Göran Hamann, Jens Tronicke, Colby M. Steelman, Anthony L. Endres

• In many hydrological applications, ground-wave velocity measurements are increasingly used to map and monitor shallow soil water content. In this study, we propose an automated spectral velocity analysis method to determine the direct ground-wave (DGW) velocity from common midpoint (CMP) or multi-offset ground-penetrating radar (GPR) data. The method introduced in this paper is a variation of the well-known spectral velocity analysis for seismic and GPR reflection events where velocity spectra are computed using different coherency measures along hyperbolas following the normal moveout model. Here, the unnormalized cross-correlation is computed between waveforms across data gathers that are corrected with a linear moveout equation using a predefined range of velocities. Peaks in the resulting velocity spectra identify linear events in the GPR data gathers like DGW events and allow for estimating the corresponding velocities. In addition to obtaining a DGW velocity measurement, we propose a robust method to estimate the associatedIn many hydrological applications, ground-wave velocity measurements are increasingly used to map and monitor shallow soil water content. In this study, we propose an automated spectral velocity analysis method to determine the direct ground-wave (DGW) velocity from common midpoint (CMP) or multi-offset ground-penetrating radar (GPR) data. The method introduced in this paper is a variation of the well-known spectral velocity analysis for seismic and GPR reflection events where velocity spectra are computed using different coherency measures along hyperbolas following the normal moveout model. Here, the unnormalized cross-correlation is computed between waveforms across data gathers that are corrected with a linear moveout equation using a predefined range of velocities. Peaks in the resulting velocity spectra identify linear events in the GPR data gathers like DGW events and allow for estimating the corresponding velocities. In addition to obtaining a DGW velocity measurement, we propose a robust method to estimate the associated velocity uncertainties based on the width of the peak in the calculated velocity spectrum. Our proposed method is tested on synthetic data examples to evaluate the influence of subsurface velocity, surveying geometry and signal frequency on the accuracy of estimated ground-wave velocities. In addition, we investigate the influence of such velocity uncertainties on subsequent soil water content estimates using an established petrophysical relationship. Furthermore, we apply our approach to analyse field data, which were collected across a test site in Canada to monitor a wide range of seasonal soil moisture variations. A comparison between our spectral velocity estimates and results derived from manually picked ground-wave arrivals shows good agreement, which illustrates that our spectral velocity analysis is a feasible tool to analyse DGW arrivals in multi-offset GPR data gathers in an objective and more automated manner.…