Analysis of variogram is a necessary step in 3D stochastic modeling, and different settings variograms can directly affect the final distribution model attribute. How to optimize setting parameters by unit facies type has become key geological modeling. This article focuses on reservoir within sedimentary background fluvial-deltas layer SII7+8 which develops multiple microfacies, such as fluvia...

Sequential Gaussian simulation (sgsim) and sequential indicator simulation (sisim) have emerged as powerful tools for stochastic imaging of Earth Science phenomena and are currently widely accepted fast simulation algorithms. In this paper, we will expand the sequential simulation toolbox by relying on a recent development in geostatistical theory (Journel, 1994) denoted as direct sequential si...

BACKGROUND Assessing heterogeneity in lung images can be an important diagnosis tool. We present a novel and objective method for assessing lung damage in a rat model of emphysema. We combined a three-dimensional (3D) computer graphics method-octree decomposition-with a geostatistics-based approach for assessing spatial relationships-the variogram-to evaluate disease in 3D computed tomography (...

The monitoring of earth surface dynamic processes at a global scale requires high temporal frequency remote sensing observations which are provided up to now by moderate spatial resolution sensors. However, the spatial heterogeneity within the moderate spatial resolution pixel biases non-linear estimation processes of land surface variables from remote sensing data. To limit its influence on th...

OBJECTIVES To determine the geographical distribution of cases of systemic lupus erythematosus (SLE) in a defined geographical area in the East Midlands, UK, and, in particular, to search for spatial variation in cases that may implicate the role of environmental factors in SLE aetiology. METHODS Six methods of case ascertainment were used. The postcode of the patient's domicile at time of fi...

Abstract As with a spatial variogram or spatial covariance, a principal purpose of estimating and modeling a space-time variogram is to quantify the spatial temporal dependence reflected in a data set. The resulting model might then be used for spatial interpolation and/or temporal prediction which might take several forms, e.g. kriging and Radial Basis functions. There are significant problems...

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional L...