Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Self potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike: mineralized zones delineation

Abstract

The self potential data interpretation is very important to delineate and trace the mineralized zones in several regions. We study how to interpret self potential anomalies due to a finite two-dimensional inclined dike using the particle swarm algorithm. However, the precise estimation of the model parameters during the inverse solution are unknown. Here, we show that the particle swarm algorithm is capable of estimating the unknown parameters with acceptable accuracy. The evaluated parameters are the polarization parameter, the depth, the inclination angle, the width, and the location of the source of the target. We found in controlled in free-noise synthetic case that the particle swarm algorithm has a remarkable capability of assessing the parameters. For a noisy case, the results also are very competitive. Furthermore, it is utilized for real mineralized zones examples from Germany and India. Our results demonstrate how the particle swarm algorithm overcomes in trapping in local minimum solutions (undesired) and go faster to the global solutions (desired). Finally, the target parameters estimated are matched with accessible geologic and geophysical information.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Abdelrahman EM, Saber HS, Essa KS, Fouda MA (2004) A least-squares approach to depth determination from numerical horizontal self-potential gradients. Pure Appl Geophys 161:399–411

  2. Abdelrahman EM, Essa KS, Abo-Ezz ER, Soliman KS (2006a) Self-potential data interpretation using standard deviations of depths computed from moving average residual anomalies. Geophys Prospect 54:409–423

  3. Abdelrahman EM, Essa KS, El-Araby TM, Abo-Ezz ER (2006b) A least-squares depth-horizontal position curves method to interpret residual SP anomaly profile. J Geophys Eng 3:252–259

  4. Abdelrahman EM, El-Araby TM, Essa KS (2009a) Shape and depth determination from second moving average residual self-potential anomalies. J Geophys Eng 6:43–52

  5. Abdelrahman EM, Soliman KS, Abo-Ezz ER, Essa KS, El-Araby TM (2009b) Quantitative interpretation of self-potential anomalies of some simple geometric bodies. Pure Appl Geophys 166:2021–2035

  6. Abdelrahman EM, Abo-Ezz ER, El-Araby T, Essa KS (2016) A simple method for depth determination from self-potential anomalies due to two superimposed structures. Explor Geophys 47:308–314

  7. Agarwal B, Sirvastava S (2009) Analyses of self-potential anomalies by conventional and extended Euler deconvolution techniques. Comput Geosci 35:2231–2238

  8. Asfahani J, Tlas M (2001) A nonlinear programming technique for the interpretation of self-potential anomalies. Pure Appl Geophys 159:1333–1343

  9. Atchuta Rao R, Ram B, Sivakumar Sinha GDJ (1982) A Fourier transform method for the interpretation of self-potential anomalies due to two to two-dimensional inclined sheets of finite depth extent. Pure Appl Geophys 120:365–374

  10. Bhattacharya BB, Roy N (1981) A note on the use of nomograms for self-potential anomalies. Geophys Prospect 29:102–107

  11. Biswas A (2013) Identification and resolution of ambiguities in interpretation of self-potential data: analysis and integrated study around South Purulia Shear Zone, India. Dissertation, Department of Geology and Geophysics, Indian Institute of Technology Kharagpur

  12. Biswas A (2016) A comparative performance of least-square method and very fast simulated annealing global optimization method for interpretation of self-potential anomaly over 2-D inclined sheet type structure. J Geol Soc India 88:493–502

  13. Biswas A (2017) A review on modeling, inversion and interpretation of self-potential in mineral exploration and tracing paleo-shear zones. Ore Geol Rev 91:21–56

  14. Biswas A, Sharma SP (2015) Interpretation of self-potential anomaly over idealized body and analysis of ambiguity using very fast simulated annealing global optimization. Near Surf Geophys 13:179–195

  15. Chetty TRK (2011) Tectonics of proterozoic Cuddapah basin, Southern India: a conceptual model. J Geol Soc India 78:446–456

  16. Corry CE (1985) Spontaneous potential associated with porphyry sulphide mineralization. Geophysics 50:1020–1034

  17. Di Maio R, Rani P, Piegari E, Milano L (2016) Self-potential data inversion through a genetic-price algorithm. Comput Geosci 94:86–95

  18. Di Maio R, Piegari E, Rani P (2017) Source depth estimation of self-potential anomalies by spectral methods. J Appl Geophys 136:315–325

  19. Di Maio R, Piegari E, Rani P, Carbonari R, Vitagliano E, Milano L (2019) Quantitative interpretation of multiple self-potential anomaly sources by a global optimization approach. J Appl Geophys 162:152–163

  20. Drahor MG (2004) Application of the self-potential method to archaeological prospection: some case histories. Archaeol Prospect 11:77–105

  21. El-Kaliouby H, Al-Garni MA (2009) Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks. J Geophys Eng 6:29–34

  22. Essa KS (2011) A new algorithm for gravity or self-potential data interpretation. J Geophys Eng 8:434–446

  23. Essa KS (2019) A particle swarm optimization method for interpreting self potential anomalies. J Geophys Eng 16:463–477

  24. Essa KS, Elhussein M (2017) A new approach for the interpretation of self-potential data by 2-D inclined plate. J Appl Geophys 136:455–461

  25. Essa KS, Elhussein M (2018) Particle swarm optimization (PSO) for interpretation of magnetic anomalies caused by simple geometrical structures. Pure Appl Geophys 175:3539–3553

  26. Essa KS, Munschy M (2019) Gravity data interpretation using the particle swarm optimization method with application to mineral exploration. J Earth Syst Sci 128:123

  27. Essa KS, Mehanee S, Smith P (2008) A new inversion algorithm for estimating the best fitting parameters of some geometrically simple body from measured self-potential anomalies. Explor Geophys 39:155–163

  28. Fedi M, Abbas M (2013) A fast interpretation of self-potential data using the depth from extreme points method. Geophysics 78:E107–E116

  29. Fernandez-Martinez J, Garcia-Gonzalo E, Naudet V (2010) Particle swarm optimization applied to solving and appraising the streaming potential inverse problem. Geophysics 75:WA3–WA15

  30. Fitterman DV (1979) Calculations of self-potential anomalies near vertical contacts. Geophysics 44:195–205

  31. Griffin WR (1949) Residual gravity in theory and practice. Geophysics 14:39–58

  32. Hinze WJ, von Frese RRB, Saad AH (2013) Gravity and magnetic exploration: principles, practices, and applications. Cambridge University Press, Cambridge

  33. Karcıoğlu G, Gürer A (2019) Implementation and model uniqueness of particle swarm optimization method with a 2D smooth modeling approach for radio-magnetotelluric data. J Appl Geophys 169:37–48

  34. Mehanee S (2014) An efficient regularized inversion approach for self-potential data interpretation of ore exploration using a mix of logarithmic and non-logarithmic model parameters. Ore Geol Rev 57:87–115

  35. Mehanee S (2015) Tracing of paleo-shear zones by self-potential data inversion: case studies from the KTB, Rittsteig, and Grossensees graphite-bearing fault planes. Earth Planets Space 67:14

  36. Mehanee S, Essa KS, Smith P (2011) A rapid technique for estimating the depth and width of a two-dimensional plate from self-potential data. J Geophys Eng 8:447–456

  37. Meiser P (1962) A method of quantitative interpretation of self-potential measurements. Geophys Prospect 10:203–218

  38. Narayan SPV, Sarma SVS, Rao DA, Jain SC, Verma SK, et al (1982) Report on multi-parameter geophysical experiment in Kalava area (Cuddapah Basin) Kurnool District, Andhra Pradesh. Paper presented at the fifth workshop on status, problems and programmes in Cuddapah Basin, held during 11–12th January, 1982, organized by the Institute of Indian Peninsular Geology, Hyderabad, India

  39. Naudet V, Revil A, Rizzo E, Bottero JY, Bégassat P (2004) Groundwater redox conditions and conductivity in a contaminant plume from geoelectrical investigations. Hydrol Earth Syst Sci 8:8–22

  40. Naudet V, Fernandez-Martinez J, Garcia-Gonzalo E, Fernandez-Alvarez J (2008) Estimation of water table from self-potential data using particle swarm optimization (PSO). SEG Expand Abstr 27:1203

  41. Obasi AI, Onwuemesi AG, Romanus OM (2016) An enhanced trend surface analysis equation for regional–residual separation of gravity data. J Appl Geophys 135:90–99

  42. Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1:235–306

  43. Roudsari MS, Beitollahi A (2013) Forward modelling and inversion of self-potential anomalies caused by 2D inclined sheets. Explor Geophys 44:176–184

  44. Roy IG (2019) On studying flow through a fracture using self-potential anomaly: application to shallow aquifer recharge at Vilarelho da Raia, northern Portugal. Acta Geod Geophys 54:225–242

  45. Saha D, Tripathy V (2012) Palaeoproterozoic sedimentation in the Cuddapah Basin, south India and regional tectonics: a review. Geol Soc Lond Spec Publ 365(1):161–184

  46. Sato M, Mooney HM (1960) The electrochemical mechanism of sulfide self-potentials. Geophysics 25:226–249

  47. Sen MK, Stoffa PL (2013) Global optimization methods in geophysical inversion. Cambridge University Press, Cambridge

  48. Sharma SP, Biswas A (2013) Interpretation of self-potential anomaly over 2D inclined structure using very fast simulated annealing global optimization: an insight about ambiguity. Geophysics 78:WB3–WB15

  49. Sindirgi P, Özyalin S (2019) Estimating the location of a causative body from a self-potential anomaly using 2D and 3D normalized full gradient and Euler deconvolution. Turkish J Earth Sci 28:640–659

  50. Singh A, Biswas A (2016) Application of global particle swarm optimization for inversion of residual gravity anomalies over geological bodies with idealized geometries. Nat Resour Res 25:297–314

  51. Sundararajan N, Srinivasa Rao P, Sunitha V (1998) An analytical method to interpret self-potential anomalies caused by 2D inclined sheets. Geophysics 63:1551–1555

  52. Sungkono, Warnana DD (2018) Black hole algorithm for determining model parameter in self-potential data. J Appl Geophys 148:189–200

  53. Tlas M, Asfahani J (2007) A best-estimate approach for determining self-potential parameters related to simple geometric shaped structures. Pure Appl Geophys 164:2313–2328

  54. Tlas M, Asfahani J (2008) Using of the adaptive simulated annealing (ASA) for quantitative interpretation of self-potential anomalies due to simple geometrical structures. J KAU: Earth Sci 19:99–118

  55. Tlas M, Asfahani J (2013) An approach for interpretation of self-potential anomalies due to simple geometrical structures using flair function minimization. Pure appl Geophys 170:895–905

  56. Yungul S (1950) Interpretation of spontaneous polarization anomalies caused by spheroidal orebodies. Geophysics 15:163–256

Download references

Acknowledgements

The author would like to thank Prof. Dr. Norbert Péter Szabó, Editor, and the two reviewers for their keen interest, valuable comments on the manuscript, and improvements to this work. The author would like to thank the Institut Francais d’Egypte (IFE) in Cairo, Egypt for providing a full support to finish this work.

Author information

Correspondence to Khalid S. Essa.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Essa, K.S. Self potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike: mineralized zones delineation. Acta Geod Geophys (2020). https://doi.org/10.1007/s40328-020-00289-2

Download citation

Keywords

  • Self potential interpretation
  • The particle swarm algorithm
  • The depth
  • Mineral exploration