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. As one of the most active nonlinear inversion methods in transientelectromagnetic (TEM) inversion, the back propagation (BP) neural networkhas high efficiency because the complicated forward model calculation isunnecessary in iteration. The global optimization ability of the particleswarm optimization (PSO) is adopted for amending the BP's sensitivity to its initialparameters, which avoids it falling into a local optimum. A chaotic-oscillation inertia weight PSO (COPSO) is proposed for acceleratingconvergence. The COPSO-BP algorithm performance is validated by two typicaltesting functions, two geoelectric models inversions and a fieldexample. The results show that the COPSO-BP method is more accurate, stable and needs relatively less training time. The proposed algorithm has ahigher fitting degree for the data inversion, and it is feasible to use it ingeophysical inverse applications.

nonlinear processes in geophysics

A fast approximation for 1-D inversion of transient electromagnetic data by using a back propagation neural network and improved particle swarm optimization