Efficiency and accuracy in simulation of random fields

A direct method for the conditional simulation of a stationary, Gaussian scalar random field is compared with an alternative formulation which uses frequency domain probability density functions. In both cases the random field is described by given correlation or spectral density functions, and no restrictions are placed on these functions, except that they must be positive definite. Efficient implementation techniques are investigated for both general methods. The major computational effort in the most efficient implementations of both procedures is in the solution of linear algebraic equations in which the coefficients are spectral densities. The direct method is shown to be significantly more efficient than existing methods for applying the probability density function technique. However, a new implementation method for the latter technique is also presented, and it equals the efficiency of the direct method. Problems of numerical accuracy due to ill-conditioned matrices are shown not to be severe except when using an inherently problematic form for the spectral density. Numerical examples demonstrate that either method can simulate highly coherent time histories.