Improved Particle Swarm Algorithm For Wave Impedance Inversion

In the later stage of petroleum exploration,we need in-depth analysis of the structure,shape,seze of the reservoir,lithology,physical property,etc.By the way of impedance inversion,we can gain more detaied description of such physical parameters.At the same time, the existing linear, single model inversion and inversion of intelligent algorithm is difficult to meet the needs of increasingly demanding.Therefore, this paper based on particle swarm algorithm combined with variety of intelligent algorithms evolutionary wave impedance inversion, in order to provide a reference for the idea of reservoir prediction.This paper introduces the basic theory of particle group, the basic processes, and on this basis, with reference to current research, complementing the PSO algorithm learning factor, speed boundary of the set, some of the strategies to adjustment of inertia weight.To gain further insight into the performance of particles search, we focused on the influence of the inertia weight on the convergence properties of the particles.A further preferred through a function test, searching for relatively good parameter combinations.It is noteworthy that, By using this combination,we can enhance the performance of the global search algorithm,but can not be further improved the local populations of finely search capabilities.By understanding genetic algorithm performance of genetic algorithm, thus,we have a more comprehensive uvderstanding.By the way of crossover and mutation, random walk constantly remodeling, destruction of the gene, and by means of a random walk, the aAdaptive algorithm can increase or reduce the scope of the numerical solution of the space, as much as possible to avoid invalid walk.For shortage of traditional particle swarm optimization, genetic algorithm in high-dimensional problem solving, in order to avoid premature particles, increase the diversity of particles, this paper presents Particle swarm genetic fusion algorithm to provide a feasible method for improving the particle precocious problem.By using embedded genetic algorithm, it enrich population diversity, and memory fine particles are retained by means of the gene. Subsequently, the improved method with improved genetic before, particles function test, to do a comparison.Prior to the actual data inversion models were tested and wavelet extraction test in order to provide impedance inversion feasibility analysis, and on this basis were the actual data impedance inversion.