GPR noise reduction by TVD and TVDEMD

The existence of coherent and incoherent (random) noises including highfrequency electromagnetic inferences in the Ground Penetration Radar (GPR) signals is inevitable. Therefore, the elimination of noise from GPR data before performing any additional analysis is of great importance to increase the accuracy of the interpretations. We apply the Total Variation Denoising (TVD) and SavitzkyGolay (SG) filter on synthetic and real GPR datasets. For a better perception, the same trace of the data is compared after applying the mentioned methods. The results indicate that the TVD method is more effective than the common adaptive filtering in the time domain for reducing noise such as the SG filter which acts as a lowpass filter for smoothing data based on a polynomial leastsquares approximation. However, due to the visibility of staircase artifacts using the TVD method, GPR data is first transferred to the Empirical Mode Decomposition (EMD) frame which is useful for nonlinear and nonstationary signal processing, and then the TVD method is applied to it. Finally, noise reduction using TVD is compared in the time and EMD domains. The comparison of the outputs shows that the TVD algorithm in the EMD domain, based on the sequential extraction of the energy belonging to the different intrinsic time scales of the signal, provides better noise attenuation than the other algorithms. In addition, TVDEMD improves the continuity in sections and preserves the event forms and signal forms.