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Volume 17 Number 6
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

The main issue in mineral exploration is to obtain as much information as possible in a time‐ and cost‐efficient manner. This may be achieved through a combination of geophysical methods including the induced polarization method. The induced polarization is the most applicable method in case of metal exploration through drilling. We argue that the relationship between induced polarization, electrical resistivity and ore grade data may be used in resource estimation. This argument is tested in the active copper mine of Madan Bozorg, Abassabad, which is located in the Miami‐Sabzevar mineralization belt in NW Iran. Within the borehole locations, geophysical profiles of induced polarization and electrical resistivity were designed and surveyed using combined resistivity sounding and profiling array. Two‐dimensional models of induced polarization and electrical resistivity were prepared through inversion of geophysical data, after the primary processing. The three‐dimensional block models of induced polarization and electrical resistivity were also compiled using geostatistical methods. Obtained two‐dimensional and three‐dimensional models of induced polarization and electrical resistivity were checked using exploratory boreholes to investigate the relationship among induced polarization, electrical resistivity and copper grade. The three‐dimensional block model of the copper ore was prepared using cokriging and artificial neural network, using a combination of induced polarization and drilling data. In the cases where borehole data were unavailable, the presence of copper ore was predicted by artificial neural network and cokriging methods, and the three‐dimensional models of ore body were constructed in all of the study area. Obtained models based on the predicted copper ore distribution were checked and compared with ore distribution model compiled using drilling data. The results indicate a good agreement between predicted and real results. This led to reduction of borehole number to about 45% and the optimization of boreholes locations. Finally, an optimized drilling plan has been prepared and presented for the Abassabad copper mine.

© 2019 European Association of Geoscientists & Engineers © 2019 European Association of Geoscientists & Engineers
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2019-07-25
2024-04-20

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References

  1. AghanabatiA.2009. Geology of Iran, Ministry and Mines: Geological survey of Iran, Tehran.
  2. AizebeokhaiA.P., OlayinkaA.I., SinghV.S. and UhuegbuC.C.2011. Effective of 3D geoelectrical resistivity imaging using pRllel 2D profiles. Current Science101, 1036–1052.
     [Google Scholar]
  3. AminiA. and RamaziH.2016a. Anomaly enhancement in 2D electrical resistivity imaging method using a residual resistivity technique. Journal of the Southern African Institute of Mining and Metallurgy116, 1–8.
     [Google Scholar]
  4. AminiA. and RamaziH.2016b. Application of electrical resistivity imaging for engineering site investigation, a case study on prospective hospital site,Varamin, Iran. Acta Geophysica64, 2200–2213.
     [Google Scholar]
  5. Ben‐awuahJ. and PadmanabhanE.2017. An enhanced approach to predict permeability in reservoir sandstones using artificial neural networks (ANN). Arabian Journal of Geosciences10, 173–188.
     [Google Scholar]
  6. BinleyA. and KemnaA.2005. DC resistivity and induced polarization methods. Hydrogeophysics. Water Science and Technology Library, Vol. 50 (eds Y.Rubin and S.S.Hubbard), pp. 129–156. Springer, Dordrecht.
     [Google Scholar]
  7. BishopJ.R. and EmersonD.W.1999. Geophysical properties of Zinc‐bearing deposits. Australian Journal of Earth Science46, 311‐328.
     [Google Scholar]
  8. BiswasA. and SharmaS.P.2016. Integrated geophysical studies to elicit the structure associated with uranium mineralization around South Purulia Shear Zone, India: a review. Ore Geology Reviews72, 1307–1326.
     [Google Scholar]
  9. BohlingG.2005. KRIGING. C&PE940, 1–20. http://people.ku.edu/~gbohling/cpe940.
     [Google Scholar]
  10. BohlingG.2007. S‐GeMS Tutorial Notes in Hydrogeophysics: Theory, Methods, and Modeling. Boise State University, Boise, Idaho.
     [Google Scholar]
  11. ChenC.H. and TsaoS.H.2001. Time‐ domain spectral IP and its applications in the southern Chinkuashin area, Northern Taiwan. TAO12, 583–598.
     [Google Scholar]
  12. ChilesJ.P. and DelfinerP.2012. Geostatistics: modeling Spatial Uncertainty. Wiley, Hoboken, NJ.
     [Google Scholar]
  13. DahlinT.2001. The development of DC resistivity imaging techniques. Computers & Geosciences9, 1019–1029.
     [Google Scholar]
  14. DahlinT., LerouxV. and NissenJ.2002. Measuring techniques in induced polarisation imaging. Journal of Applied Geophysics50, 279–298.
     [Google Scholar]
  15. DeutschC.V. and JournelA.G.1998. GSLIB: geostatistical software library and user's guide, 2nd edn. Oxford University Press, Oxford, UK.
     [Google Scholar]
  16. DuvillardP.A., RevilA., QiY., Aoueid AhmadA., CopereyA. and RavanelL.2018. Three dimensional electrical conductivity and induced polarization tomography of a rock glacier. Journal of Geophysical Research: Solid Earth123, 9528–9554.
     [Google Scholar]
  17. FerdowsS.M. and RamaziH.2015a. Application of the fractal method to determine the membership function parameter for geoelectrical data (case study: Hamyj copper deposit, Iran). Journal of Geophysics and Engineering12, 909–921.
     [Google Scholar]
  18. FerdowsS.M. and RamaziH.2015b. Application of the singularity mapping technic to identify local anomalies by polarization data (a case study: Hamyj Copper deposit, Iran). Acta Geodaetica et Geophysics50, 365–374.
     [Google Scholar]
  19. FinkJ.B., McAlistreE.O., SternbergB.K., WieduwiltW.G. and WardS.H.1990. Induced Polarization, Application and Case Histories. Investigation in Geophysics. Society of Exploration Geophysicists, Oklahoma.
     [Google Scholar]
  20. FloresC. and Peralta‐OrtegaS A.2009. Induced polarization with in‐loop transient electromagnetic soundings: a case study of mineral discrimination at El Arco porphyry copper, Mexico. Journal of Applied Geophysics68, 423–436.
     [Google Scholar]
  21. GharibiM. and BentlyL.R.2005. Resolution of 3‐D Electrical resistivity images from inversions of 2‐D Orthogonal Lines. Journal of Environmental and Engineering Geophysics10, 339–349.
     [Google Scholar]
  22. GoswamiA.D., Mishra, M.K. and PatraD.2017. Investigation of general regression neural network architecture for grade estimation of an Indian iron ore deposit. Arabian Journal of Geosciences 10.http://doi.org/10.1007/s12517-017-2868-5
     [Google Scholar]
  23. GoulardM. and VoltzM.1992. Linear coregionalization model: tools for estimation and choice of cross‐variogram matrix. Mathematical Geology24, 270–286.
     [Google Scholar]
  24. GurinG., TarasovA., IlyinY.T. and TitovK.V.2015. Application of the Debye decomposition approach to analysis of induced‐polarization profiling data (Julietta gold‐silver deposit, Magadan Region). Russian Geology and Geophysics56, 1757–1771.
     [Google Scholar]
  25. HalimI., AsyariA., WijaksanaA. and AlfadliK.2017. 3D modeling form induced polarization method for identification of gold deposit exploration in north Minahasa, North Sulawesi, Indonesia. International Geophysical Conference, Qingdao, China, April 17–20, 2017. https://doi.org/10.1190/IGC2017-056
  26. HooshmandA., DelghandiM., IzadiA. and AaliK.A.2011. Application of kriging and cokriging in spatial estimation of groundwater quality parameters. African Journal of Agricultural Research6, 3402–3408.
     [Google Scholar]
  27. HopeM. and AnderssonS.2016. The discovery and geophysical response of the Atlantida Cu‐Au porphyry deposit, Chile. Exploration Geophysics47, 237–247.
     [Google Scholar]
  28. JallohA.B., KyuroS., JallohY. and BarrieA.K.2016. Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation. Integrating Artificial Neural Networks and Geostatistic26, 581–585.
     [Google Scholar]
  29. Jun‐LuW., Ping‐RongL., MengW., DangL. and Jian‐HuaL.2017. Three‐dimensional tomography using high‐power induced polarization with the similar centeral gradient array. Applied Geophysics14(2), 291–300.
     [Google Scholar]
  30. KapageridisI.K.1999. Application of artificial neural network systems to grade estimation from exploration data. Thesis. University of Nottingham for the Degree of Doctor of Philosophy.
  31. KhesinB., AlexeyevV. and EppelbaumL.1997. Rapid methods for interpretation of induced polarization anomalies. Journal of Applied Geophysics37, 117–130.
     [Google Scholar]
  32. KnottersM., BrusD.J. and VoshaarJ.H.1995. A comparison of kriging, co‐kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma67, 227–246.
     [Google Scholar]
  33. KumarD., AhmedS., KrishnamurthyN.S. and DewandelB.2007. Reducing ambiguities in vertical electrical sounding interpretations: a geostatistical application. Journal of Applied Geophysics62, 16–32.
     [Google Scholar]
  34. LeuangthongO., KhanD. and DeutschC.V.2008. Solved problem in geostatisitcs, Chapter 9: Multiple Variable.
  35. LokeM.H.2015. Tutorial: 2‐D and 3‐D electrical imaging surveys. www.geotomosoft.com
  36. LokeM.H. and BarkerR.D.1996. Rapid least‐ squares investigation of apparent resistivity pseudo‐sections by a Quasi‐Newton method. Geophysical Prospecting44, 131–132.
     [Google Scholar]
  37. LokeM.H. and DahlinT.2002. A comparison of the Gauss–Newton and quasi‐Newton methods in resistivity imaging inversion. Journal of Applied Geophysics49, 149–162.
     [Google Scholar]
  38. MaadaniN. and EmeryX.2018. A comparison of search strategies to design the cokriging neighborhood for predicting coregionalized variables. Stochastic Environmental Research and Risk Assessment33, 183–199.
     [Google Scholar]
  39. MahmoudabadiH., IzadiM. and MenhajM.B.2009. A hybrid method for grade estimation using genetic algorithm and neural networks. Computers & Geosciences13, 91–92.
     [Google Scholar]
  40. MammoT.2013. Geophysical models for the Cu‐dominated VHMS mineralization in Katta District, Western Ethiopia. Natural Resources Research22, 5–18.
     [Google Scholar]
  41. Martínez‐MorenoF.J., Galindo‐ZaldívarJ., PedreraA., González‐CastilloL., RuanoP., CalaforraJ.M.et al. 2015. Detecting gypsum caves with microgravity and ERT under soil water content variations (Sorbas, SE Spain). Engineering Geology193, 38–48.
     [Google Scholar]
  42. Martínez‐MorenoF.J., Galindo‐ZaldívarJ., PedreraA., TeixidoT., RuanoP., PeñaJ.A.et al. 2014. Integrated geophysical methods for studying the karst system of Gruta de las Maravillas (Aracena, Southwest Spain). Journal of Applied Geophysics107, 149–162.
     [Google Scholar]
  43. Martínez‐MorenoF.J., PedreraA., RuanoP., Galindo‐ZaldívarJ., Martos‐RosilloS., González‐CastilloL.et al. 2013. Combined microgravity, electrical resistivity tomography and induced polarization to detect deeply buried caves: Algaidilla cave (Southern Spain). Engineering Geology162, 67–78.
     [Google Scholar]
  44. MashhadiS.R., MostafaeiK. and RamaziH.2017. Improving bitumen detection in resistivity surveys by using induced polarisation data. Exploration Geophysics49, 762–774.
     [Google Scholar]
  45. MashhadiS.R. and RamaziH.2018. The application of resistivity and induced polarization methods in identification skarn alteration haloes: a case study in the Qale‐Alimoradkhan area. Journal of Environmental and Engineering Geophysics23, 363–368.
     [Google Scholar]
  46. MokriA. and MarviD.2018. Copper Ore and Concentration. Ministry of Industry, Mine and Trade, Iran.
     [Google Scholar]
  47. MoreiraC.A., PaesR.A., IlhaL.M. and BittencourtC.J.2018. Reassessment of Copper mineral occurrence though electrical tomography and pseudo 3D modeling in camaquas sedimentary basin, Southern Brazil. Pure and Applied Geophysics176, 737–750.
     [Google Scholar]
  48. MostafaeiK. and RamaziH.2015. Application of electrical resistivity method in sodium sulfate deposits exploration, case study: Garmab, Iran. Journal of Biodiversity and Environmental Sciences6, 2220–6663.
     [Google Scholar]
  49. MostafaeiK. and RamaziH.2018. 3D model construction of induced polarization and resistivity data with quantifying uncertainties using geostatistical methods and drilling (Case study: Madan Bozorg, Iran). Journal of Mining & Environment9, 857–872.
     [Google Scholar]
  50. MostafaeiK. and RamaziH.2018. Compiling and verifying 3D models of 2D induced polarization and resistivity data by geostatistical methods. Acta Geophysica66, 959–971.
     [Google Scholar]
  51. NeaupaneK.M. and AhetS.H.2004. Use of back propagation neural network for landslide monitoring: a case study in the higher Himalaya. Engineering Geology74, 213–226.
     [Google Scholar]
  52. ParkG., ParkS. and KimJ.2010. Estimating the existence probability of cavities using integrated geophysics and a neural network approach. Computers and Geosciences36, 1161–1167.
     [Google Scholar]
  53. PazziV., CeccatelliM., GracchiT., MasiB. and FanitR.2018. Assessing subsoil void hazards along a road system using H/V easurements, ERTs and IPTs to support local decision makers. Near Surface Geophysics16, 282–297.
     [Google Scholar]
  54. RamaziH. and JalaliM.2014. Contribution of geophysical inversion theory and geostatistical simulation to determine geoelectrical anomalies. Studia Geophysica et Geodaetica59, 97–112.
     [Google Scholar]
  55. RamaziH. and MostafaieK.2013. Application of integrated geoelectrical methods in Marand (Iran) manganese deposit exploration. Arabian Journal of Geoscience6, 2961–2970.
     [Google Scholar]
  56. ReynoldsJ.M.2011. An Introduction to Applied and Environmental Geophysics. 2nd edn. John Wiley & Sons, Chichester, UK.
     [Google Scholar]
  57. RossiM., DahlinT., OlssonP.L. and GuntherT.2018. Data acquisition, processing and filtering for reliable 3D resistivity and time‐domain induced polarization in an urban area: field example of Vinsta, Stockholm. Near Surface Geophysics16, 220–229.
     [Google Scholar]
  58. RossiM., OlssonP.‐I., JohansonS., FiandacaG., BergdahlD.P. and DahlinT.2017. Mapping geological structures in bedrock via large‐scale direct current resistivity and time‐domain induced polarization tomography. Near Surface Geophysics15, 657–667.
     [Google Scholar]
  59. SalehiL., RasaI., AlirezaeiS. and Kazemi MehrniaA.2016. The madan bozorg, volcanic‐hosted copper deposit, East Shahroud; an example of Manto type copper deposits in Iran. Journal of Geoscience25, 93–104.
     [Google Scholar]
  60. SaljooghiB.S. and HezarkhaniA.2014. Journal of petroleum science and engineering comparison of WAVENET and ANN for predicting the porosity obtained from well log data. Journal of Petroleum Science and Engineering123, 172–182.
     [Google Scholar]
  61. SayadiA.R., TavassoliS.M.M., MonjeziM. and RezaeiM.2012. Application of neural networks to predict net present value in mining projects. Arabian Journal of Geosciences7. http://doi.org/10.1007/s12517-012-0750-z
     [Google Scholar]
  62. SinghU.K., TiwariR.K. and SinghS.B.2013. neural network modeling and prediction of resistivity structures using VES Schlumberger data over a geothermal area. Computers and Geosciences52, 246–257.
     [Google Scholar]
  63. SultanA., MansourA., SantosF. and Helaly, A.2009. Geophysical exploration for gold and associated minerals, case study: wadi El Beida, South eastern desert, Egypt. Journal of Geophysics Engineering6, 345–356.
     [Google Scholar]
  64. TahaA., IsmailA., MassoudU., MesbahH. and KomarovV.2018. Induced polarization measurement for gold exploration at the estern desert of Egupt. Bulletin of Earth Sciences of Thailand2, 20–30.
     [Google Scholar]
  65. TavakoliS., DehghannejadM., Garcia JuanateyMl., BauerTE., WeihedP. and ElmingS.2016. Potential field, geoelectrical and reflection seismic investigation for massive sulfhide exploration in the skellefte mining district, Northern Sweden. Acta Geophysica64, 2117–2199.
     [Google Scholar]
  66. TelfordW.M., GeldartL.P. and SheriffR.E.1990. Applied Geophysics, 2nd edn. Cambridge University Press.
     [Google Scholar]
  67. WackernagalH.2003. Mutivariate Geostatistics. An Introduction with Applications. Springer.
     [Google Scholar]
  68. WhiteR., CollinsS., DenneR., HeeR. and BrownP.2001. A new survey design for 3D IP inversion modelling at Copper Hill. Exploration Geophysics32, 152–155.
     [Google Scholar]
  69. WhiteR.M.S., CollinsS. and LokeM.H.2003. Resistivity and IP arrays, optimised for data collection and inversion. Exploration Geophysics34, 229.
     [Google Scholar]
  70. WuX. and ZhouY.1993. Reserve estimation using neural network techniques. Computers & Geosciences19, 567–575.
     [Google Scholar]
  71. WynnJ.C. and GroszA.E.2000. Induced polarization—a tool for mapping titanium‐bearing placers, hidden metallic objects, and urban waste on and beneath the seafloor. Journal of Environmental and Engineering Geophysics5, 27–35.
     [Google Scholar]
  72. YangJ., LiuZ. and WangL.2008. Effectiveness of natural field induced polarization for detecting polymetallic deposits. Earth Science Frontiers15, 217–221.
     [Google Scholar]
  73. ZhangG., LuQ., ZhangG.B., LinP., JiaZ.H. and SuoK.2018. Joint interpretation of geological, magnetic, AMT and ERT data for mineral exploration in the northeast of Inner Mangolia, China. Pure and Applied Geophysics175, 989–1002.
     [Google Scholar]
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Investigating the applicability of induced polarization method in ore modelling and drilling optimization: a case study from Abassabad, Iran
Near Surface Geophysics 17, 637 (2019); https://doi.org/10.1002/nsg.12055
/content/journals/10.1002/nsg.12055
/content/journals/10.1002/nsg.12055
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  • Article Type: Research Article
Keyword(s): Artificial neural network (ANN); cokriging; copper grade; induced polarization (IP); Iran

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