COMPARING GEN ETIC ALGORITHM AND PARTIC LESWARM OPTIMAIZA TION APPROACHES IN INVERSION O F SURFACE WAVE DATA

Shear-wave velocity (Vs) is an important parameter for site characterization in geotechnical and earthquake engineering studies.Shear-wave velocity is in situ measured by various methods including borehole tests, shear-wave refraction and reflection studies and surface-wave techniques. In recent years, surface waves have been increasingly used for d eriving Vsas a function of depth. But, inversio n is the key problem in processing surface wave data for estimating velocity of S-waves. In present study we applied two metaheuristic optimization approac hes, Genetic algorithm (GA) and particle swarm optimization (PSO), for inversion of Rayleigh wave dispersi on curves. GA and PSO are the global optimizat ion methods that belong to metaheuristic searching algorithm s. In geophysical surveys, the application of me taheuristic techniques is novel. After programming the GA and PSO in MATLAB, its efficiency was investigated by a synthetic model. At the end, GA and PSO inversion algorithms were tested on an experrimental Rayleigh wave dispersion curve data which was co llected for seismic hazard assessment in an area of city of Tabriz in the northwest of Iran. Real datasets we re obtained from one stations in south part of Tabriz (near Elgoli Road) that contain Miocene –Pliocene a nd pyroclastic bedrocks. The results proved a pplicability of proposed inversion algorithms in Rayleigh wave dispersion curve inversion. Also, asses sment of two inversion algorithms showed that PSO invers ion algorithm, because of few parameters to ad just, is fast and easy to implement compared to GA inversioon algorithm.