Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Earth Sci.    2019, Vol. 13 Issue (1) : 229-246    https://doi.org/10.1007/s11707-018-0705-0
RESEARCH ARTICLE
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
 Download: PDF(3141 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

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 (tSb×tAl)vein/(tSb×tAl)background 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.
Keywords LINEST and controlling function      optimized reduced model      log-ratio (clr) transformation      K-means cluster      robust factor analysis      pathfinder elements      vein-style Glojeh polymetallic mineralization     
Corresponding Author(s): F DARABI-GOLESTAN   
Just Accepted Date: 06 September 2018   Online First Date: 16 October 2018    Issue Date: 25 January 2019
 Cite this article:   
F DARABI-GOLESTAN,A HEZARKHANI. Applied statistical functions and multivariate analysis of geochemical compositional data to evaluate mineralization in Glojeh polymetallic deposit, NW Iran[J]. Front. Earth Sci., 2019, 13(1): 229-246.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0705-0
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I1/229
Fig.1  Geological map of Glojeh polymetallic veins deposit.
Fig.2  Cross section map of wall and bottom of TR4-5.
Intercept X1 X2 … Xk−1 Xk
Intercept b0 Slope b1 Slope b2 … Slope bk−1 Slope bk
Standard error for
b0 =SE0		 Standard error for b1 =SE1 Standard error for b2=SE2 … Standard error for bk−1=SEk−1 Standard error for bk =SEk
R2 SEy
F Statistic=F Degrees of freedom=df2
Regression SS=SSRa) Residual SS=SSEb)
Tab.1  The matrix of LINEST function results (regression statistics, after Borah et al., 2015)
df? A 0.80
0.20		 0.90
0.10		 0.95
0.05		 0.98
0.02		 0.99
0.01		 0.995
0.005		 0.998
0.002		 0.999
0.001
P
1 3.078 6.314 12.71 31.82 63.66 127.3 318.3 636.6
2 1.886 2.920 4.303 6.965 9.925 14.09 22.33 31.60
3 1.638 2.353 3.182 4.541 5.841 7.453 10.21 12.92
4 1.533 2.132 2.776 3.747 4.604 5.598 7.173 8.610
5 1.476 2.015 2.571 3.365 4.032 4.773 5.893 6.869
6 1.440 1.943 2.447 3.143 3.707 4.317 5.208 5.959
7 1.415 1.895 2.365 2.998 3.499 4.029 4.785 5.408
8 1.397 1.860 2.306 2.896 3.355 3.833 4.501 5.041
9 1.383 1.833 2.262 2.821 3.250 3.690 4.297 4.781
10 1.372 1.812 2.228 2.764 3.169 3.581 4.144 4.587
20 1.325 1.725 2.086 2.528 2.845 3.153 3.552 3.850
30 1.310 1.697 2.042 2.457 2.750 3.030 3.385 3.646
40 1.303 1.684 2.021 2.423 2.704 2.971 3.307 3.551
50 1.299 1.676 2.009 2.403 2.678 2.937 3.261 3.496
60 1.296 1.671 2.000 2.390 2.660 2.915 3.232 3.460
80 1.292 1.664 1.990 2.374 2.639 2.887 3.195 3.416
100 1.290 1.660 1.984 2.364 2.626 2.871 3.174 3.390
120 1.289 1.658 1.980 2.358 2.617 2.860 3.160 3.373
∞ 1.282 1.645 1.960 2.326 2.576 2.807 3.090 3.291
Tab.2  The t-distribution values (two-tailed)
Fig.3  Probability plot of F-distribution function.
Step Cor. df1 df2 FDIST TINV TDIST
of %90		 Elements by minimum t value
Ele. 1 Value Ele. 2 Value
1 0.77 42 95 1.49E?16 1.661 0.99 Se 0.02 Cr 0.11
2 0.77 40 97 1.82E?17 1.661 0.90 Ce 0.12 Bi 0.16
3 0.77 38 99 2.08E?18 1.660 0.88 La 0.15 Zn 0.16
4 0.77 36 101 2.23E?19 1.660 0.88 Zr 0.15 Rb 0.17
5 0.77 34 103 2.21E?20 1.660 0.79 Threshold limit 0.26 Te 0.30
6 0.77 33 105 3.92E?21 1.659 0.72 Ni 0.35 Sc 0.46
7 0.77 31 107 3.95E?22 1.659 0.66 Y 0.45 V 0.51
8 0.77 29 109 3.72E?23 1.659 0.65 Mo 0.45 Li 0.51
9 0.77 27 111 3.45E?24 1.659 0.42 Be 0.80 Al 0.82
10 0.76 25 113 4.9E?25 1.658 0.58 Th 0.56 W 0.98
11 0.76 23 115 5.91E?26 1.658 0.51 Ti 0.65 Fe 1.04
12 0.76 21 117 6.16E?27 1.658 0.29 Sb 1.05 S 1.18
13 0.75 19 119 1.02E?27 1.658 0.56 Ca 0.59 Ba 0.88
14 0.75 17 121 8.06E?29 1.658 0.47 K 0.73 Cd 0.99
15 0.75 15 123 6.94E?30 1.657 0.31 Mg 1.01 Co 1.23
16 0.74 13 125 7.04E?31 1.657 0.23 Na 1.21
17 0.74 12 126 2.54E?31 1.657 0.10 Tl 1.64
18 0.74 11 127 1.58E?31 1.657 0.08 Cu 1.75
19 0.73 10 128 1.14E?31 1.657 0.06 As 1.87
Tab.3  Model optimization in all data of Glojeh deposit by average CL of %90
Element (x) U Sr Sn Pb P Nb Mn Hg Cu As Ag
b(x) 0.326 −0.158 0.270 0.379 −0.285 0.196 −0.198 −0.235 −0.121 0.160 0.293
Se(x) 0.121 0.062 0.116 0.073 0.084 0.063 0.063 0.073 0.069 0.074 0.085
t-value= b(x)/ Se(x) 2.700 2.551 2.322 Max t-value=5.198 3.375 3.099 3.146 3.233 Min t-value=1.750 2.172 3.470
TDIST= p-value 0.008 0.012 0.022 0.000 0.001 0.002 0.002 0.002 0.083 0.032 0.001
Tab.4  The matrix of LINEST function results of BH2N1 at 18th step
Fig.4  Total (SST), explained (SSR), and residual (SSE) variation of BH2N1 from optimized regression line and actual Au values.
Fig.5  The differences between the t-test and F-test for optimizing model in host rock of Glojeh deposit.
Item Cluster 1 Cluster 2 Cluster 3
Number of observations 82 12 44
Within cluster sum of squares 1270.338 493.912 516.369
Average distance from centroid 3.856 6.253 3.283
Maximum distance from centroid 5.677 9.281 6.403
Tab.5  K-means cluster analysis results
Fig.6  Results of robust factor analysis according to clr-transformed data: (a) biplot of the first vs. second loading factors; (b) biplot of the first vs. second loading factors accompanied to scores of samples which categorized in three cluster by K-means clustering method. 12 samples (cluster 2) that have the maximum distance from centroid, indicates the intensity of vein polymetallic mineralization in the Glojeh deposit.
Step R2 df1 df2 F value FDIST TINV Elements by minimum t value
of %95 Ele. 1 Value Ele. 2 Value
1 0.701 42 83 4.653 1.20E?09 1.989 Mn 0.09 Se 0.07
2 0.701 40 85 5.002 2.40E?10 1.988 Na 0.13 Bi 0.11
3 0.701 38 87 5.386 4.50E?11 1.988 Li 0.14 Zr 0.13
4 0.701 36 89 5.811 7.70E?12 1.987 Ce 0.31 Ni 0.17
5 0.701 34 91 6.279 1.30E?12 1.986 Rb 0.4 V 0.34
6 0.7 32 93 6.788 2.10E?13 1.986 La 0.54 Mo 0.42
7 0.698 30 95 7.347 3.40E?14 1.985 Zn 0.74 Y 0.44
8 0.696 28 97 7.953 5.70E?15 1.985 Tl 0.79 Sc 0.78
9 0.693 26 99 8.597 1.10E?15 1.984 U 0.99 Ti 0.53
10 0.689 24 101 9.326 2.00E?16 1.984 K 1.28 Fe 1.18
11 0.681 22 103 10.007 6.20E?17 1.983 Cr 1.24 Sn 1.1
12 0.67 20 105 10.69 2.70E?17 1.983 Cd 1.33 P 0.9
13 0.661 18 107 11.615 8.50E?18 1.982 Be 1.55 Ba 1.35
14 0.649 16 109 12.649 3.50E?18 1.982 Co 1.38
15 0.643 15 110 13.254 2.30E?18 1.982 Cu 1.41
16 0.637 14 111 13.934 1.50E?18 1.982 Sr 1.56
17 0.629 13 112 14.63 1.20E?18 1.981 Th 1.4
18 0.622 12 113 15.554 7.20E?19 1.981 Ca 1.29
19 0.617 11 114 16.721 3.70E?19 1.981 W 1.64
20 0.608 10 115 17.859 3.00E?19 1.981 Threshold limit 1.69
21 0.613 10 116 18.44 8.90E?20 1.981 S 1.54
22 0.605 9 117 19.992 5.90E?20 1.98 Nb 1.76
23 0.596 8 118 21.72 5.30E?20 1.98 As 1.954
24 0.582 7 119 23.716 6.40E?20 1.98 Al 2.15
Tab.6  Model optimization in host rock of Glojeh deposit by CL of 95%
Step Parameter Te Sb Pb Mg Hg As Al Ag
1 bk 0.266 0.143 0.220 −0.238 −0.227 0.152 −0.145 0.285
2 SEk 0.091 0.072 0.085 0.099 0.081 0.078 0.068 0.091
3 R2 and SEy 0.596 0.570
4 F and df 21.720 118
5 SSR and SSE 56.452 38.336
6 t value=ABS(bk /SEk) 2.93 1.98 2.58 2.40 2.79 1.954 2.12 3.12
7 P value= TDIST function 0.004 0.050 0.011 0.013 0.006 0.05 0.036 0.002
Tab.7  The matrix of LINEST function results of BH2N1 after 23 stages
Step R2 df1 df2 FDIST TINV
of 95%		 Element by minimum t value
Element Value
1 0.877 10 1 7.38E–01 12.706 Te 0.072
2 0.877 9 2 4.49E–01 4.303 Zn 0.050
3 0.876 8 3 2.27E–01 3.182 Threshold limit 0.045
4 0.995 8 4 2.71E–04 2.776 Hg 0.445
5 0.995 7 5 2.41E–05 2.571 Ag 1.090
Tab.8  Model optimization in vein of Glojeh deposit by average CL of 83% at 5th step
Element t value ABS (t value) TDIST CL%
Sb 4.173 4.173 0.009 99.1
Pb −1.350 1.350 0.235 76.5
Mg 1.740 1.740 0.142 85.8
Cu 1.322 1.322 0.243 75.7
As 1.741 1.741 0.142 85.8
Al −1.752 1.752 0.140 86.0
Ag 1.087 1.086 0.327 67.3
Tab.9  TDIST value (error) and CL of 95% for elements which presented in vein model
Fig.7  The procedure of modeling the geochemical data in the Glojeh deposit using the proposed algorithm, order of data analysis (a), and a schematic flowchart of modeling and optimization by controlling function (b).
Fig.8  Comparison the t-value of elements in modeling from vein and brecciated zone, host rock and all the data throughout the BH2N1 borehole.
Fig.9  The variation trend of t-values for elements which participate for distinct background, vein and throughout all the BH2N1 borehole models.
1 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
6 J NBlythe, D W Lea (2008). Functions of height and width dimensions in the intertidal mussel, Mytilus californianus. J Shellfish Res, 27(2): 385–392
https://doi.org/10.2983/0730-8000(2008)27[385:FOHAWD]2.0.CO;2
7 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
25 PFilzmoser, K Hron (2008). Outlier detection for compositional data using robust methods. Math Geosci, 40(3): 233–248
https://doi.org/10.1007/s11004-007-9141-5
26 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
54 TOyman, F Minareci, ÖPişkin (2003). Efemcukuru B-rich epithermal gold deposit (Izmir, Turkey). Ore Geol Rev, 23(1–2): 35–53
https://doi.org/10.1016/S0169-1368(03)00013-1
55 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
56 VPawlowsky-Glahn, J Egozcue (2006). Compositional data and their analysis: an introduction. Geol Soc Lond Spec Publ, 264(1): 1–10
https://doi.org/10.1144/GSL.SP.2006.264.01.01
57 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
79 YZhu, F An, JTan (2011). Geochemistry of hydrothermal gold deposits: a review. Geoscience Frontiers, 2(3): 367–374
https://doi.org/10.1016/j.gsf.2011.05.006
80 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
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed