Automated diffraction tracking and inversion for EM velocity estimation

We apply an automated diffraction tracking and inversion algorithm to oversampled GPR data sets, in order to assess the influence of time and space sampling on the resulting EM velocity model. The accuracy of such model depends on the actual presence and regular distribution of undistorted diffraction hyperbolas within the recorded CO profiles. Nevertheless, hyperbolic inversion techniques on CO data are the only alternative to amplitude inversion methods in absence of clearly defined reflections. Commonly used diffraction tracking methods include manual picking, automated hyperbola fitting, edge detection, and machine learning techniques. The presented auto-picking algorithm tracks potential diffractions by transforming the coordinates of the profile so that the hyperbolas are turned into straight lines. Through a linear fit in the transformed space, the algorithm is then able to recover the average EM velocity above the tracked diffractors. We apply the procedure on GPR data sets acquired on urban environments, observing a significant impact of both temporal and spatial sampling intervals on the resulting average EM velocities and uncertainty values.