A two-stage CO-PSO minimum structure inversion using CUDA for extracting IP information from MT data

The study of induced polarization (IP) information extraction from magnetotelluric (MT) sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows: 1) dependency on the initial model, 2) easily falling into the local minimum, and 3) serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture (CUDA) is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization (CO-PSO) algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.