Magnetotelluric inversion based on BP neural network optimized by genetic algorithm

To improve nonlinear magnetotelluric (MT) inversion, this work introduces the genetic neural network algorithm. Firstly, a back propagation (BP) neural network frame is constructed for training in different models. The network inputs are the apparent resistivity values of known models, and the outputs are the model parameters. The reasonable network structure is designed by determining the number of network nodes. Secondly, the learning process of the neural network is optimized by using the genetic algorithm to obtain the optimal solution of network connection weights. Finally the trained genetic neural network is verified through inversion, in which the network inputs are the apparent resistivity values of unknown models, and the outputs are the corresponding model parameters. Experimental results show that the genetic neural network can make full use of the global searching capability of the genetic algorithm and the local optimization of the neural network. Compared with the single neural network inversion, the operation efficiency and calculation accuracy of the genetic neural network are improved significantly. By comparing the genetic neural network and least-squares regularization inversion, the tests on synthetic and real data show that this method can be applied to MT data inversion and achieve good results.