TY - JOUR A1 - Schmelzbach, C. A1 - Scherbaum, Frank A1 - Tronicke, Jens A1 - Dietrich, P. T1 - Bayesian frequency-domain blind deconvolution of ground-penetrating radar data JF - Journal of applied geophysics N2 - Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing. KW - Deconvolution KW - Inverse filtering KW - Ground penetrating radar KW - GPR KW - Data processing KW - Vertical resolution Y1 - 2011 U6 - https://doi.org/10.1016/j.jappgeo.2011.08.010 SN - 0926-9851 VL - 75 IS - 4 SP - 615 EP - 630 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmelzbach, C. A1 - Tronicke, Jens A1 - Dietrich, P. T1 - High-resolution water content estimation from surface-based ground-penetrating radar reflection data by impedance inversion JF - Water resources research N2 - Mapping hydrological parameter distributions in high resolution is essential to understand and simulate groundwater flow and contaminant transport. Of particular interest is surface-based ground-penetrating radar (GPR) reflection imaging in electrically resistive sediments because of the expected close link between the subsurface water content and the dielectric permittivity, which controls GPR wave velocity and reflectivity. Conventional tools like common midpoint (CMP) velocity analysis provide physical parameter models of limited resolution only. We present a novel reflection amplitude inversion workflow for surface-based GPR data capable of resolving the subsurface dielectric permittivity and related water content distribution with markedly improved resolution. Our scheme is an adaptation of a seismic reflection impedance inversion scheme to surface-based GPR data. Key is relative-amplitude-preserving data preconditioning including GPR deconvolution, which results in traces with the source-wavelet distortions and propagation effects largely removed. The subsequent inversion for the underlying dielectric permittivity and water content structure is constrained by in situ dielectric permittivity data obtained by direct-push logging. After demonstrating the potential of our novel scheme on a realistic synthetic data set, we apply it to two 2-D 100 MHz GPR profiles acquired over a shallow sedimentary aquifer resulting in water content images of the shallow (3-7 m depth) saturated zone having decimeter resolution. Y1 - 2012 U6 - https://doi.org/10.1029/2012WR011955 SN - 0043-1397 VL - 48 IS - 31 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Schmelzbach, C. A1 - Tronicke, Jens A1 - Dietrich, P. T1 - Three-dimensional hydrostratigraphic models from ground-penetrating radar and direct-push data JF - Journal of hydrology N2 - Three-dimensional models of hydraulic conductivity and porosity are essential to understand and simulate groundwater flow in heterogeneous geological environments. However, considering the inherent limitations of traditional hydrogeological field methods in terms of resolution, alternative field approaches are needed to establish such 3-D models with sufficient accuracy. In this study, we developed a workflow combining 3-D structural information extracted from ground penetrating radar (GPR) images with 1-D in situ physical-property estimates from direct-push (DP) logging to construct a 3-D hydrostratigraphic model. To illustrate this workflow, we collected an similar to 70 m x 90 m 100 MHz 3-D GPR data set over a shallow sedimentary aquifer system resolving six different GPR facies down to similar to 15 m depth. DP logs of the relative dielectric permittivity, the relative hydraulic conductivity, the cone resistance, the sleeve friction and the pore pressure provided crucial data (1) to establish a GPR velocity model for 3-D depth migration and to check the time-to-depth conversion of the GPR data, and (2) to construct a 3-D hydrostratigraphic model. This model was built by assigning porosity values, which were computed from the DP relative dielectric permittivity logs, and DP relative hydraulic conductivity estimates to the identified GPR facies. We conclude that the integration of 3-D GPR structural images and 1-D DP logs of target physical parameters provides an efficient way for detailed 3-D subsurface characterization as needed, for example, for groundwater flow simulations. KW - Ground-penetrating radar KW - Direct push KW - Hydrostratigraphic model KW - Three-dimensional KW - Aquifer KW - Saturated zone Y1 - 2011 U6 - https://doi.org/10.1016/j.jhydrol.2010.12.023 SN - 0022-1694 SN - 1879-2707 VL - 398 IS - 3-4 SP - 235 EP - 245 PB - Elsevier CY - Amsterdam ER -