39: Deep Kriging on the Sphere

Thomas M. Menino Convention & Exhibition Center 

Deep kriging combines classical kriging ideas with neural network–based representations, providing a flexible framework for spatial prediction beyond parametric covariance models. For global spatial data defined on the sphere, recently developed intrinsic random functions offer a principled approach to modeling non-stationarity and large-scale trends within spherical geometry.

In this work, we investigate deep kriging models for spatial processes on the sphere. Using a simulation framework based on spherical harmonic representations, we compare Euclidean deep kriging with geometry-aware formulations that incorporate great-circle structure and intrinsic covariance constructions. Our study highlights how classical intrinsic kriging ideas on the sphere can be integrated with deep learning–based spatial prediction, offering stable and interpretable modeling of global spatial processes.

kriging on the sphere

intrinsic random functions

deep neural networks

spatial statistics

nonstationary spatial processes 

Section on Statistics and the Environment