Automated reflection picking and polarity assessment through attribute analysis: Theory and application to synthetic and real ground-penetrating radar data

We have developed a procedure to automatically detect, pick, and characterize reflections in either seismic or ground-penetrating radar (GPR) data sets. Accurate picking results are essential in many research and application fields and are mandatory to recover the underground stratigraphy and reflectivity and, therefore, to create a more constrained subsurface model. We use the cosine of the instantaneous phase to track events with lateral phase continuity and record them in terms of time-space positions, amplitude, and polarity. Moreover, we use the cosine phase to reconstruct the shape of the reflected wavelets by averaging it along the picked horizons, to recover the initial phase of each reflection and, therefore, their polarities. By comparing the polarities of the transmitted and reflected wavelets, we can recover the impedance contrasts in the subsurface and evaluate the properties of the materials. We applied the picking and polarity assessment algorithms to a synthetic data set. We inverted the picked amplitudes and traveltimes to test the accuracy of the procedure by comparing the calculated stratigraphy and velocity distribution with the initial model. The inverted results were consistent with the original data, with most discordances in the stratigraphy within a few tens of centimeters, using a wavelet with a central frequency equal to 300 MHz. Local larger discrepancies were probably caused by interferences altering the amplitudes and resulting in the overestimation of the impedance contrasts. We also applied automatic picking and phase assessment procedures to real glaciological and archaeological GPR surveys, in which the influence of noise and diffractions was critically evaluated by comparing the picked results obtained from raw and processed data.