@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}
}
@article{GuillemoteauSailhacBoulangeretal.2015,
  author    = {Guillemoteau, Julien and Sailhac, Pascal and Boulanger, Charles and Trules, Jeremie},
  title     = {Inversion of ground constant offset loop-loop electromagnetic data for a large range of induction numbers},
  series = {Geophysics},
  volume    = {80},
  journal   = {Geophysics},
  number    = {1},
  publisher = {Society of Exploration Geophysicists},
  address   = {Tulsa},
  issn      = {0016-8033},
  doi       = {10.1190/GEO2014-0005.1},
  pages     = {E11 -- E21},
  year      = {2015},
  abstract  = {Ground loop-loop electromagnetic surveys are often conducted to fulfill the low-induction-number condition. To image the distribution of electric conductivity inside the ground, it is then necessary to collect a multioffset data set. We considered that less time-consuming constant offset measurements can also reach this objective. This can be achieved by performing multifrequency soundings, which are commonly performed for the airborne electromagnetic method. Ground multifrequency soundings have to be interpreted carefully because they contain high-induction-number data. These data are interpreted in two steps. First, the in-phase and out-of-phase data are converted into robust apparent conductivities valid for all the induction numbers. Second, the apparent conductivity data are inverted in 1D and 2D to obtain the true distribution of the ground conductivity. For the inversion, we used a general half-space Jacobian for the apparent conductivity valid for all the induction numbers. This method was applied and validated on synthetic data computed with the full Maxwell theory. The method was then applied on field data acquired in the test site of Provins, in the Parisian basin, France. The result revealed good agreement with borehole and geologic information, demonstrating the applicability of our method.},
  language  = {en}
}