• Home
  • Resources classification using fractal modelling in Eastern Kahang Cu-Mo porphyry deposit, Central Iran

Share To

Article Url


Manuscript ID : IJES-1801-1165 (R1) Visit : 156 Page: 56 - 67

10.30495/ijes.2019.544596

Article Type: Original Research

Resources classification using fractal modelling in Eastern Kahang Cu-Mo porphyry deposit, Central Iran

Subject Areas : Mineralogy

Amir Bijan Yasrebi 1 , Ardashir Hezarkhani 2

1 - Computational Geomechanics Group, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Streatham Campus, Exeter, UK
2 - Department of Mining and Metallurgical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Received: 2018-02-04 Accepted : 2018-10-04 Published : 2019-01-01

Keywords: Resources classification, Concentration-Volume (C-V) fractal model, Estimated variance, Eastern Kahang Cu-Mo porphyry deposit,

Abstract :

Resources/reserves classification is crucial for block model creation utilised in mine planning and feasibility study. Selection of estimation methods is an essential part of mineral exploration and mining activities. In other word, resources classification is an issue for mining companies, investors, financial institutions and authorities, but it remains subject to some confusion. The aim of this paper is to determine a resources classification for a Cu block model generated by an Ordinary Kriging (OK) and a Concentration-Volume (C-V) fractal modelling based on estimated variance in Eastern Kahang Cu-Mo porphyry deposit, Central Iran. Variography, block modelling and cell declustering for dataset with respect to Cu concentrations as the main target in this deposit were conducted firstly. Then, Cu distribution model was carried out by the OK and estimated variances were calculated for all voxels. According to a C-V log-log plot, three populations for estimated variances were detected. ‘’Measured’’ resources contain voxels with estimated variances lower than 0.08 and more than 7 samples. Estimated variances varied between 0.08 and 0.24 in which more than 3 samples were engaged for estimation of ‘’Indicated’’ resources. ‘’Inferred’’ resources include estimated variances over 0.24 which are located in marginal parts of this deposit. Results derived via this study reveal that the C-V fractal modelling can be used for resources classification in different ore deposits.

References:


  1. Afzal P, Dadashzadeh Ahari H, Rashidnejad Omran N, Aliyari F (2013) Delineation of gold mineralized zones using concentration–volume fractal model in Qolqoleh gold deposit, NW Iran, Ore Geology Reviews 55: 125–133.
    2.
  2. Afzal P, Eskandarnejad Tehrani M, Ghaderi M, Hosseini MR (2016) Delineation of supergene enrichment, hypogene and oxidation zones utilizing staged factor analysis and fractal modeling in Takht-e-Gonbad porphyry deposit, SE Iran, Journal of Geochemical Exploration 161: 119–127.
    3.
  3. Afzal P, Fadakar Alghalandis Y, Khakzad A, Moarefvand P, Rashidnejad Omran N (2011) Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modelling, Journal of Geochemical Exploration 108: 220–232.
    4.
  4. Agterberg FP, Cheng Q, Wright, DF (1993) Fractal modeling of mineral deposits. In: Elbrond, J., Tang, X. (Eds.), 24th APCOM symposium proceeding, Montreal, Canada: 43–53.
    5.
  5. Alavi M (1994) Tectonic of Zagros orogenic belt of Iran: new data and interpretations, Tectonophysics 229: 211-238.
    6.
  6. Alavi M (2004) Regional stratigraphy of the Zagros folded-thrust belt of Iran and its proforeland evolution, American Journal of Science 304: 1-20.
    7.
  7. Arik A (1999) An alternative approach to resource classification. APCOM 1999: 28th International Symposium on Computer Applications in Mineral Industries, Golden, Colorado: 45-53.
    8.
  8. Armstrong M, Boufassa A (1988) Comparing the robustness of ordinary kriging and lognormal kriging: outlier resistance, Mathematical Geology. 20(4): 447–457.
    9.
  9. Asghari O, Madani Esfahani, N (2013) A new approach for the geological risk evaluation of coal resources through a geostatistical simulation Case study: Parvadeh III coal deposit, Arabian Journal of Geosciences 6: 957-970.
    10.
  10. Bai J, Porwal A, Hart C, Ford A, Yu L (2010) Mapping geochemical singularity using multifractal analysis: application to anomaly definition on stream sediments data from Funin Sheet, Yunnan, China, Journal of Geochemical Exploration 104: 1–11.
    11.
  11. Bayraktar H, Turalioglu F.S (2005) A Kriging-based approaches for locating a sampling site-in the assessment of air quality. Stochastic Environmental Research and Risk Assessment 19(4): 301-305.
    12.
  12. Berberian M, King G.C.P (1981) towards a palaeogeography and tectonic evolution of Iran, Canadian Journal of Earth Sciences 18: 210-265.
    13.
  13. Carranza EJM (2009) Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features, Ore Geology Reviews 35: 383–400.
    14.
  14. Carranza EJM (2011) Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values, Journal of Geochemical Exploration 110: 167—185.
    15.
  15. Clark I (1999) A case study in the application of geostatistics to lognormal and quasi lognormal problems, 28th International Symposium on Application of Computers and Operations Research in the Mineral Industry, Colorado School of Mines, CSM: 407–416.
    16.
  16. Committee for Mineral Reserves International Reporting Standards (CRIRSCO) (2013) International reporting template for the public reporting of “Exploration Results, Mineral Resources And Mineral Reserves”, published by International Council on Mining and Metals (ICMM).
    17.
  17. Cressie N (1993) Statistics for spatial data, John Wiley & Sons, New York: 900.
    18.
  18. David M (1970) Geostatistical Ore Reserve Estimation, Elsevier, Amsterdam: 283.
    19.
  19. David M (1988) Handbook of applied advanced geostatistical ore reserve estimation. Developments in Geomathematics 6. Elsevier, Amsterdam. 216 pp.
    20.
  20. Davis B (1987) Uses and abuses of cross validation in geostatistics. Mathematical Geology 19(3): 241-48.
    21.
  21. De Kemp EA, Monecke T, Sheshpart M (2011) 3D GIS as a support for mineral discovery. Geochemistry: Exploration, Environment, Analysis 11(2): 117–128.
    22.
  22. Demetriades A (2014) Basic Considerations: Sampling, the Key for a Successful Applied Geochemical Survey for Mineral Exploration and Environmental Purposes. Reference Module in Earth Systems and Environmental Sciences 15: Analytical Geochemistry/Inorganic INSTR. Analysis: 1–31.
    23.
  23. Deutsch C, Journel AG (1998) GSLIB: Geostatistical Software Library and User's Guide Second Edition. Oxford University Press, New York.
    24.
  24. Emery X (2005) Simple and Ordinary Kriging Multi Gaussian Kriging for estimating recoverable Reserves. Mathematical Geology 37(3): 295-319.
    25.
  25. Emery X (2012) Análisis estadístico de datos. FACULTAD DE CIENCIAS, FISICAS Y MATEMATICAS, UNIVERSIDAD DE CHILE: 30.
    26.
  26. Emery X, González KE (2007) Incorporating the uncertainty in geological boundaries into mineral resources evaluation, Journal of the Geological Society of India 69(1): 29–38.
    27.
  27. Emery X, Ortiz JM, Rodríguez J (2004) Quantifying uncertainty in mineral resources by use of classification schemes and conditional simulations, Mathematical Geology 38(4): 445–564.
    28.
  28. Emery X, Ortiz JM (2011) Two approaches to direct block-support conditional co-simulation. Computers & Geosciences 37(8): 1015–1025.
    29.
  29. Fisher RA, Tippett LHC (1928) Limiting forms of the frequency distribution of the largest or smallest member of a sample. Mathematical Proceedings of the Cambridge Philosophical Society 24(2): 180-190.
    30.
  30. Fletcher WK (1981) Analytical methods in geochemical exploration. G.J.S. Govett (Editor), Handbook of Exploration Geochemistry 1, Elsevier, Amsterdam: 25-46.
    31.
  31. He J, Yao S, Zhang Z, You, G (2013) Complexity and productivity differentiation models of metallogenic indicator elements in rocks and supergene media around Daijiazhuang Pb–Zn deposit in Dangchang County, Gansu Province, Natural Resources Research 22: 19–36.
    32.
  32. Hormozi H, Hormozi E, Rahimi Nohooji N (2012) The Classification of the Applicable Machine Learning Methods in Robot Manipulators. International Journal of Machine Learning and Computing 2(5): 560-563.
    33.
  33. Houlding SW (2000) Practical Geostatistics: Modeling and Spatial Analysis. Germany, Springer, Berlin.
    34.
  34. Hustrulid W, Kuchta M (2006) Open Pit Mine Planning and Design. Taylor & Francis.
    35.
  35. Isaaks E, Srivastava R (1989) An Introduction to Applied Geostatistics. Oxford University Press, New York.
    36.
  36. JORC (2012) Australasian code for reporting of exploration results, mineral resources and ore reserves. Report Prepared by the Joint Ore Reserve Committee of the Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia, the JORC Code; 2012 edition: 44.
    37.
  37. Li C, Ma T, Shi J (2003) Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background, Journal of Geochemical Exploration 77: 167–175.
    38.
  38. Limpert E, Stahel WA, Abbt M (2001) Lognormal distributions across the sciences: keys and clues. Bioscience 51(5): 341–352.
    39.
  39. Luz F, Mateus A, Matos JX, Gonçalves MA (2014) Cu- and Zn-soil anomalies in the NE Border of the South Portuguese Zone (Iberian Variscides, Portugal) identified by multifractal and geostatistical analyses. Natural Resources Research 23: 195–215.
    40.
  40. Maleki Tehrani MA, Asghari O, Emery X (2013) Simulation of mineral grades and classification of mineral resources by using hard and soft conditioning data: application to Sungun porphyry copper deposit, Arabian Journal of Geosciences 6 (10): 3773–3781.
    41.
  41. Mandelbrot BB (1983) The Fractal Geometry of Nature. W.H. Freeman, San Francisco, CA. Updated and Augmented Edition.
    42.
  42. MWSINGA P (2001) Approaching resource classification: general practices and the integration of geostatistics. In: Xie, Wang, Jiang (Eds.), Computer applications in the minerals industries, New York, Lisse, pp.97-104.
    43.
  43. Olea R.A (2007) Declustering of clustered preferential sampling for global histogram and semi variogram inference. Mathematical Geology 39(5): 453–467.
    44.
  44. Reimann C, Filzmoser P (2000) Normal and lognormal data distribution in geochemistry: death of a myth. Consequences for the statistical treatment of geochemical and environmental data, Environmental Geology 39: 1001–1014.
    45.
  45. Richmond A (2002) Two-point declustering for weighting data pairs in experimental variogram calculations, Computers & Geosciences 28: 231–241.
    46.
  46. Sadeghi B, Madani N, Carranza EJM (2015) Combination of geostatistical simulation and fractal modeling for mineral resource classification. Journal of Geochemical Exploration 149: 59–73.
    47.
  47. Shahbeik Sh, Afzal P, Moarefvand P, Qumarsy M (2014) Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. Case study: Dardevey iron ore deposit, NE Iran, Arabian Journal of Geosciences 7(9): 3693–3704.
    48.
  48. Silva DSF, Boisvert JB (2014) Mineral resource classification: a comparison of new and existing techniques. Journal of the Southern African Institute of Mining and Metallurgy 114(3): 265-273.
    49.
  49. Sinclair AJ, Blackwell GH (2002) Applied mineral inventory estimation. Cambridge University Press, New York.
    50.
  50. Soltani Mohammadi S, Hezarkhani A, Tercan E (2012) Optimally Locating Additional Drill Holes in Three Dimensions Using Grade and Simulated Annealing, Journal of Geological Society of India 80: 700-706.
    51.
  51. Tabatabaei S. H, Asadi Haroni H (2006) Geochemical characteristics of Gor Gor Cu-Mo porphyry system, 25th Iranian symposium on geosciences, Geological survey of Iran: 60.
    52.
  52. Thompson M, Howarth S.R (1978) A new approach to the estimation of analytical precision, Journal of Geochemical Exploration 9: 23-30.
    53.
  53. Ver Hoef JM, Cressie N (1993) Multivariable spatial prediction. Mathematical Geology 25(2):219-240.
    54.
  54. Wang G, Pang Z, Boisvert JB, 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.
    55.
  55. Wang G, Zhang S, Yan C, Song Y, Sun Y, Li D, Xu F (2011a) Mineral potential targeting and resource assessment based on 3D geological modeling in Luanchuan region, China, Computers & Geosciences 37: 1976–1988.
    56.
  56. Wang G, Zhang S, Yan C, Xu G, Ma M, Li K, Feng Y (2011b) Application of the multifractal singular value decomposition for delineating geophysical anomalies associated with molybdenum occurrences in the Luanchuan ore field (China), Journal of Applied Geophysics 86: 109–119.
    57.
  57. Wilson CE, Aydin A, Durlofsky LJ, Boucher A, Brownlow D (2011) Use of outcrop observations, geostatistical analysis, and flow simulation to investigate structural controls on secondary hydrocarbon migration in the Anacacho limestone, Uvalde, Texas, AAPG Bulletin 95(7): 1181–1206.
    58.
  58. Yasrebi AB (2014) Determination of an Ultimate Pit Limit Utilising Fractal Modelling to Optimise NPV, Published PhD Thesis, University of Exeter Publisher, Exeter, UK.
    59.
  59. Yasrebi AB, Afzal P, Wetherelt A, Foster P, Esfahanipour R (2013) Correlation between geology and concentration-volume fractal models: significance for Cu and Mo mineralized zones separation in the Kahang porphyry deposit (Central Iran), Geologica Carpathica 64: 153–163.
    60.
  60. Vural A, Erdogan M (2014) Soil Geochemistry Study at Old Gumushane Kirkpavli Alteration Area, Gumushane, NE Black Sea, Gümüşhane University, Journal of Science and Technology Institute 4(1): 1-15.
    61.
  61. Vural, A (2018) Evaluation of Soil Geochemistry Data of Canca (Gümüşhane-Ne Turkey) By Processing Inverse Distance Weighting and Kriging Interpolation Methods-First Findings. Bulletin Of The Mineral Research and Exploration, doi: 10.19111/bulletinofmre.430531
    62.
  62. Yamamoto JK (2000) An alternative measure of the reliability of ordinary kriging estimates. Mathematical Geology 32(4): 489-509.
    63.
  63. Yasrebi AB, Hezarkhani A, Afzal P (2017) Application of Present Value-Volume (PV-V) and NPV-Cumulative Total Ore (NPV-CTO) fractal modelling for mining strategy selection, Resources Policy 53: 384-393.
    64.
  64. Yasrebi A.B, Wetherelt A, Foster P, Afzal P, Kaveh Ahangaran D (2015) Determination of an optimum voxel size based on statistical methods in the Kahang Cu porphyry deposit, Central Iran, Journal of Mining and Metallurgy 51A(1): 21–28.
    65.

Related articles

The rights to this website are owned by the Raimag Press Management System.
Copyright © 2021-2024

Home| Login| About Us| Contact Us|
[فارسی] [العربية] [fa] [ar]