Abstract

Seismic inverse problems include minimization of error between the observed and synthetic seismograms to obtain the model parameters that will provide information about the subsurface. Generally we face two problems: 1) the model space is very large and 2) the error function is multimodal. Existing calculus based methods are local in scope and easily get trapped in local minima of error function. Other methods such as simulated annealing, genetic algorithm and harmony search can be applied to solve such global optimization problems and these don