An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem. (21st September 2018)

• Record Type: Journal Article
• Authors: Wu, Qinghua; Zhu, Zhixin; Yan, Xuesong; Gong, Wenyin
• Other Names: Mei Lin guestEditor.; Yan Zhiguo guestEditor.; Shao Jie guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-21
• Subjects: correlation coefficient -- elastic parameters inversion -- particle swarm optimization algorithm -- prestack seismic; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4987
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The elastic parameter inversion technique for prestack seismic data, which combines the intelligent optimization algorithms with Amplitude Variation with Offset (AVO) technology, is an effective method for oil and gas exploration. However, when certain biological‐evolution–based optimization algorithms, eg, genetic algorithms, are used to solve this problem, the computation exhibits 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 a swarm‐intelligence‐based method‐Particle Swarm Optimization (PSO) algorithm to handle the elastic parameter inversion problem. Based on the Aki‐Richards approximation to the Zoeppritz equations, the improved PSO algorithm adopts a special initialization strategy, which can enhance the smoothness of the initialization parametric curves. Extensive experimental research confirms the superiority of the proposed algorithm. Specifically, the improved PSO algorithm is able to not only markedly enhance inversion precision but also render remarkably high correlation coefficients associated with the elastic parameters.