Generating Spatially Correlated Fields with a Genetic Algorithm

ÐGenerated realizations of random ®elds are used to quantify the natural variability of geological properties. When the realizations are used as inputs for simulations with a deterministic model, it may be desirable to minimize di€erences between statistics of sequential realizations and make the statistics close to ones speci®ed at generating the realizations. We describe the use of a genetic algorithm (GA) for this purpose. In unconditioned simulations, statistics of the GA-generated realizations were signi®cantly closer to the input ones than those from sequential Gaussian simulations. Distributions of generated values at a particular node over sequential realizations were close to the normal distribution. The GA is computationally intensive and may not be suitable for ®ne grids. The sequential Gaussian algorithm conditioned with GA-generated values on a coarse grid can produce a set of realizations with similar statistics for the ®ne grids embedding the coarse one. # 1998 Elsevier Science Ltd. All rights reserved Code available at http://www.iamg.org/CGEditor/index.htm