A Tailored Semiphysics-Driven Artificial Neural Network for Electromagnetic Full-Wave Inversion

This article presents a tailored artificial neural network (ANN) that is designed for electromagnetic (EM) full-wave inversion (FWI). The ANN includes two subnets. The first subnet is a physics-driven fully connected neural network in which the known sensitivity matrix of EM FWI is assigned to the network model parameters. The second subnet is the traditional data-driven convolutional neural network (CNN) U-Net. The role of the first subnet is to convert the scattered EM fields recorded at the receiver array into the preliminary dielectric images of the scatterers with the aid of the fast calculation of the graphics processing unit. Then, the second subnet is used to further refine the scatterer dielectric parameters. In the off-line training, the network parameters in the first subnet are frozen, and only the parameters in the second subnet are optimized. In the online prediction, the whole ANN is used to directly invert the dielectric parameters of the scatterers. The proposed semiphysics-driven ANN is compared with the purely data-driven ANN for the inversion accuracy, training cost, generalization ability, antinoise ability, and so on.