBI) and Probabilistic Neural Networks (PNN) identification properties i.e. porosity estimation. MBI technique applied observe low impedance zone at porous formation. PNN a geostatistical that transforms volume into volume. Inve...

We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as appli...

groundwater resources are the most important sources of water consumption (agriculture, industry and drinking water) in arid and semi-arid regions. the management of these water resources is more expensive and difficult than surface waters; therefore more complicated and economic methods are needed for determination of their quality and quantity. in this study geostatistical methods of kringing...

Recent research has suggested that the geostatistical structure of ground-penetrating radar data may be representative of the spatial structure of hydraulic properties. However, radar images of the subsurface can change drastically with application of signal processing or by changing the signal frequency. We perform geostatistical analyses of surface radar reflection profiles in order to invest...

Accurate prediction of petroleum reservoir performance requires reliable models of the often complex reservoir heterogeneity. Geostatistical simulation techniques generate multiple realizations of the reservoir model, all equally likely to be drawn. Traditional to geostatistics, geological continuity is represented through the variogram. The variogram is limited in describing complex geological...

We present a Bayesian inversion method for the joint inference of high-dimensional multiGaussian hydraulic conductivity fields and associated geostatistical parameters from indirect hydrological data. We combine Gaussian process generation via circulant embedding to decouple the variogram from grid cell specific values, with dimensionality reduction by interpolation to enable Markov chain Monte...

Functional inversion based on local approximate solutions (LAS) is developed for steady-state flow in heterogeneous aquifers. The method employs a set of LAS of flow to impose spatial continuity of hydraulic head and Darcy fluxes in the solution domain, which are conditioned to limited measurements. Hydraulic conductivity is first parameterized as piecewise continuous, which requires the additi...