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- Cooperative inversion (2)
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Collecting different geophysical data sets at the same object and site offers the opportunity to reduce uncertainties and ambiguities in data analysis and interpretation. To be effective, the different available data sets should be linked during the model-generation process, e.g. by cooperative inversion. In this study, we apply a recently developed zonal cooperative inversion approach based on fuzzy c-means cluster analysis to a non-destructive testing experiment. After briefly reviewing the fundamentals of the inversion strategy, we present a synthetic study investigating the potential of the method to detect air-filled voids in masonry by using ultrasonic and georadar traveltime data. Then, we present and discuss laboratory experiments including the results of cooperatively inverted ultrasonic and georadar traveltimes collected at a masonry test specimen. The geometry of the specimen is known and is thus an ideal test object for a first-time real application of the novel zonal cooperative inversion procedure. Compared to the results of separate inversions of ultrasonic and georadar traveltimes, the zonal cooperative inversion allows for an improved delineation of the size and position of the cavities. The P-wave and georadar velocities determined for the model regions corresponding to the cavities are also improved.
In this study, we use and extend a recently developed zonal cooperative inversion approach and apply it to the inversion of three independent geophysical field data sets. We invert crosshole P-wave, S-wave, and georadar data sets acquired in sand and gravel dominated unconsolidated sediments to detect and characterize different sedimentary units relevant for an engineering or hydrological site characterization. The zonal cooperative inversion of the three traveltime data sets results in a single subsurface model, which is a geophysical three-parameter model (P-wave, S-wave, and georadar velocity) outlining the major subsurface zonation while explaining all input data sets. Comparing our zonal model to direct push (DP) logging data (tip resistance, sleeve friction, and dielectric permittivity) shows good agreement; i.e., the zones in our geophysical model largely correspond to major DP parameter changes. Furthermore, we demonstrate how the sparse DP data can be inter- and extrapolated to the entire tomographic plane which allows for further geotechnical and hydrological interpretations. This study illustrates that the zonal cooperative inversion approach is highly flexible and an excellent tool to characterize a variety of environments in terms of multiple physical parameters.