Mineral resource estimation using a combination of drilling and IP-Rs data using statistical and cokriging methods

Yıl 2019, Cilt: 160 Sayı: 160, 177 - 195, 27.12.2019

Kamran Mostafaei Hamidreza Ramazi

https://doi.org/10.19111/bulletinofmre.502794

Cited By: 1

Öz

The aim of this
research is the mineral resource estimation using a combination of drilling and
IP-Rs data. Therefore the approach of this paper is to study the correlation of
induced polarization (IP) and Electrical resistivity (Rs) data with drilling
data in order to grade estimation and mineral resource estimation. Reducing the
boreholes number and optimization of the boreholes location is another aim of
this research. The Abassabad copper mine located in Miami-Sabzevar
mineralization belt northeast Iran was chosen as a case study. Within the
borehole locations, geophysical profiles were designed and surveyed. After
IP-Rs data inversion, 2D sections were prepared. The 3D block models of IP-Rs were
constructed by geostatistical methods. The correlation between IP-Rs and
drilling data were examined by statistical and geostatistical methods using
regression, multivariate regression analysis, and cokriging. Based on the
mentioned methods copper grade was estimated and the 3D block models of Cu
grade were constructed. Obtained models were checked and compared with real Cu
model compiled according to drilling data which was done after geophysical
measurements. Results showed that the regression between IP data and Cu grade
was more appropriate with least error. Rs data are not suitable for Cu
estimation, due to changing intervals which led to increasing estimation error.
Based on the suggestions of this paper, we could reduce the number of boreholes
to 30% of the initial number and optimize the boreholes locations.

Anahtar Kelimeler

Correlation, Cu grade, IP-Rs, Statistical methods, Cokriging

Kaynakça

• Amini, A., Ramazi, H. 2016a. Anomaly enhancement in 2D electrical resistivity imaging method using a residual resistivity technique, the journal of the Southern African Institute of Mining and Metallurgy, vol. 116, 1–8. http://dx.doi. org/10.17159/2411-9717/2016/v116n2a7
• Amini, A., Ramazi, H. 2016b. Application of Electrical Resistivity Imaging for Engineering Site Investigation, A Case Study on Prospective Hospital Site,Varamin, Iran, Acta Geophysica, 64(4), 2200–2213. http://doi.org/10.1515/ acgeo-2016-0100.
• Armstrong, J. S. 2012. Illusions in regression analysis International Journal of Forecasting, 28, 689–694
• Batte, A. G., Barifaijo. E., Kiberu. J. M., Kawule, W., Muwanga, A., Owor, M., Kisekulo, J. 2010. Correlation of geoelectric data with aquifer parameters to delineate the groundwater potential of hard rock terrain in central Uganda. Pure and Applied Geophysics, 167(12), 1549–1559. http:// doi.org/10.1007/s00024-010-0109-x
• Biswas, A., Sharma, S. P. 2016. Integrated geophysical studies to elicit the structure associated with Uranium mineralization around South Purulia Shear Zone, India: A Review. Ore Geology Reviews, 72, 1307-1326.
• Biswas, A., Mandal, A., Sharma, S. P., Mohanty, W. K. 2014. Delineation of subsurface structure using self-potential, gravity and resistivity surveys from South Purulia Shear Zone, India: Implication to uranium mineralization. Interpretation, 2(2), T103-T110.
• Boezio, M. N. M., Costa, J. F. C. L., Koppe, J. C. 2011. Ordinary Cokriging of Additive Log-Ratios for Estimating Grades in Iron Ore Deposits, Proceedings of the 4th International Workshopon Compositional Data Analysis 1–10.
• Bohling, G. 2005. KRIGING, C&PE 940, 1–20. ttp://people.ku.edu/~gbohling/cpe940.
• Bohling, G. 2007. S-GeMS Tutorial Notes in Hydrogeophysics: Theory, Methods, and Modeling, Boise State University, Boise, Idaho, 1–26.
• Braglia, M., Carmignani, G., Frosolini, M., Zammori, F. 2012. Data classification and MTBF prediction with a multivariate analysis approach. Reliability Engineering and System Safety, 97(1), 27–35. http://doi.org/10.1016/j.ress.2011.09.010
• Charbucinski, J., Malos, J., Rojc, A., Smith, C. 2003. Prompt gamma neutron activation analysis method and instrumentation for copper grade estimation in large diameter blast holes. Applied Radiation and Isotopes, 59, 197–203. http://doi. org/10.1016/S0969-8043(03)00163-5
• Chiles, J.P., Delfiner, P. 2012 Geostatistics: modeling Spatial Uncertainty, Hoboken, N.J., Wiley.
• Chiou, J., Yang, Y., Chen, Y. 2016. Multivariate functional linear regression and prediction. Journal of Multivariate Analysis, 146, 301–312. http://doi. org/10.1016/j.jmva.2015.10.003
• Dahlin, T., Loke, M. H. 2015. Negative apparent chargeability in time-domain induced polarisation data. Journal of Applied Geophysics, 123, 322–332. http://doi. org/10.1016/j.jappgeo.2015.08.012.
• Dahlin, T., Leroux, V., Nissen, J. 2002. Measuring techniques in induced polarisation imaging. Journal of Applied Geophysics, 50(3), 279–298. http://doi. org/10.1016/S0926-9851(02)00148-9.
• Deutsch, C.V., Journel, A.G. 1998. GSLIB: Geostatistical Software Library and User’s Guide, second edition. Oxford, UK: Oxford University Press.
• Ehinola, O. A., Oladunjoye, M. A., Gbadamosi, T. O. 2009. Chemical composition , geophysical mapping and reserve estimation of clay deposit from parts of Southwestern Nigeria, Journal of Geology and Mining Research , 1(3), 57–66.
• Faul, F., Erdfelder, E., Buchner, A., Lang, A. G. 2009. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41 (4), 1149–1160. http://doi.org/10.3758/BRM.41.4.1149.
• Ferdows, M. S., Ramazi, H. 2015. Application of the fractal method to determine the membership function parameter for geoelectrical data ( case study : Hamyj copper deposit , Iran ). Journal of Geophysics and Engineering, 12. 909–921.
• Gadallah, M. R., Fisher, R. 2009. Exploration geophysics. Berlin. Springer. http://doi.org/10.1007/978-3- 540-85160-8
• Goulard, M., Voltz, M. 1992 Linear Coregionalization Model: Tools for Estimation and Choice of Cross-Variogram Matrix, Mathmatical Geology, 24(3),269-286.
• Granian, H., Hassan, S., Asadi, H. H., John, E., Carranza, M. 2015. Multivariate regression analysis of lithogeochemical data to model subsurface mineralization: a case study from the Sari Gunay epithermal gold deposit, NW Iran. Journal of Geochemical Exploration, 148, 249–258. http:// doi.org/10.1016/j.gexplo.2014.10.009
• Gurin, G., Tarasov, A., Ilyin, Y., Titov, K. 2015. Application of the Debye decomposition approach to analysis of induced-polarization profiling data (Julietta gold-silver deposit, Magadan Region) Russian Geology and Geophysics 56, 1757–1771. http:// doi.org/10.1016/j.rgg.2015.11.008
• Habibi, M. J., Mokhtari, A. R., Baghbanan, A., Namdari, S. 2014. Journal of Petroleum Science and Engineering Prediction of permeability in dual fracture media by multivariate regression analysis. Journal of Petroleum Science and Engineering, 120, 194–201. http://doi.org/10.1016/j. petrol.2014.06.016
• Helsel, B. D. R., Hirsch, R. M. 2002. Hydrologic Analysis and Interpretation; Chapter A3 Statistical Methods in Water Resources. Techniques of Water-Resources Investigations of the United States Geological Survey.
• Hooshmand, A., Delghandi, M., Izadi, A., Aali, K. A. 2011. Application of kriging and cokriging in spatial estimation of groundwater quality parameters, African Journal of Agricultural Research, 6(14), 3402–3408.
• Howarth, R. J. 2001. A History of Regression and Related Model-Fitting in the Earth Sciences (1636? -2000), Natural Resources Research 10(4),241- 286.
• Jodeiri, B., Ramazi, H., Faramarz, S., Ardejani, D., Moradzadeh, A. 2014. Integrated Time-Lapse Geoelectrical–Geochemical Investigation at a Reactive Coal Washing Waste Pile in Northeastern Iran, NE Iran, Mine Water and the Environment, 33: 256-265.
• Jodeiri, B., Faramarz, S., Ardejani, D., Ramazi, H., Moradzadeh, A. 2016. Predicting pyrite oxidation and multi-component reactive transport processes from an abandoned coal waste pile by comparing 2D numerical modeling and 3D geoelectrical inversion, International Journal of Coal Geology, 164: 13-24
• Khanlari, G. R., Heidari, M., Momeni, A. A., Abdilor, Y. 2012. Prediction of shear strength parameters of soils using arti fi cial neural networks and multivariate regression methods. Engineering Geology, 131–132, 11–18. http://doi. org/10.1016/j.enggeo.2011.12.006
• Knotters, M., Brus, D. J., Voshaar, J. H. O. 1995. A comparison of kriging , co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations, Geoderma, 67, 227–246.
• Kumar, D., Ahmed, S., Krishnamurthy, N. S., Dewandel, B. 2007. Reducing ambiguities in vertical electrical sounding interpretations: A geostatistical application, Journal of Applied Geophysics, 62(1), 16–32. http://doi.org/10.1016/j. jappgeo.2006.07.001
• Leuangthong, O., Daniel Khan, K., Deutsch, C.V. 2008. Solved problem in geostatisitcs, Chapter 9: Multiple Variable.
• Li, X., Xie, Y., Guo, Q., Li, L. 2010. Adaptive ore grade estimation method for the mineral deposit evaluation. Mathematical and Computer Modelling, 52(11–12), 1947–1956. http://doi. org/10.1016/j.mcm.2010.04.018.
• Li, X., Li, L., Zhang, B., Guo, Q. 2013. Neurocomputing Hybrid self-adaptive learning based particle swarm optimization and support vector regression model for grade estimation. Neurocomputing, 118, 179–190.http://doi.org/10.1016/j. neucom.2013.03.002.
• Loke, M.H., 2015, Tutorial: 2-D and 3-D electrical imaging surveys: (Revision date: 17th October 2015), (www.geotomosoft.com)
• Loke, M. H., Dahlin, T. 2002. A comparison of the Gauss – Newton and quasi-Newton methods in resistivity imaging inversion, Journal of Applied Geophysics, 49, 149–162.
• Madani, N., Emery, X. 2018. A comparison of search strategies to design the cokriging neighborhood for predicting coregionalizaed variables, Stochastic Environmental Research and Risk Assessment, https://doi.org/10.1007/s00477-018-1578-1
• Mandal, A., Biswas, A., Mittal, S., Mohanty, W. K., Sharma, S. P. Sengupta, D., Sen, J., Bhatt, A. K. 2013. Geophysical anomalies associated with uranium mineralization from Beldih mine, South Purulia Shear Zone, India. Journal Geological Society of India, 82(6), 601-606.
• Mandal, A., Mohanty, W.K., Sharma S.P., Biswas, A., Sen, J., Bhatt, A. K. 2015. Geophysical signatures of uranium mineralization and its subsurface validation at Beldih, Purulia District, West Bengal, India: a case study.Geophysical Prospecting, 63(2), 713-726. https://doi.org/10.1111/1365- 2478.12205.
• Martinho, E., Almeida, F. 2006. 3D behaviour of contamination in landfill sites using 2D resistivity/IP imaging: Case studies in Portugal. Environmental Geology, 49(7), 1071–1078. http://doi.org/10.1007/s00254-005-0151-7
• Martínez-Moreno, F.J., Pedrera, A., Ruano, P., Galindo- Zaldívar, J., Martos-Rosillo, S., González- Castillo, L., Sánchez-Úbeda, J.P., Marín-Lechado, C. 2013. Combined microgravity, electrical resistivity tomography and induced polarization to detect deeply buried caves: Algaidilla cave (Southern Spain). Engineering Geology, 162, 67- 78, doi: 10.1016/j.enggeo.2013.05.008.
• Mashhadi, S.R., Mostafaei, K., Ramazi, H. 2017. Improving bitumen detection in resistivity surveys by using induced polarisation data, Exploration Geophysics. https://doi.org/10.1071/EG17032.
• Mogaji, K. A. 2016. Geoelectrical parameter-based multivariate regression borehole yield model for predicting aquifer yield in managing groundwater resource sustainability. Integrative Medicine Research, 10(4), 584–600. http://doi. org/10.1016/j.jtusci.2015.12.006
• Mokhtari, A. R. 2014. Hydrothermal alteration mapping through multivariate logistic regression analysis of lithogeochemical data. Journal of Geochemical Exploration, 145, 207–212. http:// doi.org/10.1016/j.gexplo.2014.06.008
• Mostafaie, K., Ramazi, H. 2015. Application of electrical resistivity method in sodium sulfate deposits exploration, case study: Garmab, Iran. Journal of Biodiversity and Environmental Sciences, 6(2), 2220–6663. Retrieved from http://www.innspub. Net
• Mostafaie, K., Ramazi, H. R., Jalai, M. 2014. Application of Integrated Geophysical and Geostatistical Methods in Amiriyeh Site Classification. Geodynamics Research International Bulletin (GRIB), (2) 2,1-15.
• Maurya, P.K., Ronde, V.K., Fiandaca, G., Balbarini, N., Auken, E., Bjerg, L.P., Christiansen, A.V. 2017 Detailed landfill leachate plume mapping using 2D and 3D electrical resistivity tomography- with correlation to ionic strength measured in screens. Journal of Applied Geophysica, 138:1-8.
• Noori, R., Khakpour, A., Omidvar, B., Farokhnia, A. 2010. Expert Systems with Applications Comparison of ANN and principal component analysis- multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems With Applications, 37(8), 5856–5862. http://doi. org/10.1016/j.eswa.2010.02.020
• Perozzi, L., Gloaguen, E., Rondenay, S., McDowell, G. 2012. Using stochastic crosshole seismic velocity tomography and Bayesian simulation to estimate Ni grades: Case study from Voisey’s Bay, Canada. Journal of Applied Geophysics, 78, 85–93. http:// doi.org/10.1016/j.jappgeo.2011.06.036
• Ramazi, H., Mostafaie, K. 2013. Application of integrated geoelectrical methods in Marand (Iran) manganese deposit exploration. Arabian Journal of Geosciences, 6(8), 2961–2970. http://doi. org/10.1007/s12517-012-0537-2
• Ramazi, H., Jalali, M. 2014. Contribution of geophysical inversion theory and geostatistical simulation to determine geoelectrical anomalies. Stud. Geophys. Geod. 59: 97–112.
• Salehi, L., Rasa, I., Alirezaei, S., Kazemi Mehrnia, A. 2016. The Madan Bozorg, volcanic-hosted copper deposit, East Shahroud; an example of Manto type copper deposits in Iran, Journal of Geoscience, 25(98): 93-104.
• Seccatore, J., Marin, T., Tomi, G. De, Veiga, M. 2014. A practical approach for the management of resources and reserves in Small-Scale Mining. Journal of Cleaner Production, 84, 803–808. http://doi.org/10.1016/j.jclepro.2013.09.031
• Sevil, J., Gutierrez, F., Zarroca, M., Desira, G., Carbonela, D., Guerrero, J., Linares, R., Roque, C., Fabregat, I. 2017. Sinkhole investigation in an urban area by trenching in combination with GPR, ERT and high-precision leveling. Manteled evaporate karst of Zaragoza city, NE Spain. Engineering Geology 213: 9-20 II. Shademan, Kh M., Madani,H., Hassani, H., Moarefvand, P. 2013. Determining the Best Search Neighbourhood in Reserve Estimation , using Geostatistical Method: A Case Study Anomaly No 12A Iron Deposit in Central Iran, Journal Geological Society of India , 81(12), 581–585.
• Tahmasebi, P., Hezarkhani, A. 2012. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Computers and Geosciences, 42, 18– 27. http://doi.org/10.1016/j.cageo.2012.02.004 Telford, W. M., Geldart, L. P., Sheriff, R. E. 1990. Applied Geophysics. Cambridge University Press, Cambridge. http://doi.org/10.1180/ minmag.1982.046.341.32
• Tütmez, B., Tercan, A. E., Kaymak, U. 2007. Fuzzy Modeling for Reserve Estimation Based on Spatial Variability. Mathematical Geology, 39(1),87-111. http://doi.org/10.1007/s11004-006-9066-4
• Ushie, F., Harry, T., Affiah, U. 2014. Reserve Estimation from Geoelectrical Sounding of the Ewekoro Limestone at Papalanto , Ogun State , Nigeria , Journal of Energy Technologies and Policy 4(5), 28–33.
• Wackernagal, H. 2003. Mutivariate Geostatistics; an introduction with applications, Springer sciences.
• Wang, G., Pang, Z., Boisvert, J. B., Hao, Y., Cao, Y., Qu, J. 2013. Quantitative assessment of mineral resources by combining geostatistics and fractal methods in the Tongshan porphyry Cu deposit ( China ). Journal of Geochemical Exploration, 134, 85–98. http://doi.org/10.1016/j. gexplo.2013.08.004
• Wang, Q., Deng, J., Liu, H., Yang, L., Wan, L., Zhang, R. 2010. Fractal models for ore reserve estimation. Ore Geology Reviews, 37(1), 2–14. http://doi. org/10.1016/j.oregeorev.2009.11.002
• Wang, Q., Deng, J., Liu, H., Wang, Y., Sun, X., Wan, L. 2011. Fractal models for estimating local reserves with different mineralization qualities and spatial variations. Journal of Geochemical Exploration, 108(3), 196–208. http://doi.org/10.1016/j. gexplo.2011.02.008
• Webber, T., Costa, J. F. C. L., Salvadoretti, P. 2013. Using borehole geophysical data as soft information in indicator kriging for coal quality estimation. International Journal of Coal Geology, 112, 67– 75. http://doi.org/10.1016/j.coal.2012.11.005
• White, R. M. S., Collins, S., Loke, M. H. 2003. Resistivity and IP arrays, optimised for data collection and inversion. Exploration Geophysics, 34(4), 229. http://doi.org/10.1071/EG03229
• Xu, H., Sun, J., Russell, B., Innanen, K. 2015. Porosity prediction using cokriging with multiple secondary datasets, CREWES Research Report, 27, 1–13.
• Zhang, W., Goh, A. T. C. 2016. Geoscience Frontiers Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers, 7(1), 45–52. http://doi.org/10.1016/j.gsf.2014.10.003.