We investigate the accuracy and resolution of the variogram analysis method (Maus, 1999) on syn thetic and real data. Syn thetic magnetic ight line data are generated forbasemen t models of idealized geological setups. Comparing variogram depths of the data with true model depths shows that variogram depths are accurate and un biased as long as the data analysis window is larger than 10 times t...

In this paper we consider the estimation of the variogram using the method of weighted least squares and in the process we propose two alternative set of weights. We study the asymptotic sampling properties of these estimators and compare their efficiencies both analytically and through simulations. To illustrate the above methods, we consider daily rainfall across Switzerland measured at 467 l...

Covariance and variogram functions have been extensively studied in Euclidean space. In this article, we investigate the validity of commonly used covariance and variogram functions on the sphere. In particular, we show that the spherical and exponential models, as well as power variograms with 0 < α ≤ 1, are valid on the sphere. However, two Radon transforms of the exponential model, Cauchy mo...

Various methods, such as the moving block bootstrap, have been developed to resample dependent data. These contain features that attempt to retain the dependence structure in the data. Asymptotic results for these methods have been based on increasing the number of observations as the observation region increases, combined with restrictions on the range of dependence of the data. In this work, ...

It is not simple to model cross and auto-variograms to describe the covariation of two or more soil properties, since the models that are fitted must meet certain constraints. These constraints are most readily met by fitting a linear model of coregionalization (LMCR). This presents practical problems. Not all combinations of authorized variogram functions constitute a LMCR. This paper presents...

The classical variogram estimate is convenient but can be unacceptably variable. Improved estimators are possible, especially when the locations of the available data are highly clustered. Using a simple theoretical example, we demonstrate that weighting can dramatically increase the efficiency of classical variogram estimates from clustered data. We give expressions for the weights that lead t...

Geostatistical models often require a variogram or covariance model for kriging and krigingbased simulation. Next to the initial decision of stationarity, the choice of an appropriate variogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental variograms with a nested combination of proven models such as the spherical, exponential, ...

A method for the estimation of music similarity based on the use of the standardized variogram as clustering algorithm for Mel Frequency Cepstral Coefficients, is detailed in this report. The standardized variogram is used for the compression of the information of MFCCs. The algorithm is submitted to the Audio Music Similarity task of MIREX 2011, in occasion of the 12th ISMIR Conference.

This paper discusses the use of robust geostatistical methods on a data set of rainfall measurements for Switzerland. The variables are detrended via nonparametric estimation penalized with a smoothing parameter. The optimal trend is computed with a smoothing parameter based on cross-validation. The variogram is then estimated by a highly robust estimator of scale. The parametric variogram mode...

In this study, a variogram-based texture analysis was tested for classifying detailed urban land-use classes, such as mobile home, single-family house, multi-family house, industrial, and commercial from a digital color infrared aerial photograph. Spectral classification was first carried out to separate the building class from non-building classes. Then, a building-presence binary image was ge...