Fast Gap-Filling of Massive Data by Local-Equilibrium Conditional Simulations on GPU

Abstract

AbstractThe ever-growing size of modern space–time data sets, such as those collected by remote sensing, requires new techniques for their efficient and automated processing, including gap-filling of missing values. Compute Unified Device Architecture-based parallelization on graphics processing units (GPUs) has become a popular way to dramatically increase the computational efficiency of various approaches. Recently, a computationally efficient and competitive yet simple spatial prediction approach inspired by statistical physics models, called the modified planar rotator method, was proposed. Its GPU implementation allowed additional impressive computational acceleration exceeding two orders of magnitude in comparison with central processing unit calculations. In the current study, a rather general approach to modeling spatial heterogeneity in GPU-implemented spatial prediction methods for two-dimensional gridded data is proposed by introducing spatial variability to model parameters. Predictions of unknown values are obtained from non-equilibrium conditional simulations, assuming “local” equilibrium conditions. It is demonstrated that the proposed method leads to significant improvements in both prediction performance and computational efficiency.