Journal of Central South University

, Volume 25, Issue 2, pp 342–356 | Cite as

Quadratic investigation of geochemical distribution by backward elimination approach at Glojeh epithermal Au(Ag)-polymetallic mineralization, NW Iran



The correspondence analysis will describe elemental association accompanying an indicator samples. This analysis indicates strong mineralization of Ag, As, Pb, Te, Mo, Au, Zn and to a lesser extent S, W, Cu at Glojeh polymetallic mineralization, NW Iran. This work proposes a backward elimination approach (BEA) that quantitatively predicts the Au concentration from main effects (X), quadratic terms (X2) and the first order interaction (X i ×X j ) of Ag, Cu, Pb, and Zn by initialization, order reduction and validation of model. BEA is done based on the quadratic model (QM), and it was eliminated to reduced quadratic model (RQM) by removing insignificant predictors. During the QM optimization process, overall convergence trend of R2, R2(adj) and R2(pred) is obvious, corresponding to increase in the R2(pred) and decrease of R2. The RQM consisted of (threshold value, Cu, Ag×Cu, Pb×Zn, and Ag2–Pb2) and (Pb, Ag×Cu, Ag×Pb, Cu×Zn, Pb×Zn, and Ag2) as main predictors of optimized model according to 288 and 679 litho-samples in trenches and boreholes, respectively. Due to the strong genetic effects with Au mineralization, Pb, Ag2, and Ag×Pb are important predictors in boreholes RQM, while the threshold value is known as an important predictor in the trenches model. The RQMs R2(pred) equal 74.90% and 60.62% which are verified by R2 equal to 73.9% and 60.9% in the trenches and boreholes validation group, respectively.

Key words

correspondence analysis first order interaction reduced quadratic model (RQM) optimized model order reduction and validation strong genetic effects 

后向消元法对伊朗西北地区 Glojeh 超热 Au(Ag)-多金属矿地球化学元素分布的二次元分析


本研究对指示样品中的元素组合进行描述。 结果表明: 在伊朗西北地区 Glojeh 多金属矿中, Ag, As, Pb, Te, Mo, Au 和 Zn 发生强烈矿化, 而 S, W 和 Cu 矿化程度较低。 本文采用后向消元法通过初始化, 降阶和模型验证对 Au 浓度的主效应 (X) 和二次项 (X2) 以及 Ag, Cu, Pb 和 Zn 的一阶交互作用(X i ×X j ) 进行定量预测。 后向消元法是基于二次多项式模型完成的, 通过去除不重要的指示变量进行消元而得到简化二次多项式模型。 在二次多项式优化过程中, R2(pred)增加而 R2 减小, R2, R2(adj) 和R2(pred)都具有明显的收敛趋势。 基于 288 个沟槽和 679 个钻孔岩石样品的预测结果表明: 简化二 次多项式模型包含阈值变量(Cu, Ag×Cu, Pb×Zn 和 Ag 2–Pb2) 和主指示变量 (Pb, Ag×Cu, Cu×Zn, Pb×Zn 和 Ag2)。 由于 Au 矿化具有强烈的遗传效应, Pb, Ag2 和 Ag×Pb 为沟槽样品简化二次多项式模型的重要指示变量, 而阈值变量为钻孔样品模型的重要指示变量。验证组沟槽样品和钻孔样品简化二次多项式模型的R2(pred)分别为74.9%和 60.62%, R2 分别为 73.9%和 60.9%。


相关性分析 一阶交互作用 简化二次多项式模型 优化模型 降阶和验证 强烈遗传效应 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



We would also like to thank Mr. Fattahi and Mr. Hemmati for their help in organizing this data. Finally, and more formally, we would like to acknowledge the support of the IMIDRO (Iranian Mines and Mining Industries Development & Renovation Organization) for our research.


  1. [1]
    BORRADAILE G J. Statistics of earth science data: Their distribution in time, space and orientation [M]. Berlin: Springer Science & Business Media, 2013.MATHGoogle Scholar
  2. [2]
    GRAHAM M W, MILLER D J. Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection [J]. IEEE Transactions on Signal Processing, 2006, 54: 1289–1303.CrossRefMATHGoogle Scholar
  3. [3]
    SEN Z. Spatial modeling principles in earth sciences [M]. Berlin: Springer Science & Business Media, 2009.CrossRefGoogle Scholar
  4. [4]
    ABDUL-WAHAB S A, BAKHEIT C S, AL-ALAWI S M. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations [J]. Environmental Modelling & Software. 2005, 20: 1263–1271.CrossRefGoogle Scholar
  5. [5]
    HESSAMI M, GACHON P, OUARDA T B, ST-HILAIRE A. Automated regression-based statistical downscaling tool [J]. Environmental Modelling & Software, 2008, 23: 813–834.CrossRefGoogle Scholar
  6. [6]
    LI S, ZHAO Z, MIAOMIAO X, WANG Y. Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression [J]. Environmental Modelling & Software, 2010, 25: 1789–1800.CrossRefGoogle Scholar
  7. [7]
    MILLER D J, BROWNING J. A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled/unlabeled data sets [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25: 1468–1483.CrossRefGoogle Scholar
  8. [8]
    CHUN Y, GRIFFITH D A, LEE M, SINHA P. Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters [J]. Journal of Geographical Systems, 2016, 18: 67–85.CrossRefGoogle Scholar
  9. [9]
    CORDELL H J, CLAYTON D G. A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: Application to HLA in type 1 diabetes [J]. The American Journal of Human Genetics, 2002, 70: 124–141.CrossRefGoogle Scholar
  10. [10]
    GRANIAN H, TABATABAEI S H, ASADI H H, CARRANZA E J M. Multivariate regression analysis of lithogeochemical data to model subsurface mineralization: A case study from the Sari Gunay epithermal gold deposit, NW Iran [J]. Journal of Geochemical Exploration, 2015, 148: 249–258.CrossRefGoogle Scholar
  11. [11]
    PASANDIDEH S H R, NIAKI S T A, FAR M H. Optimization of vendor managed inventory of multiproduct EPQ model with multiple constraints using genetic algorithm [J]. The International Journal of Advanced Manufacturing Technology, 2014, 71: 365–376.CrossRefGoogle Scholar
  12. [12]
    AZADI T E, ALMASGANJ F. Using backward elimination with a new model order reduction algorithm to select best double mixture model for document clustering [J]. Expert Systems with Applications, 2009, 36: 10485–10493.CrossRefGoogle Scholar
  13. [13]
    FIGUEIREDO M A T, JAIN A K. Unsupervised learning of finite mixture models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24: 381–396.CrossRefGoogle Scholar
  14. [14]
    MYERS R H, MONTGOMERY D C, ANDERSON-COOk C M. Response surface methodology: Process and product optimization using designed experiments [M]. Hoboken, NJ: John Wiley & Sons, 2016.MATHGoogle Scholar
  15. [15]
    MOHAMMADI R, MOHAMMADIFAR M A, MORTAZAVIAN A M, ROUHI M, GHASEMI J B, DELSHADIAN Z. Extraction optimization of pepsin-soluble collagen from eggshell membrane by response surface methodology (RSM) [J]. Food Chemistry, 2016, 190: 186–193.CrossRefGoogle Scholar
  16. [16]
    SAMAL A R, MOHANTY M K, FIFAREK R H. Backward elimination procedure for a predictive model of gold concentration [J]. Journal of Geochemical Exploration, 2008, 97: 69–82.CrossRefGoogle Scholar
  17. [17]
    FAHRMEIR L, KNEIB T, LANG S, MARX B. Regression: Models, methods and applications [M]. Berlin: Springer Science & Business Media, 2013.CrossRefMATHGoogle Scholar
  18. [18]
    GRANIAN H, TABATABAEI S H, ASADI H H, CARRANZA E J M. Application of discriminant analysis and support vector machine in mapping gold potential areas for further drilling in the Sari-Gunay Gold Deposit, NW Iran [J]. Natural Resources Research, 2016, 25: 145–159.CrossRefGoogle Scholar
  19. [19]
    YADAV V, MUELLER K L, MICHALAK A M. A backward elimination discrete optimization algorithm for model selection in spatio-temporal regression models [J]. Environmental Modelling & Software, 2013, 42: 88–98.CrossRefGoogle Scholar
  20. [20]
    EVRENDILEK G A, AVSAR Y K, EVRENDILEK F. Modelling stochastic variability and uncertainty in aroma active compounds of PEF-treated peach nectar as a function of physical and sensory properties, and treatment time [J]. Food Chemistry, 2016, 190: 634–642.CrossRefGoogle Scholar
  21. [21]
    PARDOE I. Applied regression modeling: A business approach [M]. Hoboken, NJ: John Wiley & Sons, 2012.CrossRefMATHGoogle Scholar
  22. [22]
    MEHRABI B, SIANI M G, AZIZI H. The genesis of the epithermal gold mineralization at North Glojeh veins, NW Iran [J]. IJSAR, 2014, 15: 479–497.Google Scholar
  23. [23]
    CHINNASAMY S S, UKEN R, REINHARDT J, SELBY D, JOHNSON S. Pressure, temperature, and timing of mineralization of the sedimentary rock-hosted orogenic gold deposit at Klipwal, southeastern Kaapvaal Craton, South Africa [J]. Mineralium Deposita, 2015, 50: 739–766.CrossRefGoogle Scholar
  24. [24]
    GRANCEA L, BAILLY L, LEROY J, BANKS D, MARCOUX E, MILESI J, CUNEY M, ANDRE A, ISTVAN D, FABRE C. Fluid evolution in the Baia Mare epithermal gold/polymetallic district, Inner Carpathians, Romania [J]. Mineralium Deposita, 2002, 37: 630–647.CrossRefGoogle Scholar
  25. [25]
    MARTINZ-ABAD I, CEPEDAL A, ARIAS D, FUERTESFUENTE M. The Au–As (Ag–Pb–Zn–Cu–Sb) veindisseminated deposit of Arcos (Lugo, NW Spain): Mineral paragenesis, hydrothermal alteration and implications in invisible gold deposition [J]. Journal of Geochemical Exploration, 2015, 151: 1–16.CrossRefGoogle Scholar
  26. [26]
    ABDI H, WILLIAMS L J, VALENTIN D. Multiple factor analysis: Principal component analysis for multi-table and multi-block data sets [J]. Computational Statistics, 2013, 5: 149–179.CrossRefGoogle Scholar
  27. [27]
    GOLESTAN F D, HEZARKHANI A, ZARE M. Interpretation of the sources of radioactive elements and relationship between them by using multivariate analyses in anzali wetland area [J]. Geoinformatics & Geostatistics: An Overview, 2013, 1(4): 1–10.Google Scholar
  28. [28]
    ROSHANI P, MOKHTARI A R, TABATABAIE S H. Objective based geochemical anomaly detection— application of discriminant function analysis in anomaly delineation in the Kuh Panj porphyry Cu mineralization (Iran) [J]. Journal of Geochemical Exploration, 2013, 130: 65–73.CrossRefGoogle Scholar
  29. [29]
    DIDAY E, NOIRHOMME-FRAITURE M. Symbolic data analysis and the SODAS software [M]. Hoboken, NJ: Wiley Online Library, 2008.MATHGoogle Scholar
  30. [30]
    GLENNIE KW. Cretaceous tectonic evolution of Arabia's eastern plate margin: a tale of two oceans [M]//Middle East models of Jurassic/Cretaceous carbonate systems. SEPM (Society for Sedimentary Geology), Spec. Publ. 2000, 69: 9–20.Google Scholar
  31. [31]
    MOHAJJEL M, FERGUSSON C. Jurassic to Cenozoic tectonics of the Zagros Orogen in northwestern Iran [J]. International Geology Review, 2014, 56: 263–287.CrossRefGoogle Scholar
  32. [32]
    RICHARDS J P. Tectonic, magmatic, and metallogenic evolution of the Tethyan orogen: From subduction to collision [J]. Ore Geology Reviews, 2015, 70: 323–345.CrossRefGoogle Scholar
  33. [33]
    VERDEL C, WERNICKE B P, HASSANZADEH J, GUEST B. A Paleogene extensional arc flare-up in Iran [J]. Tectonics, 2011, 30: TC3008.Google Scholar
  34. [34]
    YANG Z, HOU Z, WHITE N C, CHANG Z, LI Z, SONG Y. Geology of the post-collisional porphyry copper–molybdenum deposit at Qulong, Tibet [J]. Ore Geology Reviews, 2009, 36: 133–159.CrossRefGoogle Scholar
  35. [35]
    AGARD P, OMRANI J, JOLIVET L, MOUTHEREAU F. Convergence history across Zagros (Iran): Constraints from collisional and earlier deformation [J]. International Journal of Earth Sciences, 2005, 94: 401–419.CrossRefGoogle Scholar
  36. [36]
    AZIZI H, ASAHARA Y, MEHRABI B, CHUNG S L. Geochronological and geochemical constraints on the petrogenesis of high-K granite from the Suffi abad area, Sanandaj-Sirjan Zone, NW Iran [J]. Chemie der Erde-Geochemistry, 2011, 71: 363–376.CrossRefGoogle Scholar
  37. [37]
    ALIANI F, MAANIJOU M, SABOURI Z, SEPAHI A A. Petrology, geochemistry and geotectonic environment of the Alvand Intrusive Complex, Hamedan, Iran [J]. Chemie der Erde-Geochemistry, 2012, 72: 363–383.CrossRefGoogle Scholar
  38. [38]
    GOLONKA J. Plate tectonic evolution of the southern margin of Eurasia in the Mesozoic and Cenozoic [J]. Tectonophysics, 2004, 381: 235–373.CrossRefGoogle Scholar
  39. [39]
    SARKARINEJAD K. The role of the zagros suture on three dimensional deformation pattern in eghlid-deh bid area of Iran [J]. Journal of Sciences, Islamic Republic of Iran, 2010, 21(2): 155–167.Google Scholar
  40. [40]
    GHASEMI A, TALBOT C. A new tectonic scenario for the Sanandaj–Sirjan Zone (Iran) [J]. Journal of Asian Earth Sciences, 2006, 26: 683–693.CrossRefGoogle Scholar
  41. [41]
    DARABI-GOLESTAN F, GHAVAMI-RIABI R, KHALOKAKAIE R, ASADI-HARONI H, SEYEDRAHIMI-NYARAGH M. Interpretation of lithogeochemical and geophysical data to identify the buried mineralized area in Cu-Au porphyry of Dalli-Northern Hill [J]. Arabian Journal of Geosciences, 2013, 6: 4499–4509.CrossRefGoogle Scholar
  42. [42]
    EFTEKHAR-NEZHAD J N M, VALEH N. Geology of Tarom-Talesh area [R]. Geological Survey of Iran. Note No.16 with Map 1:100 u, 1965: 1–29.Google Scholar
  43. [43]
    MEHRABI B, SIANI M G, GOLDFARB R, AZIZI H, GANEROD M, MARSH E E. Mineral assemblages, fluid evolution, and genesis of polymetallic epithermal veins, Glojeh district, NW Iran [J]. Ore Geology Reviews, 2016, 78: 41–57.CrossRefGoogle Scholar
  44. [44]
    NABAVI M. An introduction to the geology of Iran [R]. Geological survey of Iran, 1976: 110.Google Scholar
  45. [45]
    GHORBANI M. The economic geology of Iran: mineral deposits and natural resources [M]. Berlin: Springer Science & Business Media, 2013.CrossRefGoogle Scholar
  46. [46]
    BAHAJROY M, TAKI S. Study of the mineralization potential of the intrusives around Valis (Tarom-Iran) [J]. Earth Sciences Research Journal, 2014, 18: 123–129.CrossRefGoogle Scholar
  47. [47]
    GHORBANI M. Alborz zone or Alborz geology state and its mineralization potential [C]//1st Conference of Alborz and Caspian Sea Marginal Regions Earth Sciences, Tehran, Iran 2005.Google Scholar
  48. [48]
    FUERTES-FUENTE M, CEPEDAL A, LIMA A, DORIA A, dos ANJOS RIBEIRO M, GUEDES A. The Au-bearing vein system of the Limarinho deposit (northern Portugal): Genetic constraints from Bi-chalcogenides and Bi–Pb–Ag sulfosalts, fluid inclusions and stable isotopes [J]. Ore Geology Reviews, 2016, 72: 213–231.CrossRefGoogle Scholar
  49. [49]
    GROS M, LORAND J P, LUGUET A. Analysis of platinum group elements and gold in geological materials using NiS fire assay and Te coprecipitation; the NiS dissolution step revisited [J]. Chemical Geology, 2002, 185: 179–190.CrossRefGoogle Scholar
  50. [50]
    JUVONEN R, KONTAS E. Comparison of three analytical methods in the determination of gold in six Finnish gold ores, including a study on sample preparation and sampling [J]. Journal of Geochemical Exploration, 1999, 65: 219–229.CrossRefGoogle Scholar
  51. [51]
    KRISHNA H, KUMAR K. Reliability estimation in Lindley distribution with progressively type II right censored sample [J]. Mathematics and Computers in Simulation, 2011, 82: 281–294.MathSciNetCrossRefMATHGoogle Scholar
  52. [52]
    PATINHA C, CORREIA E, da SILVA E F, SIMOES A, REIS P, MORGADO F, FONSECA E C. Definition of geochemical patterns on the soil of Paul de Arzila using correspondence analysis [J]. Journal of Geochemical Exploration, 2008, 98: 34–42.CrossRefGoogle Scholar
  53. [53]
    DARABI-GOLESTAN F, HEZARKHANI A, ZARE M. Assessment of 226Ra, 238U, 232Th, 137Cs and 40K activities from the northern coastline of Oman Sea (water and sediments) [J]. Marine Pollution Bulletin. 2017, 118, 1: 197–205.CrossRefGoogle Scholar
  54. [54]
    DARABI-GOLESTAN F, GHAVAMI RIABI R, MAJLESI MJ, MEMARZADE M, ASADI HAROONI H. Identify and separation of anomall variable using correspondence and discriminant analyses methods at Northern–Dalli area [J]. analytical and Numerical Method in Minning Engineering. 2012, 3: 35–45. (in Persion)Google Scholar
  55. [55]
    DARABI-GOLESTAN F, HEZARKHANI A. Evaluation of elemental mineralization rank using fractal and multivariate techniques and improving the performance by log-ratio transformation [J]. Journal of Geochemical Exploration, 2017. DOI: 10.1016/j.gexp10.2017.09.11Google Scholar
  56. [56]
    MOHAMADI N M, HEZARKKHANI A, SALJOOGHI B S. Separation of a geochemical anomaly from background by fractal and U-statistic methods, a case study: Khooni district, Central Iran [J]. Chemie der Erde-Geochemistry, 2016, 76: 491–499.CrossRefGoogle Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mining and Metallurgical EngineeringAmirkabir University of TechnologyTehranIran

Personalised recommendations