f Inversion for seismic anisotropy using genetic algorithms

Seismic anisotropy is a complicated non-linear phenomenon in which quasi compressional(qP) and two quasi shear waves(qS1 and qS2) propagate in most directions isolated from singularity effects (Crampin 1991). Problems arise in inverting shear wave data due to the presente of singularities and the deviation of group from phase velocities so that linearization approaches are invalid (Chapman and Pratt 1992). Genetic Algorithms (GA) are non-linear optimization methods which are receiving increasing attention in the geophysics community (Sen & Stoffa 1991, Sambridge & Drijkoningen 1992) due to their robustness, efficiency and wide range of applicability. Just as simulated annealing is analogous to crystal annealing so are GA's analogous to the evolutionary processes of reproduction, crossover and mutation in nature. GA's use a population of models which are combined in such a way that more successful parameter combinations receive an increasing sampling rate. Previous attempts to invert shear wave data by MacBeth (1991) used a systematic search of a database of anisotropic materials. This approach is computationally intensive and is restricted by the precalculated database size. GA's appear to offeramore flexible and efficient anisotropic roversion scheme.