Spectral velocity analysis for the determination of ground‐wave velocities and their uncertainties in multi‐offset GPR data

Abstract

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 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.

Preliminary results on multi offset GPR for imaging of landmines

“…Both object detection and identification can be significantly enhanced through the use of the additional degrees of freedom provided by bistatic geometries [22], dimension which increases the measured information content [23]. The multi-offset method, in which the transmitter and the receiver are independently managed, is a well-rehearsed technique developed by the seismic community with the aim of improving Signal to Noise Ratio (SNR) and the accuracy of velocity estimation [24] [25]. The expected potential of using bistatic solutions is to yield lighter weight, lower cost systems and improved detection, identification and coverage performance.…”

Global inversion of GPR traveltimes to assess uncertainties in CMP velocity models

“…Although the assumptions inherent in NMO based velocity analyses are avoided by this approach, we have to consider the limitations of applying such a linearized inversion strategy to the non‐linear problem of reflected traveltime inversion (e.g., Sen and Stoffa ). For example, when using local optimization approaches the influence of the starting model has to be considered because the final solution obtained by such an approach may critically depend on the initial model (Menke ; Aster et al ).…”

“…We conclude that our methodology is a feasible and powerful tool to analyse GPR CMP data and allows practitioners and researchers to evaluate the reliability of CMP derived velocity models. content or porosity (e.g., Greaves et al 1996;van Overmeeren et al 1997;Huisman et al 2003;Tronicke et al 2004;Steelman and Endres 2012;Hamann et al 2013).To determine a GPR velocity model, different surveying approaches can be employed. Cross-hole tomography (e.g., Binley et al 2001;Tronicke et al 2002) and vertical radar profiling (e.g., Cassiani et al 2004;Tronicke and Knoll 2005) can provide detailed information regarding subsurface velocity variations.…”

“…We conclude that our methodology is a feasible and powerful tool to analyse GPR CMP data and allows practitioners and researchers to evaluate the reliability of CMP derived velocity models. content or porosity (e.g., Greaves et al 1996;van Overmeeren et al 1997;Huisman et al 2003;Tronicke et al 2004;Steelman and Endres 2012;Hamann et al 2013).…”

3D ground-penetrating radar imaging of ice complex deposits in northern East Siberia

“…major primary events that were used to delineate our velocity model. Using visual inspection and linear-moveout-based analysis (Hamann et al, 2013), we estimate the velocities of the ground wave and the wave critically refracted at the interface between the active layer and the frozen ground. We only analyze these linear events because 3D effects (out-of-plane reflections and diffractions) and local topographic variations hinder a typical 1D normal-moveout-based analysis of reflected events.…”