ANN based inversion of DC resistivity data for groundwater exploration in hard rock terrain of western Maharashtra (India)

Abstract

Inversion of vertical electrical sounding (VES) data, especially from the crystalline hard rock area, assumes a special significance for groundwater exploration. Here we used a newly developed algorithm based on the Bayesian neural network (BNN) theory combined with Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme to invert the Direct Current (DC) VES measurements obtained from 30-locations around Tenduli-Vengurla, Sindhudurg district, Maharashtra, India. The inversion results suggest that the top layer is mostly comprised of laterites followed by mixture of clay/clayey sand and garnulites/granite as basement rocks. The source of groundwater appears to be accessible in weathered/semi-weathered layer of laterite/clayey sand that exists within the depth of 10–15 m from the surface. The NW–SE trending major lineaments and its criss-crosses are also identified from the apparent and true resistivity surface map. The pseudo-section at different depths in the western part of the area, near Nivti, shows extensive influence of saltwater intrusion and its impact reaching up to the depth of 30 m from the surface along the coastal area. Our results also show that intrusion of saline water decreases from the western part to the eastern part of the region. Two dimensional modeling of four resistivity profiles from the study region identified two potential groundwater reservoirs; one lying between Path-Tenduli and another in between Mat and Zaraph. The deduced true electrical resistivity section against depth correlates well with available borehole lithology in the area. The results presented here would be useful for interpreting the geological signatures like fractures, major joints and lineaments, which in turn will be helpful for identifying groundwater reservoirs and drainage pattern in the crystalline hard rock area. The newly developed HMC-based BNN method is robust and would provide insights for constraining the geophysical models and criteria for modeling resistivity data.