Blind source separation for non-stationary random fields

TL;DR

This paper extends spatial blind source separation (SBSS) to handle non-stationary random fields, introducing three new estimators and demonstrating their effectiveness through simulations and geochemical data analysis.

Contribution

The authors develop three novel estimators for SBSS that account for non-stationarity, and prove their theoretical properties such as identifiability and affine equivariance.

Findings

The new estimators perform well in simulations.

They effectively analyze non-stationary geochemical data.

The approach improves over traditional SBSS in non-stationary contexts.

Abstract

Regional data analysis is concerned with the analysis and modeling of measurements that are spatially separated by specifically accounting for typical features of such data. Namely, measurements in close proximity tend to be more similar than the ones further separated. This might hold also true for cross-dependencies when multivariate spatial data is considered. Often, scientists are interested in linear transformations of such data which are easy to interpret and might be used as dimension reduction. Recently, for that purpose spatial blind source separation (SBSS) was introduced which assumes that the observed data are formed by a linear mixture of uncorrelated, weakly stationary random fields. However, in practical applications, it is well-known that when the spatial domain increases in size the weak stationarity assumptions can be violated in the sense that the second order dependency is varying over the domain which leads to non-stationary analysis. In our work we extend the SBSS model to adjust for these stationarity violations, present three novel estimators and establish the identifiability and affine equivariance property of the unmixing matrix functionals defining these estimators. In an extensive simulation study, we investigate the performance of our estimators and also show their use in the analysis of a geochemical dataset which is derived from the GEMAS geochemical mapping project.