An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem

摘要

The elastic parameter inversion technique for prestack seismic data, which combines the intelligentoptimization algorithms with Amplitude Variation with Offset (AVO) technology, is aneffective method for oil and gas exploration. However, when certain biological-evolution–basedoptimization algorithms, eg, genetic algorithms, are used to solve this problem, the computationexhibits fast convergence and a strong tendency to be trapped to a local optimum,thereby leading to unsatisfactory inversion results. To address this issue, this paper proposes aswarm-intelligence-based method-Particle Swarm Optimization (PSO) algorithm to handle theelastic parameter inversion problem. Based on the Aki-Richards approximation to the Zoeppritzequations, the improvedPSOalgorithm adopts a special initialization strategy, which can enhancethe smoothness of the initialization parametric curves. Extensive experimental research confirmsthe superiority of the proposed algorithm. Specifically, the improved PSO algorithm is ableto not only markedly enhance inversion precision but also render remarkably high correlationcoefficients associated with the elastic parameters.