RESIDUAL STATICS ESTIMATION USING THE GENETIC ALGORITHM

An optimization problem as complex as residual statics estimation in s eismic image processing requires novel techniques. One interesting tec hnique, the genetic algorithm, is based loosely on the optimization pr ocess forming the basis of biological evolution. The objective of this paper is to examine this algorithm's applicability to residual static s estimation and present three new ingredients that help the algorithm successfully resolve residual statics. These three ingredients includ e (1) breaking the population into subpopulations with restricted bree ding between the subpopulations, (2) localizing the search, to varying degrees, about the uncorrected input stack, and (3) modifying the opt imization function to take account of CDP-dependent structural feature s. Introducing subpopulations has the effect of enhancing the search w hen the volume of phase space being searched is large and limited info rmation is given about where the algorithm should concentrate its effo rts. Subpopulations work well initially, but as the performance increa ses, the search efficiency decreases. Thus, search efficiency is depen dent on both the subpopulation size and the present performance of the subpopulation. The greediness of genetic algorithms in their rapid ac ceptance of a local minimum can be recompensed through a more cautious and user-controlled exploration of the phase space. Specifically, gen etic algorithms can be ''fed'' the uncorrected input stack as a way of biasing the search in the region of phase space that contains the rou gh event images observable in most uncorrected seismic stacks. We comp are three types of starting populations: (1) a randomized population, (2) a biased start with a randomized population save one individual re presenting the input stack, and (3) a biased start restricted to a slo wly expanding (generation-dependent) volume of phase space. Efficient searches also require an optimization function that places the perfect ly corrected seismic image at the function's global maximum. Construct ing such a function is nontrivial, and we implement a seismic data set that allows us to examine the genetic algorithm's sensitivity to inap propriate optimization functions.