Zonal Cooperative Inversion of Partially Co-located Crosshole Tomographic P- and S-wave Traveltime Data Sets under Automated Incorporation of a Priori Information

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 reliability of the obtained model parameters, the information content of all data sets should be considered during the tomographic model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the recently developed zonal cooperative inversion methodology based on fuzzy c-means cluster analysis and conventional single-input data set inversion algorithms for the cooperative inversion of two crosshole tomographic traveltime data sets with partly co-located model areas. This is done by considering recently published modifications made to the 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. The only requirement is that the additional information considered can be expressed numerically, e.g., by physical parameters or indicator variables. The approach results in a single zoned multi-parameter model, which is consistent with all available geoscientific information and outlines the major subsurface units. Additionally, physical parameter models underlying the zoned multi-parameter model are obtained for each of the input data sets.