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...

Measured grades rely on the relative positions of measurement locations within the ore site. These measurements at a set of locations give some insight into regional variability. This variability determines the regional behaviour as well as the predictability of the grade. The larger the variability, the more heterogeneous is the geological environment1. One of the tools used to measure regiona...

Spatial disagregation is needed when environmental models of climate, air quality, hydrology, etc. require input data at a finer scale than available or when models produce outputs at a coarser scale than required by users. Area-to-Point Kriging is a common geostatistical framework to address the problem of spatial disaggregation from block to point support. Spatial disaggregation using Area-to...

The soil roughness at the field level is an easy visually perceptible notion, but difficult to describe numerically. The objective of this paper is to propose a method to establish quantitative and descriptive soil roughness indices. A measurement system was developed using a laptop-computer and a laser cell. The elevation data are measured on the ground along a square array. They are treated w...

Abstract: In this paper, the way topographic spatial information changes with resolution was investigated using semi-variograms and an Independent Structures Model (ISM) to identify the mechanisms involved in changes of topographic parameters as resolution becomes coarser or finer. A typical Loess Hilly area in the Loess Plateau of China was taken as the study area. DEMs with resolutions of 2.5...

When modelling a large area, models that can take into a count the variation from the general mean in small sub-areas could perform better in prediction than a general model fitted to entire dataset. One method for adjusting the large-area models for such variation is kriging, in which the predictions are corrected with the aid of neighbouring observations. A variogram represents the spatial co...

Model variograms describe the space domain statistics of magnetic and gravity data. Variogram analysis can be used to map intensity, depth, and scaling exponent (self-correlation) of source. In previous statistical methods the measured data were gridded and transformed to the wavenumber domain; then their power spectrum was analyzed using a spectral model. To avoid the loss and distortion of in...

Modified GSLIB FORTRAN 77 routines are given in this paper for estimating and modeling space-time variograms. Two general families of models are incorporated in the programs: these are the product model and the product-sum model, both based on the decomposition of the space-time covariance in terms of a space covariance and a time covariance. The GSLIB kriging program has also been modified to ...

Aggressive device scaling has made it imperative to account for process variations in the design flow. A robust model of process variations is an essential requirement for any meaningful variation aware design analysis and optimization. Unfortunately the previous approaches on extracting spatial correlation function assume ergodicity and isotropy while estimating the inter-die(global) component...