@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{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{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}
}