Characterization of the shear zone with pole–pole electrical resistivity tomography (ERT) was carried out to explore deep groundwater potential zone in a water scarce granitic area. As existing field conditions does not always allow to plant the remote electrodes at sufficiently far of distance, the effect of insufficient distance of remote electrodes on apparent resistivity measurement was studied and shown that the transverse pole–pole array affects less compared to the collinear pole–pole array. Correction factor have been computed for transverse pole–pole array for various positions of the remote electrodes. The above results helped in exploring deep aquifer site, where a 270 m deep well was drilled. Temporal hydro-chemical samples collected during the pumping indicated the hydraulic connectivity between the demarcated groundwater potential fractures. Incorporating all the information derived from different investigations, a subsurface model was synthetically simulated and generated 2D electrical resistivity response for different arrays and compared with the field responses to further validate the geoelectrical response of deep aquifer set-up associated with lineament.

The inverse modelling technique seeks to improve the existing estimates of natural recharge in hard rocks by coupling multiple hydrogeophysical parameters that jointly affect natural hydrogeological processes. This approach involves coupling of an initial set of multiple hydrogeophysical (soil resistivity, bedrockdepth and rainfall) parameters in the form of exponents assigned to each parameter and a multiplication coefficient to obtain natural recharge. These model parameters (i.e., exponents and coefficients) are then quantified using linear least squares inversion against the known recharge values. To reduce the effect of geomorphic heterogeneity, viz., hills on natural recharge, laterally constrained inversion has been employed to integrate data sets (e.g., recharge measured at various points and logical expectation over exposed hills in an area) and constrained interpolation is then carried out along the grid lines for increaseddata density. Finally, Kriging interpolation over dense data obtained through data integration and constrained interpolation is used to significantly minimise the risks of overshooting the observations. Thus, the present approach provides a realistic spatial distribution of natural recharge values in a highly heterogeneous hard rock terrain.