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Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach

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Advances in Modeling and Interpretation in Near Surface Geophysics

Part of the book series: Springer Geophysics ((SPRINGERGEOPHYS))

Abstract

In this work, a Bayesian neural network modeling technique optimized by Hybrid-Monte Carlo (HMC-BNN) was employed to interpret DC vertical electrical sounding (VES) data. Survey was carried out at 27-locations of Sagareshwar-Redi-Malewad region, Sindhudurg district, Maharashtra, India. Before doing the real data analysis, the algorithm was tested on synthetic data which is perturbed by diverse level of chaotic noise to find the range of optimal network parameters. The present technique is different from the previous technique in the sense that it aims to find which hyper parameters and their ranges enable successful recovery of layer parameters from noise (chaotic) intervened VES data. The modeling results indicate that the proposed scheme is capable to cope up with the intervention of chaotic noise with the data. The data-driven inversion results were interpreted using available litho-logical information to characterize the hydro-geological sections. The interpreted hydro-geological sections suggest that top layer is mostly composed of laterites/fractured laterites while the second layer appears to be dominated with mixture of clay/clayey sand. The basement is composed of garnulites/fractured garnulites and groundwater does strike at the boundary between overburden and bedrock. The empirical relations established by regression analyses between the resistivity of the earth and resistivity of the earth and total dissolved solids (TDS) suggest that the resistivity of the earth is appeared to be controlled by both TDS and soil resistivity in the region. These results would provide useful guidelines for exploration of groundwater resources and its management practices in the hard rock area.

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Acknowledgements

We thank directors IIT(ISM), Dhanbad and IIG, New Panvel for giving permission to publish the work. SM is grateful to Ministry of Earth Sciences (MoES), Govt. of India, New Delhi, India, for partial financial support through the Grant No. MoES/P.O. (Geosci)/44/2015.

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Authors and Affiliations

  1. Department of Applied Geophysics, IIT (ISM), Dhanbad, India

    Saumen Maiti

  2. Indian Institute of Geomagnetism, Mumbai, India

    Gautam Gupta

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  1. Saumen Maiti
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  2. Gautam Gupta
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Correspondence to Saumen Maiti .

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Editors and Affiliations

  1. Department of Geology, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Arkoprovo Biswas

  2. Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Shashi Prakash Sharma

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Maiti, S., Gupta, G. (2020). Integrated Geoelectrical and Hydrochemical Investigation of Shallow Aquifers in Konkan Coastal Area, Maharashtra, India: Advanced Artificial Neural Networks Based Simulation Approach. In: Biswas, A., Sharma, S. (eds) Advances in Modeling and Interpretation in Near Surface Geophysics. Springer Geophysics. Springer, Cham. https://doi.org/10.1007/978-3-030-28909-6_3

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