@article{SauerPoppDittfurthetal.2013,
  author    = {Sauer, David and Popp, Steffen and Dittfurth, Angela and Altdorff, Daniel and Dietrich, Peter and Paasche, Hendrik},
  title     = {Soil moisture assessment over an alpine hillslope with significant soil heterogeneity},
  series = {Vadose zone journal},
  volume    = {12},
  journal   = {Vadose zone journal},
  number    = {4},
  publisher = {Soil Science Society of America},
  address   = {Madison},
  issn      = {1539-1663},
  doi       = {10.2136/vzj2013.01.0009},
  pages     = {12},
  year      = {2013},
  abstract  = {We strive to assess soil water content on a well-studied slow-moving hillslope in Austria. In doing so, we employ time lapse mapping of bulk electrical conductivity using a geophysical electromagnetic induction system operated at low induction numbers. This information is complemented by the acquisition of soil samples for gravimetric water content analysis during one survey campaign. Simple visual soil sample analysis reveals that the upper material in the survey area is a spatially highly variable mixture of predominately sandy, silty, clayey and organic materials. Due to this heterogeneity, classical approaches of mapping soil moisture on the basis of stationary mapping of electrical conductivity variations are not successful. Also the time-lapse approach does not allow ruling out some of the ambiguity inherent to the linkage of bulk electrical conductivity to soil water content. However, indication is found that time-lapse measurements may have supportive capabilities to identify regions of low precipitation infiltration due to high soil saturation. Furthermore, the relationship between the mean electrical conductivity averaged over a full vegetation period and an already available ecological moisture map produced by vegetation analysis is found to resemble closely the relationship observed between gravimetric soil water content and electrical conductivity during the time of sample collection except for highly organic soils. This leads us to the assumption that the relative soil moisture distribution is temporarily stable except for those areas characterized by highly organic soils.},
  language  = {en}
}
@article{HachmoellerPaasche2013,
  author    = {Hachm{\"o}ller, Barbara and Paasche, Hendrik},
  title     = {Integration of surface-based tomographic models for zonation and multimodel guided extrapolation of sparsely known petrophysical parameters},
  series = {Geophysics},
  volume    = {78},
  journal   = {Geophysics},
  number    = {4},
  publisher = {Society of Exploration Geophysicists},
  address   = {Tulsa},
  issn      = {0016-8033},
  doi       = {10.1190/GEO2012-0417.1},
  pages     = {EN43 -- EN53},
  year      = {2013},
  abstract  = {We integrate the information of multiple tomographic models acquired from the earth's surface by modifying a statistical approach recently developed for the integration of cross-borehole tomographic models. In doing so, we introduce spectral cluster analysis as the new core of the model integration procedure to capture the spatial heterogeneity present in all considered tomographic models and describe this heterogeneity in a fuzzy sense. Because spectral cluster algorithms analyze model structure locally, they are considered relatively robust with regard to systematically and spatially varying imaging capabilities typical for geophysical tomographic surveys conducted on the earth's surface. Using a synthetic aquifer example, a fuzzy spectral cluster algorithm can be used to integrate the information provided by 2D tomographic refraction seismic and DC resistivity surveys. The integrated information in the fuzzy membership domain is then used to derive an integrated zonal geophysical model outlining the major structural units present in both input models. We also explain how the fuzzy membership information can be used to identify optimal locations for sparse logging of additional target parameters, i.e., porosity information in our synthetic example. We demonstrate how this sparse porosity information can be extrapolated based on all tomographic input models. The resultant 2D porosity model matches the original porosity distribution reasonably well within the spatial resolution limits of the underlying tomographic models. Consecutively, we apply this approach to a field data base acquired over a former river channel. Sparse information about natural gamma radiation is available and extrapolated on the basis of the fuzzy membership information obtained by spectral cluster analysis of 2D P-wave velocity and electrical resistivity models. This field data shows that the presented parameter extrapolation procedure is robust, even if the locations of target parameter acquisition have not been optimized with regard to the fuzzy membership information.},
  language  = {en}
}