Particle Swarm and Differential Evolution Optimization - Global Optimization for Geophysical Inversion
Puneet Saraswat · GEO 2010 · 2010
Inversion of pre- and post-stack seismic data for acoustic and shear impedance is highly non-linear and ill-posed. In this paper we report on the application of two new global optimization schemes, namely, Particle Swarm Optimization (PSO) and Differential Evolution (DE) to the problem of stochastic inversion of post-stack seismic data. A starting model is drawn from a fractional Gaussian distribution (based on a fractal model) and a suitably defined objective function is optimized in search of acceptable models using PSO and DE. Our investigations reveal that both the methods have nice convergence properties. However, the DE converges at least 10 times faster than PSO. We demonstrate the performance of these methods with application to synthetic and field seismic data. The social behavior observed in a flock (swarm) of birds and in insects searching food has been simulated to develop a global optimization strategy popularly known as the Particle Swarm Optimization (PSO). Particle Swarm Optimization (PSO) emulates the social behaviours in a flock of birds (swarm) in solving an optimization problem. It utilizes both local and global properties of the swarm to formulate a novel search strategy that guides the swarm towards the best solution with constant updating of the cognitive and social knowledge of the particles in the swarm.