Neural Network Based Automated Interpretation Algorithm for Combined Geophysical Soundings in Coastal Zones

  • Rambhatla G Sastry
  • Haile G Tesfakiros


The fresh water availability in coastal aquifers is an important problem faced by a major part of world’s population dwelling there. For in situ and dynamic characterization of seawater encroachment into coastal aquifers, electrical geophysical methods are better suited. However, the resolving power of such data in distinguishing saline sands from moist clays in the subsurface is very poor. To meet this aspect and also the problem of analyzing voluminous data sets, we propose a feed forward back-propagation neural network (BPNN) based approach for the analysis of combined vertical electrical and induced polarization soundings. Our method is tested on synthetic data computed from available geo-electric sections and prevailing subsurface geological information of coastal aquifers of East Coast of India.

The synthetic data are comprised of 18 combined soundings spread over five profiles. 15 out of 18 are used for training the BPNN, while 3 are used for testing. The trained BPNN (one node each in each of the input and output layers and 18 hidden nodes) showed 84.85% accuracy in testing phase for distinguishing clays from saline sands.

Our method is also tested on real data concerning a shaly aquifer in Bahia, Brazil yielding an overall accuracy of 84.9%, comparable to that of synthetic case; thereby validating our approach.


ANN application coastal aquifers geo-electric surveys 


  1. Abrahart, R. J. and Kneale, P. E.: 1997, ‘Exploring Neural Network Rainfall-Runoff Modelling’, in: Proceedings Sixth National Hydrology Symposium, University of Salford, 15–18 September 1997, Wallingford: British Hydrological Society, 935–944.Google Scholar
  2. Abrahart, R. J. and See, L.: 1988, ‘Neural Network vs. ARMA Modelling: Constructing Benchmark Case Studies of River Flow Prediction’, In: Geocomputation 98, Proceedings Third International Conference on Geocomputation, University of Bristol, 17–19 September 1998, Manchester: Geocomputation CD-ROM.Google Scholar
  3. Aminzadeh, F., Barhen, J., Glover, C. W. and Toomarian, N. B.: 2000, ‘Reservoir parameter estimation using hybrid neural network,’ Computers Geosci. 26, 869–875.CrossRefGoogle Scholar
  4. An, P., Moon, W. M.: 1993, Reservoir Characterization Using Feed-Forward Neural Networks, 63rd Annual Internat. Mtg., Soc. Expl. Geophy.Google Scholar
  5. Anupama Sharma: 1997, Numerical Modelling of Seawater Transport in Coastal Aquifers, Ph.D Thesis, University of Roorkee (India).Google Scholar
  6. Arora, C. L. Bose, R. N.: 1981, ‘Demarcation of fresh and saline water zones using electrical methods (Abhor area, Ferozpur District, Punjab)’, J. Hydrology 49, 75–86.CrossRefGoogle Scholar
  7. Baan, M. V. D. and Jutten, C.: 2000, ‘Neural networks in geophysical applications’, Geophys. 65, 1032–1047.CrossRefGoogle Scholar
  8. Boadu, F.: 1998, ‘Inversion of fracture density from seismic velocities using ANN’, Geophys. 63, 534–545.CrossRefGoogle Scholar
  9. Brown, M. and Poulton, M.: 1996, ‘Locating buried objects for environmental site investigations using neural networks’, J. Environ. Eng. Geophy. 1(3), 179–188.CrossRefGoogle Scholar
  10. Buffenmyer, V., Poulton, V. and Johnson, R.: 2000, ‘Identification of seismic crew noise in marine surveys by neural networks’, The Leading Edge 19(4), 370–377.CrossRefGoogle Scholar
  11. Carpenter, G. A. and Crossberg, S.: 1987, ‘A massively parallel architecture for a self-organizing neural pattern recognition machine,’ Comp. Vision, Graphics Image Processing 37, 54–115.CrossRefGoogle Scholar
  12. Chen, H. C. and Fang, J. H.: 1993, ‘A new method for prospect appraisal’, AAPG Bull. 77, 8–9.Google Scholar
  13. Chitra, S. P.: 1993, Use of Neural Networks for Problem Solving, Chemical Engineering Press, Hercules Inc.Google Scholar
  14. Coppola, E., Szidarovszky, F., Poulton, M. and Charles, E.: 2003, A computational neural network approach for predicting transient water levels in a multi-layer groundwater systems under variable state pumping and climate conditions, ASCE J. Hydrol. Eng.Google Scholar
  15. Custodio, E.(ed.) 1987, Ground Water Problem in Coastal Areas, UNESCO Publication, Belgium.Google Scholar
  16. Dai, H. C. and Mac Bath, C.: 1997, ‘Application of back-propagation neural networks to identification of seismic arrival types’, Phys. Earth Planet. Int. 101, 177–188.CrossRefGoogle Scholar
  17. Dai, H. C. and Mac Bath, C.: 1995, ‘Automatic picking of seismic arrivals in local earthquake data using an artificial neural network’, Geophys. J. Inter. 120, 758–774.Google Scholar
  18. Dai, H. C. and Mac Bath, C.: 1997, ‘The application of back-propagation neural network to automatic pick seismic arrivals from single-component recordings’, J. Geophys. Res. 102(B7), 15105–15113.CrossRefGoogle Scholar
  19. Dystart, P. S. and Pulli, J. J.: 1990, ‘Regional seismic event classification at the NORESS array: Seismological measurements and the use of trained neural network’, Bull. Seism. Soc. America 80, 1910–1933.Google Scholar
  20. Eberhart, R. G. and Dobbins, R. W.: 1990, Neural Network PC Tools, Academic Press, New York, 1990.Google Scholar
  21. Elshorbagy, A. and Simonovic, S. P.: 2000, ‘Performance evaluation of artificial neural networks for runoff prediction’, J. Hydrol. Eng. 5(4), 424–427.CrossRefGoogle Scholar
  22. Feng, X. T., Sato, M. and Katsuyama, K.: 1997, ‘Neural dynamic modelling on earthquake magnitude series, Geophy’, J. Int. 128, 547–556.Google Scholar
  23. Finnie, G. J.: 1999, ‘Using neural networks to discriminate between genuine and spurious seismic events in mines’, Pure Appl. Geophys. 154(1) 41–56.CrossRefGoogle Scholar
  24. Fischer, M. M., Gopal, S., Staufer, P. and Steinocher, K.: 1994, ‘Evaluation of neural pattern classifiers for a remote sensing application. Paper presented at the 34th European Congress of the Regional Science Association, Groningen, August 1994’, Geographical Systems 4(2) 195–223 and 235–236.Google Scholar
  25. French, M. N., Kajeewski, W. F. and Cuykendall, R. R.: 1991, ‘Rainfall forecasting in space and time using a neural network’, J. Hydrology 37, 1–31.Google Scholar
  26. Fu, L.: 1994, Neural Networks in Computer Intelligence, N.Y, Mc Graw-Hill, pp. 460.Google Scholar
  27. Gupta Sarma, D.: 1982, ‘Optimization of short digital linear filters for increased accuracy’, Geophys. Prospecting 30, 501–514.CrossRefGoogle Scholar
  28. Hsu, K. L, Gupta, H. V. and Sorooshian, S.: 1995, ‘Artificial neural network modelling of the rainfall-runoff process’, Water Resources Res. 31, 2517–2530.CrossRefGoogle Scholar
  29. Huang, Z., Shimeld, J., Williamson, M. and Katsube, J.: 1996, ‘Permeability prediction with artificial neural network modelling in the Venture gas field’, Offshore Eastern Canada, Geophys. 61 422–436.Google Scholar
  30. Ibrahim, Y., Al-lsmaili and Warner, M. R.: 2002, Nonlinear cross-equalization of time-lapse seismic surveys using artificial neural networks, EAAGE 64th Conference & Exhibition, Italy 27–30, May.Google Scholar
  31. Johnston, D. H.: 1993, ‘Seismic Attribute Calibration Using Neural Networks’, 63rd Annual Internat. Mtg., Soc. Expl. Geophy., Extended Abstracts, 93, 250–253.Google Scholar
  32. Koefoed, O.: 1979, Geosounding Principles 1, Amsterdam, Elsevier,Google Scholar
  33. Lima, O. A. L. D. and Niwas, S.: 2000, ‘Estimation of hydraulic parameters of shaly sandstone aquifers from geoelectrical measurements’, J. Hydrology 235, 12–26.CrossRefGoogle Scholar
  34. Lingireddy, S.: 1998, ‘Aquifer parameter estimation using genetic algorithms and neural networks’, Civil Eng. Env. Eng. Systems 15, 125–144.Google Scholar
  35. McCormack, M. D.: 1991, ‘Neural computing in geophysics’, Geophysics: The Leading Edge of Exploration 10, 11–15.CrossRefGoogle Scholar
  36. Murat, M. and Rudman, A.: 1992, ‘Automated first arrival picking: A neural network approach’, Geophys. Prosp. 40, 587–604.CrossRefGoogle Scholar
  37. McCormack, M. D., Zaucha, D. E. and Dushek, D. W.: 1993, ‘First- break refraction event picking and seismic data trace editing using neural network’, Geophys. 58, 67–78.CrossRefGoogle Scholar
  38. Ogilvy, A. A. and Kuzmina, E. N.: 1972, ‘Hydro geologic and engineering geologic possibilities for employing the method of induced potentials’, Geophys. 37(5), 839–861.CrossRefGoogle Scholar
  39. Oldenburg, D. W. and Li, Y.: 1994, ‘Inversion of induced polarization data’, Geophys. 59(9), 1327–1341.CrossRefGoogle Scholar
  40. Openshaw, S. and Openshaw, C.: 1997, Artificial Intelligence in Geography, Chichester, Wiley.Google Scholar
  41. Ouenes, A.: 2000, ‘Practical application of fuzzy logic and neural networks to fractured reservoir characterization’, Comp. Geosci. 26, 953–962.CrossRefGoogle Scholar
  42. Patra, H. P.: 1967, ‘A note on the possibility of saline water invasion around the Jaldha Coast’, West Bengal (India), Geoexplor. 5, 89–94.Google Scholar
  43. Patella, D.: 1972, ‘An interpretation theory for induced polarization vertical soundings (Time-domain)’, Geophys. Prosp. 20, 561–579.CrossRefGoogle Scholar
  44. Patella, D.: 1973, ‘A new parameter for the interpretation of induced polarization field prospecting (Time-domain)’, Geophys. Prosp. 21, 315–329.CrossRefGoogle Scholar
  45. Paul, J. and Goidammer, P.: 1990, Neural Net Applications in Medicine. In: J. Stender and T. Addis (eds.), Symbols versus neurons?, Washington D.C, IOS Press, 215–231.Google Scholar
  46. Pezeshk, S., Camp, C. V. and Karprapv, S.: 1996, ‘Geophysical log interpretation using neural network’, J. Comp. Civil Eng. 10(2), 136–142.CrossRefGoogle Scholar
  47. Poulton, M. M., Sternberg, B. K. and Glass, C. E.: 1992, ‘Location of subsurface targets in geophysical data using neural networks’, Geophys. 57, 1534–1544.CrossRefGoogle Scholar
  48. Prasad, G. D. V.: 1991, Geophysical, Hydro Chemical and Sedimentological Characteristics of the Coastal Environments of Western Krishna Delta, Ph.D Thesis, Andhra University (India), pp. 178.Google Scholar
  49. Prasad, P. R. and Ohse, W.: 1983, Geochemical and Geophysical Studies of Salt Water Intrusion in Coastal Regions, Proceedings of Hamburg Symposium, Hamburg.Google Scholar
  50. Poulton, M.: 2002, ‘Neural networks as an intelligence amplification tool: A review of applications’, Geophy. 67(3), 979–993.CrossRefGoogle Scholar
  51. Rao, K.: 1989, Holocene Sediments of Western Krishna Delta, East Coast of India, Ph.D Thesis, Andhra University (India).Google Scholar
  52. Richards, J.: 1993, Remote Sensing Digital Image Analysis, an Introduction, Springer-Verlag, NY.Google Scholar
  53. Rizzo, D. M. and Dougherty, D. E.: 1994, ‘Characterization of aquifer properties using artificial neural networks: neural kriging’, Water Resources Res. 30, 483–497.CrossRefGoogle Scholar
  54. Rogers, L. L. and Dowla, F. U.: 1994, ‘Optimisation of groundwater remediation using ANN with parallel solute transport modelling’, Water Resources Res. 30(2), 457–481.CrossRefGoogle Scholar
  55. Rogers, S. J., Fang, J. H., Karr, C. L. and Stanley, D. A.: 1992, ‘Determination of lithology, from well logs using a neural network’, AAPG Bull. 76, 731–739.Google Scholar
  56. Roth, G. and Tarantola, A.: 1994, ‘Neural networks and inversion of seismic data’, J. Geophys. Res. 99(B4), 6753–6768.CrossRefGoogle Scholar
  57. Roy, K. K. and Elliot, H. M.: 1980, ‘Resistivity and IP survey for delineating saline and fresh water zones’, Geoexpl. 8, 145–162.CrossRefGoogle Scholar
  58. Schaap, M. G. and Bouten, W.: 1996, ‘Modelling water retention curves of sandy soils using neural networks’, Water Resources Res. 32(10), 3033–3040.CrossRefGoogle Scholar
  59. Schalkoff, R. J.: 1997, Artificial Neural Networks, New York, Mc Graw-Hill Book Co.Google Scholar
  60. Smith, J. and Eli, R. N.: 1995, ‘Neural-network models of rainfall-runoff process’, J. Water Resources Planning Manage. 121, 499–509.CrossRefGoogle Scholar
  61. Telford, W. M., Geldart, L. P., Sheriff, R. E. and Keys, D. A.: 1988, Applied Geophysics, New- Delhi, Oxford & IBM Publishing Co. Pvt. Ltd.Google Scholar
  62. Tesfakiros, H. G.: 1998. Resistivity and IP Modelling of Coastal Aquifer Problems, Unpublished M.Tech Thesis, University of Roorkee (India), pp. 86.Google Scholar
  63. Wong, P. M., Jiang, F. X. and Taggart, I. J.: 1995, ‘A critical comparison of neural networks and discriminant analysis in lithofacies, porosity and permeability predictions’, J. Petro. Geol. 18, 191–206.Google Scholar
  64. Wong, P. M., Gedeon, T. D. and Taggart, I. J.: 1995, ‘An improved technique in prediction: A neural network approach’, IEEE Trans. Geosci. Remote Sens. 33, 971–980.CrossRefGoogle Scholar
  65. Zhang, L, Poulton, M. and Wang, T.: 2002, ‘Borehole electrical resistivity modelling using neural networks’, Geophys. 67(6), 1779–1789.CrossRefGoogle Scholar
  66. Zohdy, A. A. R.: 1989, ‘A new method for the automatic interpretation of Schlumberger and Wenner sounding curves’, Geophys. 54(2), 245–253.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Earth ScienceI.I.TRoorkeeIndia
  2. 2.Geophysicist, Phoenix Geophysics Ltd.TorontoCanada

Personalised recommendations