@article{GuillemoteauLueckTronicke2017, author = {Guillemoteau, Julien and L{\"u}ck, Erika and Tronicke, Jens}, title = {1D inversion of direct current data acquired with a rolling electrode system}, series = {Journal of applied geophysics}, volume = {146}, journal = {Journal of applied geophysics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2017.09.010}, pages = {167 -- 177}, year = {2017}, abstract = {Direct current systems employing a kinematic surveying strategy allow to analyze the electrical resistivity of the subsurface for large areas (i.e., several hectares). Typical applications are found in precision agriculture, archaeological prospecting and soil sciences. With the typical survey setting, the collected data sets are often characterized by a rather high level of noise and a rather coarse lateral sampling compared to data acquired with fixed electrodes. We therefore present an efficient one-dimensional inversion approach in which we put special attention on modeling the effects of noise. We apply this method to data recorded with a five-offset equatorial dipole-dipole system employing rolling electrodes. By performing several synthetic tests with realistic noise levels, we found that the considered five-configuration soundings allow for a reliable imaging of two-layer cases in the uppermost two meters of the subsurface, where the subsurface can be assumed to follow a horizontally layered geometry within 3 m around the system. By analyzing the corresponding sensitivity functions, we also show that the equatorial dipole-dipole array is relatively well suited for a 1D inversion approach compared to standard in-line electrode arrays. To illustrate this aspect, we show that our method can provide results similar to those obtained with a 2D Wenner imaging procedure for data recorded across a well-constrained 2D target. We finally apply our method to a large five-offset data set acquired in an agricultural study. The final pseudo-3D model of electrical resistivity is in accordance with borehole data available for the surveyed area. Our results demonstrate the applicability and the versatility of the presented inversion approach for large-scale data sets as they are typically collected with such rolling electrode systems. (C) 2017 Elsevier B.V. All rights reserved.}, language = {en} } @article{GuillemoteauSimonLuecketal.2016, author = {Guillemoteau, Julien and Simon, Francois-Xavier and L{\"u}ck, Erika and Tronicke, Jens}, title = {1D sequential inversion of portable multi-configuration electromagnetic induction data}, series = {Near surface geophysics}, volume = {14}, journal = {Near surface geophysics}, publisher = {Wiley-VCH}, address = {Houten}, issn = {1569-4445}, doi = {10.3997/1873-0604.2016029}, pages = {423 -- 432}, year = {2016}, abstract = {We present an algorithm that performs sequentially one-dimensional inversion of subsurface magnetic permeability and electrical conductivity by using multi-configuration electromagnetic induction sensor data. The presented method is based on the conversion of the in-phase and out-of-phase data into effective magnetic permeability and electrical conductivity of the equivalent homogeneous half-space. In the case of small-offset systems, such as portable electromagnetic induction sensors, for which in-phase and out-of-phase data are moderately coupled, the effective half-space magnetic permeability and electrical conductivity can be inverted sequentially within an iterative scheme. We test and evaluate the proposed inversion strategy using synthetic and field examples. First, we apply it to synthetic data for some highly magnetic environments. Then, the method is tested on real field data acquired in a basaltic environment to image a formation of archaeological interest. These examples demonstrate that a joint interpretation of in-phase and out-of-phase data leads to a better characterisation of the subsurface in magnetic environments such as volcanic areas.}, language = {en} } @article{GuillemoteauSimonHulinetal.2019, author = {Guillemoteau, Julien and Simon, Francois-Xavier and Hulin, Guillaume and Dousteyssier, Bertrand and Dacko, Marion and Tronicke, Jens}, title = {3-D imaging of subsurface magnetic permeability/susceptibility with portable frequency domain electromagnetic sensors for near surface exploration}, series = {Geophysical journal international}, volume = {219}, journal = {Geophysical journal international}, number = {3}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0956-540X}, doi = {10.1093/gji/ggz382}, pages = {1773 -- 1785}, year = {2019}, abstract = {The in-phase response collected by portable loop-loop electromagnetic induction (EMI) sensors operating at low and moderate induction numbers (<= 1) is typically used for sensing the magnetic permeability (or susceptibility) of the subsurface. This is due to the fact that the in-phase response contains a small induction fraction and a preponderant induced magnetization fraction. The magnetization fraction follows the magneto-static equations similarly to the magnetic method but with an active magnetic source. The use of an active source offers the possibility to collect data with several loop-loop configurations, which illuminate the subsurface with different sensitivity patterns. Such multiconfiguration soundings thereby allows the imaging of subsurface magnetic permeability/susceptibility variations through an inversion procedure. This method is not affected by the remnant magnetization and theoretically overcomes the classical depth ambiguity generally encountered with passive geomagnetic data. To invert multiconfiguration in-phase data sets, we propose a novel methodology based on a full-grid 3-D multichannel deconvolution (MCD) procedure. This method allows us to invert large data sets (e.g. consisting of more than a hundred thousand of data points) for a dense voxel-based 3-D model of magnetic susceptibility subject to smoothness constraints. In this study, we first present and discuss synthetic examples of our imaging procedure, which aim at simulating realistic conditions. Finally, we demonstrate the applicability of our method to field data collected across an archaeological site in Auvergne (France) to image the foundations of a Gallo-Roman villa built with basalt rock material. Our synthetic and field data examples demonstrate the potential of the proposed inversion procedure offering new and complementary ways to interpret data sets collected with modern EMI instruments.}, language = {en} } @article{KoyanTronickeAllroggen2021, author = {Koyan, Philipp and Tronicke, Jens and Allroggen, Niklas}, title = {3D ground-penetrating radar attributes to generate classified facies models}, series = {Geophysics}, volume = {86}, journal = {Geophysics}, number = {6}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2021-0204.1}, pages = {B335 -- B347}, year = {2021}, abstract = {Ground-penetrating radar (GPR) is a standard geophysical technique used to image near-surface structures in sedimentary environments. In such environments, GPR data acquisition and processing are increasingly following 3D strategies. However, the processed GPR data volumes are typically still interpreted using selected 2D slices and manual concepts such as GPR facies analyses. In seismic volume interpretation, the application of (semi-)automated and reproducible approaches such as 3D attribute analyses as well as the production of attribute-based facies models are common practices today. In contrast, the field of 3D GPR attribute analyses and corresponding facies models is largely untapped. We have developed and applied a workflow to produce 3D attribute-based GPR facies models comprising the dominant sedimentary reflection patterns in a GPR volume, which images complex sandy structures on the dune island of Spiekeroog (Northern Germany). After presenting our field site and details regarding our data acquisition and processing, we calculate and filter 3D texture attributes to generate a database comprising the dominant texture features of our GPR data. Then, we perform a dimensionality reduction of this database to obtain meta texture attributes, which we analyze and integrate using composite imaging and (also considering additional geometric information) fuzzy c-means cluster analysis resulting in a classified GPR facies model. Considering our facies model and a corresponding GPR facies chart, we interpret our GPR data set in terms of near-surface sedimentary units, the corresponding depositional environments, and the recent formation history at our field site. Thus, we demonstrate the potential of our workflow, which represents a novel and clear strategy to perform a more objective and consistent interpretation of 3D GPR data collected across different sedimentary environments.}, language = {en} } @article{SchennenTronickeWetterichetal.2016, author = {Schennen, Stephan and Tronicke, Jens and Wetterich, Sebastian and Allroggen, Niklas and Schwamborn, Georg and Schirrmeister, Lutz}, title = {3D ground-penetrating radar imaging of ice complex deposits in northern East Siberia}, series = {Geophysics}, volume = {81}, journal = {Geophysics}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2015-0129.1}, pages = {WA195 -- WA202}, year = {2016}, abstract = {Ice complex deposits are characteristic, ice-rich formations in northern East Siberia and represent an important part in the arctic carbon pool. Recently, these late Quaternary deposits are the objective of numerous investigations typically relying on outcrop and borehole data. Many of these studies can benefit from a 3D structural model of the subsurface for upscaling their observations or for constraining estimations of inventories, such as the local carbon stock. We have addressed this problem of structural imaging by 3D ground-penetrating radar (GPR), which, in permafrost studies, has been primarily used for 2D profiling. We have used a 3D kinematic GPR surveying strategy at a field site located in the New Siberian Archipelago on top of an ice complex. After applying a 3D GPR processing sequence, we were able to trace two horizons at depths below 20 m. Taking available borehole and outcrop data into account, we have interpreted these two features as interfaces of major lithologic units and derived a 3D cryostratigraphic model of the subsurface. Our data example demonstrated that a 3D surveying and processing strategy was crucial at our field site and showed the potential of 3D GPR to image geologic structures in complex ice-rich permafrost landscapes.}, language = {en} } @article{KoyanTronicke2020, author = {Koyan, Philipp and Tronicke, Jens}, title = {3D modeling of ground-penetrating radar data across a realistic sedimentary model}, series = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology}, volume = {137}, journal = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0098-3004}, doi = {10.1016/j.cageo.2020.104422}, pages = {9}, year = {2020}, abstract = {Ground-penetrating radar (GPR) is an established geophysical tool to explore a wide range of near-surface environments. Today, the use of synthetic GPR data is largely limited to 2D because 3D modeling is computationally more expensive. In fact, only recent developments of modeling tools and powerful hardware allow for a time-efficient computation of extensive 3D data sets. Thus, 3D subsurface models and resulting GPR data sets, which are of great interest to develop and evaluate novel approaches in data analysis and interpretation, have not been made publicly available up to now.
We use a published hydrofacies data set of an aquifer-analog study within fluvio-glacial deposits to infer a realistic 3D porosity model showing heterogeneities at multiple spatial scales. Assuming fresh-water saturated sediments, we generate synthetic 3D GPR data across this model using novel GPU-acceleration included in the open-source software gprMax. We present a numerical approach to examine 3D wave-propagation effects in modeled GPR data. Using the results of this examination study, we conduct a spatial model decomposition to enable a computationally efficient 3D simulation of a typical GPR reflection data set across the entire model surface. We process the resulting GPR data set using a standard 3D structural imaging sequence and compare the results to selected input data to demonstrate the feasibility and potential of the presented modeling studies. We conclude on conceivable applications of our 3D GPR reflection data set and the underlying porosity model, which are both publicly available and, thus, can support future methodological developments in GPR and other near-surface geophysical techniques.}, 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{ArboledaZapataGuillemoteauTronicke2022, author = {Arboleda-Zapata, Mauricio and Guillemoteau, Julien and Tronicke, Jens}, title = {A comprehensive workflow to analyze ensembles of globally inverted 2D electrical resistivity models}, series = {Journal of applied geophysics}, volume = {196}, journal = {Journal of applied geophysics}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0926-9851}, doi = {10.1016/j.jappgeo.2021.104512}, pages = {12}, year = {2022}, abstract = {Electrical resistivity tomography (ERT) aims at imaging the subsurface resistivity distribution and provides valuable information for different geological, engineering, and hydrological applications. To obtain a subsurface resistivity model from measured apparent resistivities, stochastic or deterministic inversion procedures may be employed. Typically, the inversion of ERT data results in non-unique solutions; i.e., an ensemble of different models explains the measured data equally well. In this study, we perform inference analysis of model ensembles generated using a well-established global inversion approach to assess uncertainties related to the nonuniqueness of the inverse problem. Our interpretation strategy starts by establishing model selection criteria based on different statistical descriptors calculated from the data residuals. Then, we perform cluster analysis considering the inverted resistivity models and the corresponding data residuals. Finally, we evaluate model uncertainties and residual distributions for each cluster. To illustrate the potential of our approach, we use a particle swarm optimization (PSO) algorithm to obtain an ensemble of 2D layer-based resistivity models from a synthetic data example and a field data set collected in Loon-Plage, France. Our strategy performs well for both synthetic and field data and allows us to extract different plausible model scenarios with their associated uncertainties and data residual distributions. Although we demonstrate our workflow using 2D ERT data and a PSObased inversion approach, the proposed strategy is general and can be adapted to analyze model ensembles generated from other kinds of geophysical data and using different global inversion approaches.}, language = {en} } @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{AllroggenTronicke2016, author = {Allroggen, Niklas and Tronicke, Jens}, title = {Attribute-based analysis of time-lapse ground-penetrating radar data}, series = {Geophysics}, volume = {81}, journal = {Geophysics}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2015-0171.1}, pages = {H1 -- H8}, year = {2016}, abstract = {Analysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tested and evaluated our attributes using synthetic data of different complexity. Afterward, we applied them to a field data example, in which subsurface flow was induced by an artificial rainfall event. For all examples, we identified our structural similarity attribute to be a robust measure for highlighting time-lapse changes also in data with low signal-to-noise ratios. We determined that our new attribute-based workflow is a promising tool to analyze time-lapse GPR data, especially for imaging subsurface hydrological processes.}, language = {en} }