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In many near-surface geophysical studies it is now common practice to collect co-located disparate geophysical data sets to explore subsurface structures. Reconstruction of physical parameter distributions underlying the available geophysical data sets usually requires the use of tomographic reconstruction techniques. To improve the quality of the obtained models, the information content of all data sets should be considered during the model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the zonal cooperative inversion methodology based on fuzzy c-means cluster analysis and conventional single-input data set inversion algorithms for the cooperative inversion of data sets with partially co-located model areas. This is done by considering recent developments in fuzzy c-means cluster analysis. Additionally, we show how supplementary a priori information can be incorporated in an automated fashion into the zonal cooperative inversion approach to further constrain the inversion. The only requirement is that this a priori information can be expressed numerically; e.g., by physical parameters or indicator variables. We demonstrate the applicability of the modified zonal cooperative inversion approach using synthetic and field data examples. In these examples, we cooperatively invert S- and P-wave traveltime data sets with partially co-located model areas using water saturation information expressed by indicator variables as additional a priori information. The approach results in a zoned multi-parameter model, which is consistent with all available information given to the zonal cooperative inversion and outlines the major subsurface units. In our field example, we further compare the obtained zonal model to sparsely available borehole and direct-push logs. This comparison provides further confidence in our zonal cooperative inversion model because the borehole and direct-push logs indicate a similar zonation.
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.