A Hybrid Forward-Inverse Neural Network With the Transceiver-Configuration-Independent Technique for the Wideband Electromagnetic Inverse Scattering Problem

In this work, a hybrid forward-inverse neural network (HFINN) with a transceiver transformation module (TTM) is proposed to increase the generalizability of machine learning-based electromagnetic (EM) inversion methods. The HFINN consists of two parts: a forward module and an inverse module. The inverse module combines the Res-Net with a fully convolutional network (FCN), which is called “Res-FCN,” to show the performance in wideband inversion; however, the data misfit from Res-FCN often remains high because it only minimizes the model misfit; thus, a forward module based on a classical neural network, U-Net, is trained first to alleviate the problem of large data misfit. To train HFINN better, a new loss function is proposed so that the frequency information is used as a prior physical constraint to optimize HFINN. Meanwhile, to further improve the generalizability of HFINN, TTM is incorporated into the HFINN as physical assistance so that it does not need to be retrained for different transceiver configurations. A total of 4000 random test samples are employed to verify the performance of the proposed HFINN, and the average model misfit is 27.15%. Six numerical examples are also provided to verify the inversion performance of HFINN over the whole frequency band. After adding the forward module, the average data misfit of the result is reduced by 7.5%. The numerical results show that HFINN performs well across the whole frequency band, even when the testing transceiver configuration is different from the training transceiver configuration.