Spatial variables in the geosciences often display different patterns of variability at different spatial scales. When these variables are cross-correlated, the magnitude of the cross-correlation may differ at the different scales of spatial variability. This scale-dependent spatial variability and cross-correlation can be investigated by geostatistical factor analysis, which consists of four steps: Semi-variogram analysis. Semi-variograms and cross-semi-variograms are estimated from the experimental data and are used to identify the scales of variability. The different scales of variability can be interpreted and can often be linked to physical causes. Fitting a model to the set of semi-variograms and cross-semi-variograms. A consistent model, such as the linear model of coregionalization, is fitted to the set of experimental semi-variograms and cross-semi-variograms. Factorial cokriging is used to estimate an individual factor, an individual component or a combination of components of any of the coregionalized variables. Interpretation of the estimated factor or component. The purpose of this paper is to describe programs for performing the second and third steps. Programs are also provided for automatic fitting of a linear coregionalization model and for estimating, by factorial cokriging, a single factor, a single component or a combination of components. Although programs for factorial kriging and programs for cokriging can be found elsewhere the software described here (FACTOR2D) provides a complete solution by combining factorial cokriging with a program for fitting a linear coregionalization model (LCMFIT2). The use and performance of the programs is demonstrated on simulated and real case studies. (C) 2002 Elsevier Science Ltd. All rights reserved. References: 9
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