A residual fully convolutional network (Res-FCN) for electromagnetic inversion of high contrast scatterers at an arbitrary frequency within a wide frequency band

Abstract

Many successful machine learning methods have been developed for electromagnetic inverse scattering problems. However, so far, their inversion has been performed only at the specifically trained frequencies. To make the machine learning based inversion method more generalizable for realistic engineering applications, this work proposes a residual fully convolutional network (Res-FCN) to perform EM inversion of high contrast scatterers at an arbitrary frequency within a wide frequency band. The proposed Res-FCN combines the advantages of the Res-Net and the fully convolutional network (FCN). Res-FCN consists of an encoder and a decoder: the encoder is employed to extract high-dimensional features from the measured scattered field through the residual frameworks, while the decoder is employed to map from the high-dimensional features extracted by the encoder to the electrical parameter distribution in the inversion region by the up-sample layer and the residual frameworks. Four numerical examples verify that the proposed Res-FCN can achieve good performance in the 2-D EM inversion problem for high contrast scatterers with anti-noise ability at an arbitrary frequency point within a wide frequency band.