Abstract

Interpretation of vertical electrical sounding (VES) data is crucial for groundwater exploration. The study area occupies crystalline hard rock of Pre-Cambrian hornblende and feldspathic gneisses, schists, granulites, quartzites, metabasics and pegmatities. We develop here a novel algorithm based on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte Carlo (HMC)/ Markov Chain Monte Carlo (MCMC) simulation scheme for direct inversion of direct current (DC) VES measurements from the 30-locations around the Tenduli- Vengurla, Sindhudurg district, coastal 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 water strikes weathered/semi-weathered layer of laterite/clayey sand within the depth of 10-15m from the surface. Two dimension inversion via the HMC-BNN method of three resistivity profiles data from the study region demarcate two potential groundwater reservoirs; one lying between Path-Tenduli and another in between Math and Zaraph. The inverted true electrical resistivity section against depth correlates well with available borehole lithology in the area. The results presented here are useful for interpreting the geological signatures like fractures, major joints and lineaments, which in turn, will be useful for identifying groundwater reservoirs and drainage pattern in the crystalline hard rock area.