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

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第9期31卷

作者： Wu, Qinghua Zhu, Zhixin Yan, Xuesong Gong, Wenyin Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

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.

关键词： correlation coefficient elastic parameters inversion particle swarm optimization algorithm prestack seismic

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Cauchy prior distribution-based AVO elastic parameter estimation via weakly nonlinear waveform inversion

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Applied Geophysics 2013年第4期10卷 442-452,511,512页

作者：麻纪强耿建华同济大学海洋地质国家重点实验室上海200092

Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.

关键词： Cauchy priori distribution AVO elastic parameters inversion weakly nonlinear waveform inversion

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