Residual statics estimation using the genetic algorithm

An optimization problem as complex as residual statics estimation in seismic image processing requires novel techniques. One interesting technique, the genetic algorithm, is based loosely on the optimization process forming the basis of biological evolution. The objective of this paper is to examine this algorithm’s applicability to residual statics estimation and present three new ingredients that help the algorithm successfully resolve residual statics. These three ingredients include (1) breaking the population into subpopulations with restricted breeding between the subpopulations, (2) localizing the search, to varying degrees, about the uncorrected input stack, and (3) modifying the optimization function to take account of CDP‐dependent structural features. Introducing subpopulations has the effect of enhancing the search when the volume of phase space being searched is large and limited information is given about where the algorithm should concentrate its efforts. Subpopulations work well initially,...

[1]  Kris Vasudevan,et al.  APPLICATION OF THE GENETIC ALGORITHM TO RESIDUAL STATICS ESTIMATION , 1991 .

[2]  Christof Stork,et al.  Hybrid Genetic Autostatics: New Approach For Large-Amplitude Statics With Noisy Data , 1992 .

[3]  J. Felsenstein.  The theoretical population genetics of variable selection and migration. , 1976, Annual review of genetics.

[4]  Daniel H. Rothman,et al.  Nonlinear inversion, statistical mechanics, and residual statics estimation , 1985 .

[5]  Heinz Mühlenbein,et al.  Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization , 1989, Parallelism, Learning, Evolution.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[8]  Jon F. Claerbout,et al.  Surface-consistent residual statics estimation by stack-power maximization , 1985 .

[9]  M. Sambridge,et al.  Genetic algorithms in seismic waveform inversion , 1992 .

[10]  R. A. Price,et al.  Lithoprobe southern Canadian Cordilleran transect: Rocky Mountain thrust belt to Valhalla gneiss complex , 1987 .

[11]  Mrinal K. Sen,et al.  Nonlinear multiparameter optimization using genetic algorithms; inversion of plane-wave seismograms , 1991 .

[12]  M. Turhan Taner,et al.  ESTIMATION AND CORRECTION OF NEAR‐SURFACE TIME ANOMALIES , 1974 .

[13]  J. Scales,et al.  Global search and genetic algorithms , 1992 .

[14]  Jerry A. Coyne,et al.  Genetics and speciation , 1992, Nature.

[15]  Kris Vasudevan,et al.  Simulated annealing statics computation using an order-based energy function , 1991 .

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .