Multiparameter Full-waveform inversion of on-ground GPR using Memoryless quasi-Newton (MLQN) method

Abstract

Full waveform inversion (FWI) of ground penetrating radar (GPR) is a promising imaging tool for the detailed characterization of underground targets. In this study, on-ground GPR FWI is used to construct permittivity and conductivity variations of underground targets simultaneously. We applied memoryless quasi-Newton (MLQN) method to solve inverse problem of GPR. MLQN can attain acceptable results with low computational cost and small memory storage requirements. Numerical test is examined from on-ground multi-offset GPR data and the results show that our inversion strategies are feasible and reliable in simultaneous inversion of permittivity and conductivity from on-ground GPR data.