@article{RumpfTronicke2015, author = {Rumpf, Michael and Tronicke, Jens}, title = {Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy}, series = {Geophysical prospecting}, volume = {63}, journal = {Geophysical prospecting}, number = {5}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0016-8025}, doi = {10.1111/1365-2478.12240}, pages = {1188 -- 1197}, year = {2015}, abstract = {To analyse and invert refraction seismic travel time data, different approaches and techniques have been proposed. One common approach is to invert first-break travel times employing local optimization approaches. However, these approaches result in a single velocity model, and it is difficult to assess the quality and to quantify uncertainties and non-uniqueness of the found solution. To address these problems, we propose an inversion strategy relying on a global optimization approach known as particle swarm optimization. With this approach we generate an ensemble of acceptable velocity models, i.e., models explaining our data equally well. We test and evaluate our approach using synthetic seismic travel times and field data collected across a creeping hillslope in the Austrian Alps. Our synthetic study mimics a layered near-surface environment, including a sharp velocity increase with depth and complex refractor topography. Analysing the generated ensemble of acceptable solutions using different statistical measures demonstrates that our inversion strategy is able to reconstruct the input velocity model, including reasonable, quantitative estimates of uncertainty. Our field data set is inverted, employing the same strategy, and we further compare our results with the velocity model obtained by a standard local optimization approach and the information from a nearby borehole. This comparison shows that both inversion strategies result in geologically reasonable models (in agreement with the borehole information). However, analysing the model variability of the ensemble generated using our global approach indicates that the result of the local optimization approach is part of this model ensemble. Our results show the benefit of employing a global inversion strategy to generate near-surface velocity models from refraction seismic data sets, especially in cases where no detailed a priori information regarding subsurface structures and velocity variations is available.}, language = {en} } @article{GuillemoteauTronicke2015, author = {Guillemoteau, Julien and Tronicke, Jens}, title = {Non-standard electromagnetic induction sensor configurations: Evaluating sensitivities and applicability}, series = {Journal of applied geophysics}, volume = {118}, journal = {Journal of applied geophysics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2015.04.008}, pages = {15 -- 23}, year = {2015}, abstract = {For near surface geophysical surveys, small-fixed offset loop-loop electromagnetic induction (EMI) sensors are usually placed parallel to the ground surface (i.e., both loops are at the same height above ground). In this study, we evaluate the potential of making measurements with a system that is not parallel to the ground; i.e., by positioning the system at different inclinations with respect to ground surface. First, we present the Maxwell theory for inclined magnetic dipoles over a homogeneous half space. By analyzing the sensitivities of such configurations, we,show that varying the angle of the system would result in improved imaging capabilities. For example, we show that acquiring data with a vertical system allows detection of a conductive body with a better lateral resolution compared to data acquired using standard horizontal configurations. The synthetic responses are presented for a heterogeneous medium and compared to field data acquired in the historical Park Sanssouci in Potsdam, Germany. After presenting a detailed sensitivity analysis and synthetic examples of such ground conductivity measurements, we suggest a new strategy of acquisition that allows to better estimate the true distribution of electrical conductivity using instruments with a fixed, small offset between the loops. This strategy is evaluated using field data collected at a well-constrained test-site in Horstwalde (Germany). Here, the target buried utility pipes are best imaged using vertical system configurations demonstrating the potential of our approach for typical applications. (C) 2015 Elsevier B.V. Pill rights reserved.}, language = {en} } @article{AllroggenvanSchaikTronicke2015, author = {Allroggen, Niklas and van Schaik, N. Loes M. B. and Tronicke, Jens}, title = {4D ground-penetrating radar during a plot scale dye tracer experiment}, series = {Journal of applied geophysics}, volume = {118}, journal = {Journal of applied geophysics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2015.04.016}, pages = {139 -- 144}, year = {2015}, abstract = {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.}, language = {en} } @article{TronickeAllroggen2015, author = {Tronicke, Jens and Allroggen, Niklas}, title = {Toward automated delineation of ground-penetrating radar facies in clastic sediments: An example from stratified glaciofluvial deposits}, series = {Geophysics}, volume = {80}, journal = {Geophysics}, number = {4}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2015-0188.1}, pages = {A89 -- A94}, year = {2015}, abstract = {Ground-penetrating radar (GPR) is an established geophysical method to explore near-surface sedimentary environments. Interpreting GPR images is largely based on manual procedures following concepts known as GPR facies analysis. We have developed a novel strategy to distinguish GPR facies in a largely automated and more objective manner. First, we calculate 13 textural attributes to quantify GPR reflection characteristics. Then, this database is reduced using principal component analysis. Finally, we image the dominating principal components using composite imaging and classify them using standard clustering methods. The potential of this work-flow is evaluated using a 2D GPR field example collected across stratified glaciofluvial deposits. Our results demonstrate that the derived facies images are well correlated with the composition and the porosity of the sediments as known from independent borehole logs. Our analysis strategy eases and improves the interpretability of GPR data and will help in a variety of geologic and hydrological problems.}, language = {en} } @article{AllroggenTronickeDelocketal.2015, author = {Allroggen, Niklas and Tronicke, Jens and Delock, Marcel and B{\"o}niger, Urs}, title = {Topographic migration of 2D and 3D ground-penetrating radar data considering variable velocities}, series = {Near surface geophysics}, volume = {13}, journal = {Near surface geophysics}, number = {3}, publisher = {European Association of Geoscientists \& Engineers}, address = {Houten}, issn = {1569-4445}, doi = {10.3997/1873-0604.2014037}, pages = {4}, year = {2015}, abstract = {We present a 2D/3D topographic migration scheme for ground-penetrating radar (GPR) data which is able to account for variable velocities by using the root mean square (rms) velocity approximation. We test our migration scheme using a synthetic 2D example and compare our migrated image to the results obtained using common GPR migration approaches. Furthermore, we apply it to 2D and 3D field data. These examples are recorded across common subsurface settings including surface topography and variations in the GPR subsurface velocity field caused by a shallow ground water table. In such field settings, our migration strategy provides well focused images of commonoffset GPR data without the need for a detailed interval velocity model. The synthetic and field examples demonstrate that our topographic migration scheme allows for accurate GPR imaging in the presence of variations in surface topography and subsurface velocity.}, language = {en} } @article{TronickeBoeniger2015, author = {Tronicke, Jens and Boeniger, Urs}, title = {Denoising magnetic data using steering kernel regression}, series = {Near surface geophysics}, volume = {13}, journal = {Near surface geophysics}, number = {1}, publisher = {European Association of Geoscientists \& Engineers}, address = {Houten}, issn = {1569-4445}, doi = {10.3997/1873-0604.2014038}, pages = {33 -- 44}, year = {2015}, abstract = {Ground-based magnetic surveying is a common geophysical method to explore near-surface environments in a non-destructive manner. In many typical applications (such as archaeological prospection), the resulting anomaly maps are often characterized by low signal-to-noise ratios and, thus, the suppression of noise is a key step in data processing. Here, we propose the steering kernel regression (SKR) method to denoise magnetic data sets. SKR has been recently developed to suppress random noise in images and video sequences. The core of the method is the steering kernel function which represents a robust estimate of local image structure. Using such a kernel within an iterative regression based denoising framework, helps to minimize image blurring and to preserve the underlying structures such as edges and corners. Because such filter characteristics are desirable for random noise attenuation in potential field data sets, we apply the SKR method for processing high-resolution ground-based magnetic data as they are typically collected in archaeological applications. We test and evaluate the SKR method using synthetic and field data examples and also compare it to more commonly employed denoising strategies relying, for example, on fixed filter masks (e.g., Gaussian filters). Our results show that the SKR method is successful in removing random and acquisition related noise present in our data. Concurrently, it preserves the local image structure including the amplitudes of anomalies. As demonstrated by derivative based transformations, the mentioned filter characteristics significantly impact subsequent processing steps and, therefore, result in an improved analysis and interpretation of magnetic data. Thus, the method can be considered as a promising and novel approach for denoising ground-based magnetic data.}, language = {en} }