The core idea of PSO is that each particle searches the best solution of optimization problems according to “information sharing” between surrounding particles and itself. PSO has fast convergence speed and high global search capability. For low accuracy and divergent results of elementary PSO, this paper proposes a kind of PSO with adaptive parameters and boundary constraints. Inertia weight and learning factors increase or decrease linearly with iterative process, in order that the particles search the global space in early period of the algorithm and converge towards the global optimum later. At the same time, the author sets particle boundary constraints to ensure the optimization accuracy. Theoretical analysis and numerical simulation results show the efficiency and high optimization accuracy of the designed method.

The seismic wavelet estimation is finally a multi-dimension, multi-extreme and multi-parameter optimization problem. PSO is easy to fall into local optimum, which has simple concepts and fast convergence. This paper proposes an improved PSO with adaptive parameters and boundary constraints, in ensuring accuracy of the algorithm optimization and fast convergence. Simulation results show that the methods have good applicability and stability for seismic wavelet extraction.