Improvement to resistivity pseudosection modelling by removal of near-surface inhomogeneity effects: application to a soil system in south Cameroon

Near-surface inhomogeneities (NSIs) can lead to severe problems in the inte rpretation of apparent resistivity pseudosections because their effects sig nificantly complicate the image aspect. In order to carry out a more effici ent and reliable interpretation process, these problematic features should be removed from field data. We describe a filtering scheme using two-sided half-Schlumberger array data. The scheme was tested on synthetic data, gene rated from a simple 2D resistivity model contaminated by NSIs, and is shown to be suitable for eliminating such contaminations from apparent resistivi ty data. Furthermore, the original model without NSIs can be recovered sati sfactorily from the inversion of filtered apparent resistivity data. The al gorithm is also applied efficiently to a real data set collected at Nsimi, in southern Cameroon, along a 200-m shallow depth profile crossing a comple x transitional zone. For this case, the filtering scheme provides accurate structural and behavioural interpretations of both the geometry of the majo r soil constituents and the groundwater partitioning.