1887

Abstract

Summary

Self-Potential (SP) fields are natural fields that originate from various forcing mechanisms related to electrical, hydraulic, chemical and thermal gradients. Due to the complexity of the source mechanisms, inversion of SP data is not easy and motivates the development of suitable techniques depending on application field, which ranges from engineering and geotechnical investigations to geothermal and mineral explorations. In this work, quantitative interpretations of self-potential data are given when SP anomaly sources can be modelled by simple polarized bodies whose parameters have to be determined. In particular, a comparative analysis is performed for the solutions of three different methods based on high-resolution spectral analysis, tomographic approach and global optimization, respectively. The efficiency of each technique has been tested by finding depth, polarization angle and shape factor of the anomaly source on synthetic data generated by simple geometrical structures (like sphere, horizontal and vertical cylinder and inclined sheet) and on field examples. The study shows limits and potentialities of the investigated methods and suggests hybrid algorithms as suitable tools for an accurate and full characterization of the anomaly source.

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/content/papers/10.3997/2214-4609.201600560
2016-05-30
2024-04-19
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References

  1. Abdelazeem, M. and Gobashy, M.
    [2006] Self-potential inversion using genetic algorithm, JKAU: Earth Science, 17, 83–101.
    [Google Scholar]
  2. Abdelrahman, E.M., El-Araby, H.M., Hassaneen, A.G. and Hafez, M.A.
    [2003] New methods for shape and depth determinations from SP data, Geophysics, 68, 1202–1210.
    [Google Scholar]
  3. Agarwal, B.N.P. and Srivastava, S.
    [2009] Analyses of self-potential anomalies by conventional and extended Euler deconvolution techniques, Computers & Geosciences, 35, 2231–2238.
    [Google Scholar]
  4. Biswas, A. and Sharma, S.P.
    [2014] Resolution of multiple sheet-type structures in self-potential measurements, Earth System Science, 123, 809–825.
    [Google Scholar]
  5. Bresco, M., Raiconi, G., Barone, F., De Rosa, R., Milano, L.
    [2005] Genetic approach helps to speed classical Price algorithm for global optimization, Soft Computing, 9, 525–535.
    [Google Scholar]
  6. Burg, J.P.
    [1975] Maximum entropy spectral analysis, Ph.D. Thesis, Stanford University, California.
    [Google Scholar]
  7. Das, B., and Agarwal, P.B.
    [2012] Depth determination of 2-D SP anomaly source using energy spectrum method and its advantages, Proceedings of 9th SPG Annual meeting, Expanded abstracts.
    [Google Scholar]
  8. Di Maio, R. and Patella, D.
    [1994] Self-potential anomaly generation in volcanic areas. The Mt. Etna case history, Acta Vulcanologica, 4, 119–124.
    [Google Scholar]
  9. Di Maio, R., Piegari, E., Rani, P. and Avella, A.
    [2016] Self-potential data inversion through the integration of spectral analysis and tomographic approaches, Geophysical Journal International (submitted).
    [Google Scholar]
  10. Essa, K.S.
    [2011] A new algorithm for gravity or self-potential data interpretation, Journal of Geophysics and Engineering, 8, 434–446.
    [Google Scholar]
  11. Göktürkler, G. and Balkaya, Ç.
    [2012] Inversion of self-potential anomalies caused by simple-geometry bodies using global optimization algorithms, Journal of Geophysics and Engineering, 9, 498–507.
    [Google Scholar]
  12. Iuliano, T., Mauriello, P. and Patella, D.
    [2001] A probability tomography approach to the analysis of potential field data in the Campi Flegrei Caldera (Italy), Annals of Geophysics, 44, 403–420.
    [Google Scholar]
  13. Patella, D.
    [1997] Introduction to ground surface self-potential tomography, Geophysical Prospecting, 45, 653–681.
    [Google Scholar]
  14. Monteiro Santos, F.A.
    [2010] Inversion of self-potential of idealized bodies’ anomalies using particle swarm optimization, Computer & Geosciences, 36, 1185–1190.
    [Google Scholar]
  15. Price, W.L.
    [1976] A controlled random search procedure for global optimization, The Computer Journal, 20, 357–370.
    [Google Scholar]
  16. Rani, P., Di Maio, R. and Piegari, E.
    [2015] High-resolution spectral analysis methods for self-potential data inversion, Proceedings of 85th SEG Annual meeting, Expanded abstracts, 1596–1601.
    [Google Scholar]
  17. Spector, A. and Grant, F.S.
    [1970] Statistical models for interpreting aeromagnetic data, Geophysics, 35, 293–302.
    [Google Scholar]
  18. Srivastava, S., Agarwal, B.N.P.
    [2009] Interpretation of self-potential anomalies by enhanced local wavenumber technique, Journal of Applied Geophysics, 68, 259–268.
    [Google Scholar]
  19. Stoica, P. and Moses, R.L.
    [2005] Spectral Analysis of Signals. Prentice Hall, New Jersey, 452 pp.
    [Google Scholar]
  20. Ulrych, T.J. and Bishop, T.N.
    [1975] Maximum entropy spectral analysis and autoregressive decomposition, Review of Geophysics and Space Physics, 13, 183–200.
    [Google Scholar]
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