Spatial decorrelation methods : beyond MAF and PCA (original) (raw)

In the geostatistical treatment of multivariate data sets the joint modelling of their spatial continuity is usually required. While it is possible to automate the inference of a suitable variogram model a transformation of the set of attributes into spatially uncorrelated factors that can be simulated independently, might be desirable. Standard methods used in geostatistics for this purpose are principal component analysis (PCA) and the method of minimum/maximum autocorrelation factors (MAF). Both methods have restrictions in their applicability and a more flexible approach may be more suitable such as that offered by approximate joint diagonalisation (AJD) methods common in Blind Source Separation. The application of two AJD methods to a family of experimental semivariogram matrices is explored here and the performance is assessed on a number of simulated data sets with different spatial characteristics. A comparison with MAF and PCA shows that the use of AJD algorithm results in ...