Elastic parameter inversion problem based on brain storm optimization algorithm

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.