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摘要
IP information extraction from CSAMT data contributes to better inversion results. The linear inversion method, which is commonly used in this aspect, has such problems as dependency on the initial model and easily falling into local minimum. Considering the nonlinearity and nonconvexity of extracting IP response, a shuffled frog leaping algorithm (SFLA) with inertia weight and Cauchy distribution is proposed, in the procedure of which an improved two-stage minimum structure inversion approach is adopted. Firstly, the Cauchy operator is used to substitute the random operator to enhance the global search ability of SFLA. And the inertia weight of chaotic oscillation is employed to balance the experience between individuals and groups in the evolutionary process for stable convergence. Secondly, a two-stage inversion strategy is applied to enhance the impact of polarizability in the inversion process. Meanwhile, to solve the multi-solution problem, the regularization factor is utilized to the fitness function of the SFLA. Thirdly, CPU parallel computing is employed to accelerate the local search process of memeplexes in the proposed method. The inversion results show that the proposed algorithm is capable of IP information extraction and geoelectric structure reconstruction, and is also robust in noisy environment. Compared with other nonlinear algorithms (such as SFLA, DE and PSO), the proposed algorithm has better global searching performance and higher computational efficiency, which is suitable for extraction of weak IP information.
- 出版日期2017-8
- 单位湖南师范大学; 湖南涉外经济学院
