Theory and generation of conditional, scalable sub‐ G aussian random fields

Many earth and environmental (as well as a host of other) variables, Y , and their spatial (or temporal) increments, Δ Y , exhibit non‐Gaussian statistical scaling. Previously we were able to capture key aspects of such non‐Gaussian scaling by treating Y and/or Δ Y as sub‐Gaussian random fields (or processes). This however left unaddressed the empirical finding that whereas sample frequency distributions of Y tend to display relatively mild non‐Gaussian peaks and tails, those of Δ Y often reveal peaks that grow sharper and tails that become heavier with decreasing separation distance or lag. Recently we proposed a generalized sub‐Gaussian model (GSG) which resolves this apparent inconsistency between the statistical scaling behaviors of observed variables and their increments. We presented an algorithm to generate unconditional random realizations of statistically isotropic or anisotropic GSG functions and illustrated it in two dimensions. Most importantly, we demonstrated the feasibility of estimating all parameters of a GSG model underlying a single realization of Y by analyzing jointly spatial moments of Y data and corresponding increments, Δ Y . Here, we extend our GSG model to account for noisy measurements of Y at a discrete set of points in space (or time), present an algorithm to generate conditional realizations of corresponding isotropic or anisotropic random fields, introduce two approximate versions of this algorithm to reduce CPU time, and explore them on one and two‐dimensional synthetic test cases.