量子行为的粒子群算法在叠前AVO反演中的应用

Application of Quantum-behaved Particle Swarm Optimization Algorithm in AVO inversion

量子行为的粒子群优化算法突破了粒子群优化算法所遵循的牛顿随机搜寻,在搜索过程中加入了量子运动,既改善了全局优化的能力和收敛速度,又减少了算法中需要控制的参数,有效地解决了传统粒子群优化算法无法收敛到全局最优解的问题。基于量子行为的粒子群优化算法原理简单,需要控制的参数很少,易于实现,可以进一步用于多参数、多极值地球物理反演。本文使用基于量子行为的粒子群优化算法进行叠前AVO弹性参数反演,无噪声和加噪声模型的反演结果说明了算法的有效性和稳定性,以及良好的抗噪性。

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.