Singh, U. K; Tiwari, R K.; Singh, S B; Rajan, S. (2006) Prediction of Electrical Resistivity Structures Using Artificial Neural Networks. Journal of the Geological Society of India, 67 (2). 234-242 doi:10.17491/jgsi/2006/670212
| Reference Type | Journal (article/letter/editorial) | ||
|---|---|---|---|
| Title | Prediction of Electrical Resistivity Structures Using Artificial Neural Networks | ||
| Journal | Journal of the Geological Society of India | ||
| Authors | Singh, U. K | Author | |
| Tiwari, R K. | Author | ||
| Singh, S B | Author | ||
| Rajan, S. | Author | ||
| Year | 2006 (February 1) | Volume | 67 |
| Page(s) | 234-242 | Issue | 2 |
| Publisher | Geological Society of India | Place | Bangaluru, India |
| URL | |||
| DOI | doi:10.17491/jgsi/2006/670212Search in ResearchGate | ||
| Generate Citation Formats | |||
| Classification | Not set | LoC | Not set |
| Mindat Ref. ID | 19415264 | Long-form Identifier | mindat:1:5:19415264:0 |
| GUID | 0 | ||
| Full Reference | Singh, U. K; Tiwari, R K.; Singh, S B; Rajan, S. (2006) Prediction of Electrical Resistivity Structures Using Artificial Neural Networks. Journal of the Geological Society of India, 67 (2). 234-242 doi:10.17491/jgsi/2006/670212 | ||
| Plain Text | Singh, U. K; Tiwari, R K.; Singh, S B; Rajan, S. (2006) Prediction of Electrical Resistivity Structures Using Artificial Neural Networks. Journal of the Geological Society of India, 67 (2). 234-242 doi:10.17491/jgsi/2006/670212 | ||
| In | (2006, February) Journal of the Geological Society of India Vol. 67 (2). Geological Society of India | ||
| Abstract/Notes | Abstract The artificial neural network (ANN) technique is at present most efficient and modern tool for parameter estimation and inversion of geophysical data This paper deals with the application of ANN technique for the inversion of vertical electrical resistivity sounding (VES) data obtained from the NNW SSE part of Barmer district, Rajasthan The efficiency of ANN technique is tested first on synthetic resistivity data generated from the numerical model and then trained on the actual VES field data The analyses predict sediment thickness of the order of 172 m at Rawtra (S 15), and indicate that there is possibility of fresh aquifeis at all sounding locations along the profile except at Sonadi (S 1) These results match with the depth-resistivity structure obtained by the conventional method However, the high accuracy and faster ANN imaging system seems to have highly correlated with that of conventional method for mapping the complex subsurface resistivity structures with less ambiguity These finding also correlate remarkably well with known drilling results and geologic boundaries | ||
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