Efficient source-independent full waveform dual-parameter inversion of common-offset GPR data

Abstract

Full waveform inversion (FWI) of common-offset ground penetrating radar (GPR) data can make up for the defects of the traditional prospecting technology such as destructive, cumbersome, and low efficiency, which also provides a robust and high-precision tool for quantitative assessment of subsurface prospecting. Nonetheless, conventional GPR-FWI is of limited practicality since the deficiency of accurate source wavelet and tremendous computational cost. We develop an efficient source-independent inversion algorithm based on the convolution method to eliminate the influence of source wavelet and enhance the algorithm efficiency to inverse field data. To suppress the noises induced by the convolution and cross-correlation operations, the time window is introduced in the reference trace which is selected automatically by singular value decomposition (SVD). The stochastic strategy reduces the computational cost by source subsampling. The synthetic model of a complex lens body demonstrates that source-independent FWI with time window has more tolerant to the initial model and noise, and the stochastic strategy improves the efficiency with ensured inversion accuracy. In the field GPR data of a sand trough containing two anomalous bodies, our algorithm has good adaptability and can accurately reconstruct the distribution of the permittivity and conductivity of the subsurface media without accurate estimation of the source wavelet. The results above indicate that the proposed algorithm in this study is valid for common-offset GPR data and improves the inversion efficiency by 1.24 to 3.10 times, which shows great potential to promote qualitative assessment in real-time subsurface exploration.