A robust learning method for low-frequency extrapolation in GPR full waveform inversion

Abstract

Full-waveform inversion (FWI) plays a significant role in producing high-resolution subsurface imaging in seismic prospecting and ground penetrating radar (GPR). However, FWI faces various challenges in practice. For example, the lack of low-frequency information due to acquisition limitations will make the FWI prone to falling to the local minimum. In this project, a deep learning-based approach is proposed to extrapolate the low-frequency data. Specifically, we propose a robust progressive learning (RPL) algorithm that combines physics-guided FWI and data-driven deep learning technology. The proposed method is robust against the choice of the initial model. Experimental results show that our method can achieve high efficiency and accuracy by using a limited amount of training data. The subsurface structures are successfully reconstructed with our extrapolated low-frequency data.