To enhance the 3D inversion accuracy of transient electromagnetic (TEM) prospecting, a TEM 3D visualization inversion method combining Particle Warm Optimization-Nonlinear Conjugate Gradient(PSO-NLCG) hybrid optimization method and adaptive regularization method is proposed. Based on establish the regularized inversion objective function with the L2 norm, an adaptive parameter adjustment strategy for the regularization objective function is proposed to optimize the regularization parameters and improve the stability of the inversion. To further ensure the stability of the inversion computation, a dynamic adjustment mechanism between data fitting and model smoothing is constructed, and the upper and lower bounds constraints and inversion termination conditions are formulated. To improve computation speed and inversion accuracy of 3D TEM inversion, based on the theory of gradient optimization algorithms, the global search capability of the particle swarm optimization (PSO) algorithm is combined with the refined exploration of the nonlinear conjugate gradient (NLCG) algorithm to form the PSO-NLCG hybrid optimization algorithm. The results of the comparative experiments show that, the proposed PSO-NLCG hybrid optimization and adaptive regularization algorithm for TEM 3D inversion can effectively recover the spatial distribution of underground anomalous target bodies, and the inversion accuracy is higher than that of the comparative method.
Jianqiang et al. (Thu,) studied this question.