Sampling Gaussian Stationary Random Fields: A Stochastic Realization Approach

TL;DR

This paper introduces an efficient stochastic realization method for sampling Gaussian stationary random fields, reducing computational costs compared to traditional covariance matrix decomposition, and enabling multiscale simulations.

Contribution

It presents a novel ARMA-based approach for sampling Gaussian stationary fields that is computationally efficient and suitable for multiscale modeling.

Findings

Method outperforms covariance decomposition in efficiency

Samples generated with low-order ARMA models

Effective for multiscale and high-dimensional fields

Abstract

Generating large-scale samples of stationary random fields is of great importance in the fields such as geomaterial modeling and uncertainty quantification. Traditional methodologies based on covariance matrix decomposition have the diffculty of being computationally expensive, which is even more serious when the dimension of the random field is large. This paper proposes an effcient stochastic realization approach for sampling Gaussian stationary random fields from a systems and control point of view. Specifically, we take the exponential and Gaussian covariance functions as examples and make a decoupling assumption when there are multiple dimensions. Then a rational spectral density is constructed in each dimension using techniques from covariance extension, and the corresponding autoregressive moving-average (ARMA) model is obtained via spectral factorization. As a result, samples of the random field with a specific covariance function can be generated very effciently in the space domain by implementing the ARMA recursion using a white noise input. Such a procedure is computationally cheap due to the fact that the constructed ARMA model has a low order. Furthermore, the same method is integrated to multiscale simulations where interpolations of the generated samples are achieved when one zooms into finer scales. Both theoretical analysis and simulation results show that our approach performs favorably compared with covariance matrix decomposition methods.