IP extraction from magnetotelluric sounding data based on adaptive differential evolution inversion
Dong Li1,2, Jiang Feibo1,3, Li Diquan1
1. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China;
2. College of Information Science and Engineering, Hunan International Economics University, Changsha, Hunan 410205, China;
3. College of Physics and Information Science, Hunan Normal University, Changsha, Hunan 410081, China
Abstract:Induced polarization (IP) information extraction from magnetotelluric (MT) sounding data is of great significance of earth deep structure and hydrocarbon exploration. Taking the nonlinearity and non convexity of MT IP extraction into consideration, a two-stage adaptive differential evolution (DE) inversion based on non-uniform statistical distribution and minimum structure is proposed by improving the adaptive strategy of evolutionary parameter in the chaotic DE algorithm. On the one hand, the statistical properties of Cauchy and Gaussian distribution are used to obtain the evolutionary parameter F and CR adaptively, which improves the global searching ability, and the successful evolutionary parameters in the previous iterative process are recorded to enhance the stability in later stage of the algorithm. On the other hand, the impact of the polarizability on observation data is strengthened by introducing the second stage inversion process, and the regularization parameter is applied in the fitness function of DE algorithm to solve the problem of multi-solutions in inversion. Inversion results of MT 1D model with IP effect show that geoelectric structure can be reconstructed and IP information can be well extracted. And the proposed algorithm is fairly robust in noise environment. Compared with inversion results of other nonlinear methods such as chaotic differential evolution (CDE), DE and particle swarm optimization (PSO), the proposed algorithm has better global searching ability and higher inversion accuracy, which is suitable for the weak IP extraction from MT signal.
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