The pre-stack Amplitude Variation with Offset (AVO) elastic parameter inversion technique combined with an intelligent optimization algorithm provides a more effective identification method for oil and gas exploration. However, biological evolution-based optimization algorithms, such as genetic algorithm, generally suffer problems such as premature convergence and high probability of becoming trapped in a local optimum, and these problems lead to unsatisfactory inversion results. To solve the above problems, this paper proposes a swarm-intelligence-based brain storm optimization algorithm, which is more suitable for solving the inversion problem of pre-stack AVO elastic parameters. The algorithm employs a specific initialization strategy for Aki and Rechard’s approximation equation, which is used in the inversion process, to produce a smoother initialization parameter curve. Multiple experiments prove that the correlation coefficients of the elastic parameters obtained by inversion are high, while the inversion accuracy is improved significantly.

中文翻译：

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基于头脑风暴优化算法的弹性参数反演问题

叠前偏移量变幅（AVO）弹性参数反演技术与智能优化算法相结合，为油气勘探提供了更有效的识别方法。但是，基于生物进化的优化算法，例如遗传算法，通常会遇到诸如早熟收敛和陷入局部最优的高概率之类的问题，并且这些问题导致反演结果不令人满意。针对上述问题，本文提出了一种基于群体智能的头脑风暴优化算法，该算法更适合解决叠前AVO弹性参数反演问题。该算法针对Aki和Rechard的近似方程采用了特定的初始化策略，该方程用于反演过程中，产生更平滑的初始化参数曲线。多次实验证明，反演得到的弹性参数的相关系数较高，反演精度有较大提高。