Applied statistical functions and multivariate analysis of geochemical compositional data to evaluate mineralization in Glojeh polymetallic deposit, NW Iran
F DARABI-GOLESTAN(), A HEZARKHANI
Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran
Various genesis of epithermal veins as well as host rock cause complication in the modeling process. Thus LINEST and controlling function were applied to improve the accuracy and the quality of the model. The LINEST is a model which is based on multiple linear regression and refers to a branch of applied statistics. This method concerns directly to the application of t-test (TINV and TDIST to analyses of variables in the model) and F-test (FDIST, F-statistic to compare different models) analysis. Backward elimination technique is applied to reduce the number of variables in the model through all the borehole data. After 18 steps, an optimized reduced model (ORM) was constructed and ranked in order of importance as Pb>Ag>P>Hg>Mn>Nb>U>Sr>Sn>As>Cu, with the lowest confidence level (CL) of 92% for Cu. According to the epigenetic vein genesis of Glojeh polymetallic deposit, determination of spatial patterns and elemental associations accompanied by anomaly separation were conducted by K-means cluster and robust factor analysis method based on centered log-ratio (clr) transformed data. Therefore, 12 samples (cluster 2) with the maximum distance from centroid, indicates the intensity of vein polymetallic mineralization in the deposit. In addition, an ORM for vein population was extracted for Sb>Al>As>Mg>Pb>Cu>Ag elements with the R2 up to 0.99. On the other hand, after 23 steps of optimization process at the host rock population, an ORM was conducted by Ag>Te>Hg>Pb>Mg>Al>Sb>As represented in descending order of t-values. It revealed that Te and Hg can be considered as pathfinder elements for Au at the host rock. Based on the ORMs at each population Ag, Pb, and As were often associated with Au mineralization. The concentration ratio of as an enrichment index can intensify the mineralization detection. Finally, Glojeh deposit was evaluated to be classified as a vein-style Au (Ag, Pb, As)-polymetallic mineralization.
. [J]. Frontiers of Earth Science, 2019, 13(1): 229-246.
F DARABI-GOLESTAN, A HEZARKHANI. Applied statistical functions and multivariate analysis of geochemical compositional data to evaluate mineralization in Glojeh polymetallic deposit, NW Iran. Front. Earth Sci., 2019, 13(1): 229-246.
HAbdi, L J Williams, D Valentin (2013). Multiple factor analysis: principal component analysis for multi-table and multi-block data sets. Comput Stat, 5(2): 149–179 https://doi.org/10.1002/wics.1246
2
AAlmasi, A Jafarirad, HKheyrollahi, MRahimi, PAfzal (2014). Evaluation of structural and geological factors in orogenic gold type mineralisation in the Kervian area, north-west Iran, using airborne geophysical data. Explor Geophys, 45(4): 261–270 https://doi.org/10.1071/EG13053
3
R TAngelo, M S Cringan, D L Chamberlain, A J Stahl, S G Haslouer, C A Goodrich (2007). Residual effects of lead and zinc mining on freshwater mussels in the Spring River Basin (Kansas, Missouri, and Oklahoma, USA). Sci Total Environ, 384(1–3): 467–496 https://doi.org/10.1016/j.scitotenv.2007.05.045
4
F PBierlein, S McKnight (2005). Possible intrusion-related gold systems in the western Lachlan Orogen, southeast Australia. Econ Geol, 100: 385–398
5
C JBise (2013). Modern American Coal Mining: Methods and Applications. Society for Mining, Metallurgy and Exploration
PBorah, M K Singh, S Mahapatra (2015). Estimation of degree-days for different climatic zones of North-East India. Sustainable Cities and Society, 14: 70–81 https://doi.org/10.1016/j.scs.2014.08.001
8
JBreidenbach, R E McRoberts, R Astrup (2016). Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume. Remote Sens Environ, 173: 274–281 https://doi.org/10.1016/j.rse.2015.07.026
9
GBriand, R C Hill (2013). Teaching basic econometric concepts using Monte Carlo simulations in Excel. Int Rev Econ Educ, 12: 60–79 https://doi.org/10.1016/j.iree.2013.04.001
10
ABuccianti, E Grunsky (2014). Compositional data analysis in geochemistry: are we sure to see what really occurs during natural processes? Elsevier
11
QCheng (2007). Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geol Rev, 32(1–2): 314–324 https://doi.org/10.1016/j.oregeorev.2006.10.002
12
C LCiobanu, N J Cook, P G Spry (2006). Telluride and selenide minerals in gold deposits—how and why? Mineral Petrol, 87(3‒4): 163–169 https://doi.org/10.1007/s00710-006-0133-9
13
N JCook, C L Ciobanu (2005). Tellurides in Au deposits: implications for modelling. In: Mao J W, Bierlein F P, eds. Mineral Deposit Research: Meeting the Global Challenge. Proceedings of the 8th Biennial SGA Meeting, Beijing, China, 1387–1390
14
H JCordell, D G Clayton (2002). 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. Am J Hum Genet, 70(1): 124–141 https://doi.org/10.1086/338007
15
FDarabi-Golestan, R Ghavami-Riabi, HAsadi-Harooni (2013). Alteration, zoning model, and mineralogical structure considering lithogeochemical investigation in Northern Dalli Cu–Au porphyry. Arab J Geosci, 6(12): 4821–4831 https://doi.org/10.1007/s12517-012-0689-0
16
FDarabi-Golestan, A Hezarkhani (2016). High precision analysis modeling by backward elimination with attitude on interaction effects on Au (Ag)-polymetallic mineralization of Glojeh, Iran. J Afr Earth Sci, 124: 505–516 https://doi.org/10.1016/j.jafrearsci.2016.09.030
17
FDarabi-Golestan, A Hezarkhani (2017a). Evaluation of elemental mineralization rank using fractal and multivariate techniques and improving the performance by log-ratio transformation. J Geochem Explor, doi: 10.1016/j.gexplo.2017.09.011
18
FDarabi-Golestan, A Hezarkhani (2017b). R- and Q-mode multivariate analysis to sense spatial mineralization rather than uni-elemental fractal modeling in polymetallic vein deposits. Geosystem Engineering, doi: 10.1080/12269328.2017.1407266
19
FDarabi-Golestan, A Hezarkhani, MZare (2017). Assessment of 226Ra, 238U, 232Th, 137Cs and 40K activities from the northern coastline of Oman Sea (water and sediments). Mar Pollut Bull, 118(1‒2): 197–205 https://doi.org/10.1016/j.marpolbul.2017.02.064
29
FDarabi-Golestan, A Hezarkhani, MZare (2014). Interpretation of the sources of radioactive elements and relationship between them by using multivariate analyses in Anzali Wetland Area. Geoinformatics & Geostatistics. An Overview, 1: 4 https://doi.org/10.4172/2327-4581.1000114
20
WDeCoursey (2003). Statistics and Probability for Engineering Applications. New York: Elsevier
21
NDehak, P J Kenny, R Dehak, PDumouchel, POuellet (2011). Front-end factor analysis for speaker verification. IEEE Trans Audio Speech Lang Process, 19(4): 788–798 https://doi.org/10.1109/TASL.2010.2064307
22
MDora, K Randive (2015). Chloritisation along the Thanewasna shear zone, Western Bastar Craton, Central India: its genetic linkage to Cu–Au mineralisation. Ore Geol Rev, 70: 151–172 https://doi.org/10.1016/j.oregeorev.2015.03.018
23
J JEgozcue, V Pawlowsky-Glahn, GMateu-Figueras, CBarcelo-Vidal (2003). Isometric logratio transformations for compositional data analysis. Math Geol, 35(3): 279–300 https://doi.org/10.1023/A:1023818214614
24
DFávaro, S Damatto, EMoreira, BMazzilli, FCampagnoli (2007). Chemical characterization and recent sedimentation rates in sediment cores from Rio Grande reservoir, SP, Brazil. J Radioanal Nucl Chem, 273(2): 451–463 https://doi.org/10.1007/s10967-007-6855-2
PFilzmoser, K Hron, CReimann (2009). Principal component analysis for compositional data with outliers. Environmetrics, 20(6): 621–632 https://doi.org/10.1002/env.966
27
R JFox (1983). Confirmatory Factor Analysis. Wiley Online Library
28
GGarson (2012). Multiple regression (statistical associates blue book series). Asheboro, NC: Statistical Associates Publishers
30
LGrancea, L Bailly, JLeroy, DBanks, EMarcoux, JMilési, MCuney, AAndré, DIstvan, CFabre (2002). Fluid evolution in the Baia Mare epithermal gold/polymetallic district, Inner Carpathians, Romania. Miner Depos, 37(6–7): 630–647 https://doi.org/10.1007/s00126-002-0276-5
31
SGuha, N Mishra (2016). Clustering data streams. In: Garofalakis M, Gehrke J, Rastogi R. Data Stream Management: Processing High-Speed Data Streams. Springer Berlin Heidelberg, 169–187
32
AHamilton, K Campbell, JRowland, PBrowne (2017). The Kohuamuri siliceous sinter as a vector for epithermal mineralisation, Coromandel Volcanic Zone, New Zealand. Miner Depos, 52(2): 181–196 https://doi.org/10.1007/s00126-016-0658-8
33
B RHargreaves, T PMcWilliams (2010). Polynomial trendline function flaws in Microsoft Excel. Comput Stat Data Anal, 54(4): 1190–1196 https://doi.org/10.1016/j.csda.2009.10.020
34
AHezarkhani (2008). Hydrothermal evolution of the Miduk porphyry copper system, Kerman, Iran: a fluid inclusion investigation. Int Geol Rev, 50(7): 665–684 https://doi.org/10.2747/0020-6814.50.7.665
35
THill, P Lewicki (2006). Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. Tulsa: StatSoft, Inc.
36
ZHuang (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowl Discov, 2(3): 283–304 https://doi.org/10.1023/A:1009769707641
37
SJiang, M Liu, JHao, WQian (2015). A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry. Comput Ind Eng, 87: 518–531 https://doi.org/10.1016/j.cie.2015.06.002
38
S MJovic, D M Guido, R Ruiz, G NPáez, I BSchalamuk (2011). Indium distribution and correlations in polymetallic veins from Pingüino deposit, Deseado Massif, Patagonia, Argentina. Geochem Explor Environ Anal, 11(2): 107–115 https://doi.org/10.1144/1467-7873/09-IAGS-013
39
DKaramanis, K Ioannides, KStamoulis (2009). Environmental assessment of natural radionuclides and heavy metals in waters discharged from a lignite-fired power plant. Fuel, 88(10): 2046–2052 https://doi.org/10.1016/j.fuel.2009.02.032
40
M HKutner, C J Nachtsheim, J Neter, WLi (2005). Applied Linear Statistical Models. New York: McGraw-Hill Irwin
41
D TLarose (2006). Data Mining Methods & Models. New York: John Wiley & Sons: 1–223
42
D TLarose (2003). Discovering Knowledge in Data: An Introduction to Data Mining. Hoboken: John Wiley & Sous.,1–223
43
YLiu, Q Cheng, KZhou, QXia, X Wang (2016). Multivariate analysis for geochemical process identification using stream sediment geochemical data: a perspective from compositional data. Geochem J, 50(4): 293–314 https://doi.org/10.2343/geochemj.2.0415
44
JMacQueen (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA, 281–297
45
IMartínez-Abad, ACepedal, DArias, MFuertes-Fuente (2015). The Au–As (Ag–Pb–Zn–Cu–Sb) vein-disseminated deposit of Arcos (Lugo, NW Spain): mineral paragenesis, hydrothermal alteration and implications in invisible gold deposition. J Geochem Explor, 151: 1–16 https://doi.org/10.1016/j.gexplo.2014.11.019
46
BMehrabi, M G Siani, H Azizi (2014). The genesis of the epithermal gold mineralization at North Glojeh veins. NW Iran. IJSAR, 15: 479–497
47
BMehrabi, M G Siani, R Goldfarb, HAzizi, MGanerod, E EMarsh (2016). Mineral assemblages, fluid evolution, and genesis of polymetallic epithermal veins, Glojeh district. NW Iran. Ore Geol Rev, 78: 41–57 https://doi.org/10.1016/j.oregeorev.2016.03.016
48
DMihai, M Mocanu (2015). Statistical considerations on the k-means algorithm. Annals of the University of Craiova-Mathematics and Computer Science Series, 42: 365–373
49
N MMohammadi, AHezarkhani, AMaghsoudi (2018). Application of K-means and PCA approaches to estimation of gold grade in Khooni district (central Iran). Acta Geochimica, 37(1): 102–112 https://doi.org/10.1007/s11631-017-0161-7
50
R HMyers, D C Montgomery, C M Anderson-Cook (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. John Wiley & Sons
51
ANamhata, L Zhang, R MDilmore, SOladyshkin, D VNakles (2017). Modeling changes in pressure due to migration of fluids into the above zone monitoring interval of a geologic carbon storage site. Int J Greenh Gas Control, 56: 30–42 https://doi.org/10.1016/j.ijggc.2016.11.012
52
VNaumov, B Osovetsky (2013). Mercuriferous gold and amalgams in Mesozoic-Cenozoic rocks of the Vyatka-Kama Depression. Lithol Miner Resour, 48(3): 237–253 https://doi.org/10.1134/S0024490213030061
53
P MNude, J M Asigri, S M Yidana, E Arhin, GFoli, J MKutu (2012). Identifying pathfinder elements for gold in multi-element soil geochemical data from the Wa-Lawra belt, northwest Ghana: a multivariate statistical approach. Int J Geosci, 3(01): 62–70 https://doi.org/10.4236/ijg.2012.31008
JParnell, S Spinks, DBellis (2016). Low-temperature concentration of tellurium and gold in continental red bed successions. Terra Nova, 28(3): 221–227 https://doi.org/10.1111/ter.12213
S ARadosavljević, J NStojanović, N SVuković, A S Radosavljević-Mihajlović, V D Kašić (2015). Low-temperature Ni-As-Sb-S mineralization of the Pb (Ag)-Zn deposits within the Rogozna ore field, Serbo-Macedonian Metallogenic Province: ore mineralogy, crystal chemistry and paragenetic relationships. Ore Geol Rev, 65: 213–227 https://doi.org/10.1016/j.oregeorev.2014.09.029
58
VRamasamy, M Sundarrajan, KParamasivam, VMeenakshisundaram, GSuresh (2013). Assessment of spatial distribution and radiological hazardous nature of radionuclides in high background radiation area, Kerala, India. Appl Radiat Isot, 73: 21–31 https://doi.org/10.1016/j.apradiso.2012.11.014
59
FReith, D McPhail, AChristy (2005). Bacillus cereus, gold and associated elements in soil and other regolith samples from Tomakin Park Gold Mine in southeastern New South Wales, Australia. J Geochem Explor, 85(2): 81–98 https://doi.org/10.1016/j.gexplo.2004.11.001
60
DRemenyi, G Onofrei, JEnglish (2011). An introduction to statistics using Microsoft Excel. Academic Conferences and Publishing International Ltd.
61
JRøislien, H Omre (2006). T-distributed random fields: a parametric model for heavy-tailed well-log data. Math Geol, 38(7): 821–849 https://doi.org/10.1007/s11004-006-9050-z
62
A RSamal, M K Mohanty, R H Fifarek (2008). Backward elimination procedure for a predictive model of gold concentration. J Geochem Explor, 97(2–3): 69–82 https://doi.org/10.1016/j.gexplo.2007.11.004
63
ESavazzi, R Reyment (1999). Aspects of Multivariate Statistical Analysis in Geology. Elsevier
64
SSoheily-Khah, A Douzal-Chouakria, EGaussier (2016). Generalized k-means-based clustering for temporal data under weighted and kernel time warp. Pattern Recognit Lett, 75: 63–69 https://doi.org/10.1016/j.patrec.2016.03.007
65
CStanciu (1973). Hydrothermal alteration of Neogene volcanics rocks from ore deposits in Gutai Mountains (East Carpathians). Rev Roum Geol Geophys Geogr Ser Geol, 17: 43–62
66
GSuresh, P Sutharsan, VRamasamy, RVenkatachalapathy (2012). Assessment of spatial distribution and potential ecological risk of the heavy metals in relation to granulometric contents of Veeranam lake sediments, India. Ecotoxicol Environ Saf, 84: 117–124 https://doi.org/10.1016/j.ecoenv.2012.06.027
67
G JSzékely, M LRizzo (2013). The distance correlation t-test of independence in high dimension. J Multivariate Anal, 117: 193–213 https://doi.org/10.1016/j.jmva.2013.02.012
68
MTempl, P Filzmoser, CReimann (2008). Cluster analysis applied to regional geochemical data: problems and possibilities. Appl Geochem, 23(8): 2198–2213 https://doi.org/10.1016/j.apgeochem.2008.03.004
69
CTokatli, E Köse, AÇiçek (2014). Assessment of the effects of large borate deposits on surface water quality by multi statistical approaches: a case study of Seydisuyu Stream (Turkey). Pol J Environ Stud, 23: 1741–1751
70
SVriend, P Van Gaans, JMiddelburg, ADe Nijs (1988). The application of fuzzy c-means cluster analysis and non-linear mapping to geochemical datasets: examples from Portugal. Appl Geochem, 3(2): 213–224 https://doi.org/10.1016/0883-2927(88)90009-1
71
WWang, J Zhao, QCheng (2014). Mapping of Fe mineralization-associated geochemical signatures using logratio transformed stream sediment geochemical data in eastern Tianshan, China. J Geochem Explor, 141: 6–14 https://doi.org/10.1016/j.gexplo.2013.11.008
72
A TYalta (2008). The accuracy of statistical distributions in Microsoft® Excel 2007. Comput Stat Data Anal, 52(10): 4579–4586 https://doi.org/10.1016/j.csda.2008.03.005
73
LYang, Q Wang, XLiu (2015). Correlation between mineralization intensity and fluid–rock reaction in the Xinli gold deposit, Jiaodong Peninsula, China: constraints from petrographic and statistical approaches. Ore Geol Rev, 71: 29–39 https://doi.org/10.1016/j.oregeorev.2015.04.005
74
MYousefi, A Kamkar-Rouhani, E J MCarranza (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochem Explor Environ Anal, 14(1): 45–58 https://doi.org/10.1144/geochem2012-144
75
CZaiontz (2014). Real statistics using Excel.
76
M FZarandi, E H Yazdi (2008). A type-2 fuzzy rule-based expert system model for portfolio selection. Proceeding of The 11th Joint Conference On Information Sciences. Atlantis Press
77
DZhang, Q Cheng, FAgterberg, ZChen (2016). An improved solution of local window parameters setting for local singularity analysis based on Excel VBA batch processing technology. Comput Geosci, 88: 54–66 https://doi.org/10.1016/j.cageo.2015.12.012
78
XZhao, C Xue, D TSymons, ZZhang, HWang (2014). Microgranular enclaves in island-arc andesites: a possible link between known epithermal Au and potential porphyry Cu–Au deposits in the Tulasu ore cluster, western Tianshan, Xinjiang, China. J Asian Earth Sci, 85: 210–223 https://doi.org/10.1016/j.jseaes.2014.01.014
MZiaii, E J M Carranza, M Ziaei (2011). Application of geochemical zonality coefficients in mineral prospectivity mapping. Comput Geosci, 37(12): 1935–1945 https://doi.org/10.1016/j.cageo.2011.05.009