A framework for conditional simulation of nonstationary non-Gaussian random field and multivariate processes

We propose a conditional simulation framework for the nonstationary/nonhomogeneous non-Gaussian/Gaussian field and multivariate processes. There are three distinct stages within the proposed framework. The first one is to sample directly the unconditional nonstationary non -Gaussian field or multivariate processes without resorting to the underlying Gaussian charac-teristics. The second stage maps the unconditional samples and the conditions (i.e., observations) to the Gaussian space and uses them to find the conditional mean and covariance. In the final stage, the conditional samples by considering the effect of the conditioning are generated in Gaussian space and mapped back to the original non-Gaussian field. The efficiency of the framework relies on the efficient unconditional simulation algorithms (i.e., the iterative rank -dependent shuffling algorithm, and the iterative power and amplitude correction algorithm), and the reuse of the unconditional samples for the conditional simulation. The conditional samples match the conditional mean and conditional variance-covariance. The framework can directly apply to the random field and multivariate processes defined by the marginal probability distribution function and the covariance-variance information, or the time-frequency dependent PSD function. The framework is illustrated using several examples.

Automated summary

This study proposes a conditional simulation framework for nonstationary/nonhomogeneous non-Gaussian/Gaussian field and multivariate processes. The framework consists of three stages, utilizing efficient unconditional simulation algorithms and the reuse of unconditional samples in conditional simulation to improve efficiency. The conditional samples match the conditional mean and conditional variance-covariance.