Abstract：Quantum-behaved Particle Swarm Optimization Algorithm breaks the Newton random searching rule which the Particle Swarm Optimization Algorithm needs to follow,by adding quantum motion in the searching process,not only the ability of global optimization and convergence speed were improved,but also the numbers of the parameters which need to be controlled in the algorithm were decreased,as a result the global optimal solution which could not be converged to in the traditional Particle Swarm Optimization got resolved.The principle of the Quantum-behaved Particle Swarm Optimization Algorithm is simple,there are only a few parameters needed to be controlled,it is easy to realized,and it also can be further used in multi-parameter and multi-extreme value geophysical inversion.In this paper the Quantum-behaved Particle Swarm Optimization Algorithm was utilized to conduct pre-stack AVO elastic parameter inversion,the inversion results for the noise-free and noisy models demonstrated the effectiveness,stability as well as the excellent anti-noise performance of the algorithm.
