Application of sparse representation of ground penetrating radar data in a study of extracting rock fracture signature

Due to complex subsurface situation, echo signals surveyed with Ground Penetration Radar (GPR) often contain a lot of clutters, including direct-coupling wave, random noises and multiples. Existence of these clutters submerges measured feature signals of rock structures with GPR, so suppression of them is often essential conduct for rock feature extraction. For extracting rockmass structure features from surveyed GPR data signals, sparse representation (SR) of the signals is an invaluable scheme with a small number of elementary signals from over-complete dictionary. In processing GPR signal data for extraction of rock structure and fracture features, this paper investigates sole Curvelet transform or matching pursuit (MP) for directcoupling wave and clutter suppression and feature extraction, and analyzes their limitations. By modeling ground penetrating radar signals with sparse decomposition, the method can achieve better results. Experimental results with simulation as well as real field data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for geological interpretation.