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A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

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Abstract

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

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Authors and Affiliations

  1. Vel Tech Dr. RR & Dr. SR Technical University, Avadi, Chennai, 600 062, India

    A Stanley Raj

  2. Centre for GeoTechnology, Manonmaniam Sundaranar University, Tirunelveli, 627 012, India

    Y SRINIVAS & D Hudson Oliver

  3. M.D.T. Hindu College, Tirunelveli, Tamil Nadu, India

    D Muthuraj

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  1. A Stanley Raj
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  2. Y SRINIVAS
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  3. D Hudson Oliver
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  4. D Muthuraj
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Correspondence to A Stanley Raj.

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Raj, A.S., SRINIVAS, Y., Oliver, D.H. et al. A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN). J Earth Syst Sci 123, 395–411 (2014). https://doi.org/10.1007/s12040-014-0402-7

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  • DOI: https://doi.org/10.1007/s12040-014-0402-7

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