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

Abstract

As one of the most active nonlinear inversion methods in transient electromagnetic (TEM) inversion, the back propagation (BP) neural network has high efficiency because the complicated forward model calculation is unnecessary in iteration. The global optimization ability of the particle swarm optimization (PSO) is adopted for amending the BP's sensitivity to its initial parameters, which avoids it falling into a local optimum. A chaotic-oscillation inertia weight PSO (COPSO) is proposed for accelerating convergence. The COPSO-BP algorithm performance is validated by two typical testing functions, two geoelectric models inversions and a field example. The results show that the COPSO-BP method is more accurate, stable and needs relatively less training time. The proposed algorithm has a higher fitting degree for the data inversion, and it is feasible to use it in geophysical inverse applications.