Abstract. Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M = 6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC) vertical electrical sounding (VES) data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD)-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural deCorrespondence to: S. Maiti (saumen maiti2002@yahoo.co.in) tails. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results are quite useful for the interpretation of fractures and lineaments in seismically active region.

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Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb's diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: "Very good", "Good", and "Unsuitable". The BNN model-based results suggest that groundwater quality falls mostly in the range of "Good" to "Very good" except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.

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Ground magnetic data collected at an average data spacing of 5 km over the Deccan Traps in Maharashtra, India are studied. In conjunction with a magnetic anomaly map generated from ground, aero and marine magnetic data, seven lineaments are identified below the Deccan Traps: six NW–SE-trending lineaments (Ln1 to Ln6) and a NE–SW-oriented lineament, Ln7. From the filtered Bouguer gravity data the sources at different depths are studied. From combined analysis we find that Ln1 coincides with the continuation of Bababudan Nallur Shear zone. The boundary between the Western and Eastern Dharwar cratons, the Chitradurg Boundary Shear, merges with Ln2. Ln3 is part of the Peninsular Lineament identified earlier. Ln4 coincides in part with the Bhima River, while Ln5 and Ln6 are possibly related to Godavari rifting. The region between Ln4 and Ln5 relates to the Kurudwadi Lineament Zone. The Kaladgi and Bhima sediments and the schist belts continue northwards below the Deccan Traps and are constrained to lie to the south of NE–SW-oriented Ln7. With the exception of Ln3 (the Peninsular Lineament) the other six lineaments below the Deccan Traps are identified for the first time. The crustal structure below the Deccan Traps that we derive from 2D modelling of combined gravity and magnetic data supports the presence of the identified lineaments.

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