TY - JOUR A1 - Klose, Tim A1 - Guillemoteau, Julien A1 - Vignoli, Giulio A1 - Walter, Judith A1 - Herrmann, Andreas A1 - Tronicke, Jens T1 - Structurally constrained inversion by means of a Minimum Gradient Support regularizer BT - examples of FD-EMI data inversion constrained by GPR reflection data JF - Geophysical journal international N2 - Many geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a prioriinformation is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally constrained smooth inversion as well as to more standard (i.e., not structurally constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach. KW - Controlled source electromagnetics (CSEM) KW - Inverse theory KW - Electrical properties KW - Ground penetrating radar KW - Frequency Domain Electromagnetics KW - Inversion Y1 - 2023 U6 - https://doi.org/10.1093/gji/ggad041 SN - 0956-540X SN - 1365-246X VL - 233 IS - 3 SP - 1938 EP - 1949 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Allroggen, Niklas A1 - van Schaik, N. Loes M. B. A1 - Tronicke, Jens T1 - 4D ground-penetrating radar during a plot scale dye tracer experiment JF - Journal of applied geophysics N2 - Flow phenomena in the unsaturated zone are highly variable in time and space. Thus, it is challenging to measure and monitor such processes under field conditions. Here, we present a new setup and interpretation approach for combining a dye tracer experiment with a 4D ground-penetrating radar (GPR) survey. Therefore, we designed a rainfall experiment during which we measured three surface-based 3D GPR surveys using a pair of 500 MHz antennas. Such a survey setup requires accurate acquisition and processing techniquesto extract time-lapse information supporting the interpretation of selected cross-sections photographed after excavating the site. Our results reveal patterns of traveltime changes in the measured GPR data, which are associated with soil moisture changes. As distinct horizons are present at our site, such changes can be quantified and transferred into changes in total soil moisture content. Our soil moisture estimates are similar to the amount of infiltrated water, which confirms our experimental approach and makes us confident for further developing this strategy, especially, with respect to improving the temporal and spatial resolution. (C) 2015 Elsevier B.V. All rights reserved. KW - Ground penetrating radar KW - Time-lapse imaging KW - Brilliant blue Y1 - 2015 U6 - https://doi.org/10.1016/j.jappgeo.2015.04.016 SN - 0926-9851 SN - 1879-1859 VL - 118 SP - 139 EP - 144 PB - Elsevier CY - Amsterdam ER - 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 -