Seismic Wavelet Estimation Based on Adaptive Chaotic Embedded Particle Swarm Optimization Algorithm

Cumulants matching method for seismic wavelet extraction is ultimately a multi-parameters, multi-extremes nonlinear functional optimization process. in this paper, a novel improved PSO algorithm (Adaptive Chaotic Embedded Particle Swarm Optimization, ACEPSO) has been proposed to be applied in wavelet parameters estimation. ACEPSO embeds chaotic variables in standard particle swarm optimization algorithm, and adjusts parameters nonlinearly and adaptively. It also estimates particles whether being focusing or discrete by judging the population fitness variance of particle swarm and average distance amongst points, then chaotic researching is applied to escaping from premature convergence. Simulation experimental results in wavelet extraction of synthetic seismogram and real seismic records show that this algorithm has high precision, good applicability and stability in seismic wavelet estimation.