GENERATING SPATIALLY CORRELATED FIELDS WITH A GENETIC ALGORITHM

Generated realizations of random fields are used to quantify the natur al variability of geological properties. When the realizations are use d as inputs for simulations with a deterministic model, it may be desi rable to minimize differences between statistics of sequential realiza tions and make the statistics close to ones specified at generating th e realizations. We describe the use of a genetic algorithm (GA) for th is purpose. In unconditioned simulations, statistics of the GA-generat ed realizations were significantly closer to the input ones than those from sequential Gaussian simulations. Distributions of generated valu es at a particular node over sequential realizations were close to the normal distribution. The GA is computationally intensive and may not be suitable for fine grids. The sequential Gaussian algorithm conditio ned with GA-generated values on a coarse grid can produce a set of rea lizations with similar statistics for the fine grids embedding the coa rse one. (C) 1998 Elsevier Science Ltd. All rights reserved.