Identifying porphyry-Cu geochemical footprints using local neighborhood statistics in Baft area, Iran

Abstract

Identifying geochemical anomalies related to ore deposition processes facilitates the practice of vectoring toward undiscovered mineral deposit sites. In district-scale exploration studies, analysis of dispersion patterns of ore-forming elements results in more-reliable targets. Therefore, deriving significant geochemical footprints and mapping the ensuing geochemical anomalies are of important issues that lead exploration geologists toward anomaly sources, e.g., mineralization. This paper aims to examine the effectiveness of local relative enrichment index and singularity mapping technique, as two methods of local neighborhood statistics, in the delineation of anomalous areas for further exploration. A data set of element contents obtained from stream sediment samples in Baft area, Iran, therefore was applied to illustrate the procedure proposed. The close relationship between anomalous patterns recognized and known Cu-occurrences demonstrated that the procedures proposed can efficiently model complex dispersion patterns of geochemical anomalies in the study area. The results showed that singularity mapping method is a better technique, compared to local relative enrichment index, to delineate targets for follow-up exploration in the area. We made this comparison because, as pointed out by exploration geochemists, dispersion patterns of geochemical indicators in stream sediments vary in different areas even for the same deposit type. The variety in the dispersion patterns is due to the operation of post-mineralization subsystems, which are affected by local factors such as landscape of the areas under study. Therefore, the effectiveness of the methods should be evaluated in every area for every targeted deposit.

Keywords

local neighborhood statistics / robust principal component analysis / singularity mapping technique / local relative enrichment index / exploration targets

References

[1] Afzal P, Khakzad A, Moarefvand P, Omran N R, Esfandiari B, Alghalandis Y F (2010). Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. J Geochem Explor, 104(1–2): 34–46

[2] Afzal P, Alghalandis Y F, Moarefvand P, Omran N R, Haroni H A (2012). Application of power-spectrum–volume fractal method for detecting hypogene, supergene enrichment, leached and barren zones in Kahang Cu porphyry deposit, Central Iran. J Geochem Explor, 112: 131–138

[3] Afzal P, Mirzaei M, Yousefi M, Adib A, Khalajmasoumi M, Zia Zarifi A, Foster P, Yasrebi A B (2016). Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis. J Afr Earth Sci, 119: 139–149

[4] Afzal P, Yousefi M, Mirzaie M, Ghadiri-Sufi E, Ghasemzadeh S, Daneshvar Saein L (2019). Delineation of podiform-type chromite mineralization using geochemical mineralization prospectivity index and staged factor analysis in Balvard area (SE Iran). J Min Environ, 10(3): 705–715

[5] Aghazadeh M, Hou Z, Badrzadeh Z, Zhou L (2015). Temporal–spatial distribution and tectonic setting of porphyry copper deposits in Iran: constraints from zircon U–Pb and molybdenite Re–Os geochrono-logy. Ore Geol Rev, 70: 385–406

[6] Agard P, Jolivet L, Vrielynck B, Burov E, Monie P (2007). Plate acceleration: the obduction trigger? Earth Planet Sci Lett, 258(3–4): 428–441

[7] Agterberg F P, Bonham-Carter G F (2005). Measuring the performance of mineral-potential maps. Nat Resour Res, 14(1): 1–17

[8] Aitchison J (1986). The Statistical Analysis of Compositional Data. London: Chapman and Hall.

[9] Aitchison J, Barcelo-Vidal C, Martín-Fernandez J, Pawlowsky-Glahn V (2000). Logratio analysis and compositional distance. Math Geol, 32(3): 271–275

[10] Allègre J C, Lewin E (1995). Scaling laws and geochemical distributions. Earth Planet Sci Lett, 132(1–4): 1–13

[11] Aliyari F, Afzal P, Harati H, Zengqian H (2020). Geology, mineralogy, ore fluid characteristics, and ⁴⁰Ar/³⁹Ar geochronology of the Kahang Cu-(Mo) porphyry deposit, Urumieh-Dokhtar Magmatic Arc, Central Iran. Ore Geol Rev, 116: 103238

[12] Ayati F, Yavuz F, Asadi H, Richards J P, Jourdan F (2013). Petrology and geochemistry of calc–alkaline volcanic and subvolcanic rocks, Dalli porphyry copper–gold deposit, Markazi Province, Iran. Int Geol Rev, 55(2): 158–184

[13] 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. J Geochem Explor, 104(1–2): 1–11

[14] Boomeri M, Nakashima K, Lentz D R (2009). The Miduk porphyry Cu deposit, Kerman, Iran: a geochemical analysis of the potassic zone including halogen element systematics related to Cu mineralization processes. J Geochem Explor, 103(1): 17–29

[15] Bonham-Carter G F (1994). Geographic Information Systems for Geoscientists, Modelling with GIS. Pergamon: Oxford

[16] Buccianti A (2015). The FOREGS repository: modelling variability in stream water on a continental scale revising classical diagrams from CoDA (compositional data analysis) perspective. J Geochem Explor, 154: 94–104

[17] Buccianti A, Lima A, Albanese S, De Vivo B (2018). Measuring the change under compositional data analysis (CoDA): insight on the dynamics of geochemical systems. J Geochem Explor, 189: 100–108

[18] Carranza E J M (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Handbook of Exploration and Environmental Geochemistry, 11

[19] Carranza E J M (2009). Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Comput Geosci, 35(10): 2032–2046

[20] Carranza E J M (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. J Geochem Explor, 110(2): 167–185

[21] Carranza E J M, Hale M (1997). A catchment basin approach to the analysis of reconnaissance geochemical-geological data from Albay Province, Philippines. J Geochem Explor, 60(2): 157–171

[22] Chaffee M A, Hill R H, Sutley S J, Watterson J R (1981). Regional geochemical studies in the Patagonia Mountains, Santa Cruz County, Arizona. J Geochem Explor, 14: 135–153

[23] Chen Y, Lu L, Li X (2014). Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. J Geochem Explor, 140: 56–63

[24] Cheng Q (1999). Spatial and scaling modelling for geochemical anomaly separation. J Geochem Explor, 65(3): 175–194

[25] Cheng Q (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

[26] Cheng Q, Agterberg F P (2009). Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Comput Geosci, 35(2): 234–244

[27] Cheng Q, Agterberg F P, Ballantyne S B (1994). The separation of geochemical anomalies from background by fractal methods. J Geochem Explor, 51(2): 109–130

[28] Cheng Q, Agterberg F P, Bonham-Carter G F (1996). A spatial analysis method for geochemical anomaly separation. J Geochem Explor, 56(3): 183–195

[29] Cheng Q, Xu Y, Grunsky E (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Nat Resour Res, 9(1): 43–52

[30] Cheng Q, Jing L, Panahi A (2006). Principal component analysis with optimum order sample correlation coefficient for image enhancement. Int J Remote Sens, 27(16): 3387–3401

[31] Cheng Q, Bonham-Carter G, Wang W, Zhang S, Li W, Qinglin X (2011). A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Comput Geosci, 37(5): 662–669

[32] Cohen D R, Kelley D L, Anand R, Coker W B (2010). Major advances in exploration geochemistry, 1998–2007. Geochem Explor Environ Anal, 10(1): 3–16

[33] Darabi-Golestan F, Hezarkhani A (2019). Applied statistical functions and multivariate analysis of geochemical compositional data to evaluate mineralization in Glojeh polymetallic deposit, NW Iran. Front Earth Sci-PRC, 13(1): 229–246

[34] Andrada de Palomera P, van Ruitenbeek F J A, Carranza E J M (2015). Prospectivity for epithermal gold–silver deposits in the Deseado Massif, Argentina. Ore Geol Rev, 71: 484–501

[35] Rocha W F C, Nogueira R, Baptista da Silva G E, Queiroz S M, Sarmanho G F (2013). A comparison of three procedures for robust PCA of experimental results of the homogeneity test of a new sodium diclofenac candidate certified reference material. Microchem J, 109: 112–116

[36] Dimitrijevic M D (1973). Geology of Kerman Region. Geological Survey of Iran Report YU/52

[37] Egozcue J J, Pawlowsky-Glahn V, Mateu-Figueras G, Barceloó-Vidal C (2003). Isometric logratio transformations for compositional data analysis. Math Geol, 35(3): 279–300

[38] Filzmoser P, Hron K (2008). Outlier detection for compositional data using robust methods. Math Geosci, 40(3): 233–248

[39] Filzmoser P, Hron K (2009). Correlation analysis for compositional data. Math Geosci, 41(8): 905–919

[40] Filzmoser P, Hron K, Reimann C (2009a). Principal component analysis for compositional data with outliers. Environmetrics, 20(6): 621–632

[41] Filzmoser P, Hron K, Reimann C, Garrett R (2009b). Robust factor analysis for compositional data. Comput Geosci, 35(9): 1854–1861

[42] Ghasemzadeh S, Maghsoudi A, Yousefi M, Mihalasky M J (2019a). Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and C-A fractal modeling. Ore Geol Rev, 113: 103115

[43] Ghasemzadeh S, Maghsoudi A, Yousefi M (2019b). Application of geometric average approach for Cu-porphyry prospectivity mapping in the Baft area, Kerman. J Geosci (Prague), 29: 123–130

[44] Gong Q, Li J, Xiang Y, Liu R, Wu X, Yan T, Chen J, Li R, Tong Y (2018). Determination and classification of geochemical anomalies based on backgrounds and cutoff grades of trace elements: a case study in South Nanling Range, China. J Geochem Explor, 194: 44–51

[45] Grunsky E C, Drew L J, Sutphin D M (2009). Process recognition in multi-element soil and stream-sediment geochemical data. Appl Geochem, 24(8): 1602–1616

[46] Halter W E, Bain N, Becker K, Heinrich C A, Landtwing M, VonQuadt A, Clark A H, Sasso A M, Bissig T, Tosdal R M (2004). From andesitic volcanism to the formation of a porphyry Cu-Au mineralizing magma chamber: the Farallo’n Negro Volcanic Complex, northwestern Argentina. J Volcanol Geotherm Res, 136(1–2): 1–30

[47] Harris J R, Wilkinson L, Grunsky E C (2000). Effective use and interpretation of lithogeochemical data in regional mineral exploration programs: application of Geographic Information Systems (GIS) technology. Ore Geol Rev, 16(3–4): 107–143

[48] He J Z, Yao S Z, Zhang Z P, You G J (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. Nat Resour Res, 22(1): 19–36

[49] Hezarkhani A (2006). Petrology of the intrusive rocks within the Sungun Porphyry Copper Deposit, Azerbaijan, Iran. J Asian Earth Sci, 27(3): 326–340

[50] Hezarkhani A, Williams-Jones A E (1998). Controls of alteration and mineralization in the Sungun porphyry copper deposit, Iran; evidence from fluid inclusions and stable isotopes. Econ Geol, 93(5): 651–670

[51] Hengl T (2006). Finding the right pixel size. Comput Geosci, 32(9): 1283–1298

[52] Hurst H E (1951). Long-term storage capacity of reservoirs. Trans Am Soc Civ Eng, 116: 770–808

[53] Jamali H (2017). The behavior of rare-earth elements, zirconium and hafnium during magma evolution and their application in determining mineralized magmatic suites in subduction zones: constraints from the Cenozoic belts of Iran. Ore Geol Rev, 81: 270–279

[54] John D A, Ayuso R A, Barton M D, Blakely R J, Bodnar R J, Dilles J H, Gray F, Graybeal F T, Mars J C, McPhee D K, Seal R R, Taylor R D, Vikre P G (2010). Porphyry Copper Deposit Model: Chapter B in Mineral Deposit Models for Resource Assessment. Reston: U.S. Geological Survey

[55] Jolliffe I (2002). Principal Component Analysis and Factor Analysis. Principal Component Analysis. New York: Springer

[56] Kirkwood C, Cave M, Beamish D, Grebby S, Ferreira A (2016). A machine learning approach to geochemical mapping. J Geochem Explor, 167: 49–61

[57] Kreuzer O P, Yousefi M, Nykänen V (2020). Introduction to the special issue on spatial modelling and analysis of ore forming processes in mineral exploration targeting. Ore Geol Rev, 119: 103391

[58] Li C, Ma T, Shi J (2003). Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. J Geochem Explor, 77(2–3): 167–175

[59] Macklin M G, Ridgway J, Passmore D G, Rumsby B T (1994). The use of overbank sediment for geochemical mapping and contamination assessment: results from selected English and Welsh floodplains. Appl Geochem, 9(6): 689–700

[60] Mandelbrot B B, Wallis J R (1969). Some long-run properties of geographysical records. Water Resour Res, 5(2): 321–340

[61] Mirzaie A, Bafti S S, Derakhshani R (2015). Fault control on Cu mineralization in the Kerman porphyry copper belt, SE Iran: a fractal analysis. Ore Geol Rev, 71: 237–247

[62] Mohajjel M, Fergusson C L (2000). Dextral transpression in Late Cretaceous continental collision, Sanandaj–Sirjan zone, western Iran. J Struct Geol, 22(8): 1125–1139

[63] Moon C J (1999). Towards a quantitative model of downstream dilution of point source geochemical anomalies. J Geochem Explor, 65(2): 111–132

[64] Moghadam M C, Tahmasbi Z, Ahmadi-Khalaji A, Santos J F (2018). Petrogenesis of Rabor-Lalehzar magmatic rocks (SE Iran): constraints from whole rock chemistry and Sr-Ndisotopes. Geochemistry, 78(1): 58–77

[65] Niktabar S M, Moradian A, Ahmadipour H, Santos J F, Mendes M H (2015). Petrogenesis of the Lalezar granitoid intrusions (Kerman Province-Iran). Journal of Sciences, 26(4): 333–348

[66] Parsa M, Maghsoudi A, Yousefi M, Sadeghi M (2016). Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. J Afr Earth Sci, 114: 228–241

[67] Parsa M, Maghsoudi A, Carranza E J M, Yousefi M (2017 a). Enhancement and mapping of weak multivariate stream sediment geochemical anomalies in Ahar Area, NW Iran. Nat Resour Res, 26(4): 443–455

[68] Parsa M, Maghsoudi A, Yousefi M (2017 b). An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets. Int J Appl Earth Obs Geoinf, 58: 157–167

[69] Parsa M, Maghsoudi A, Yousefi M (2018a). A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits. Nat Resour Res, 27(1): 15–28

[70] Parsa M, Maghsoudi A, Yousefi M (2018b). Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district. NW Iran. Ore Geol Rev, 92: 97–112

[71] Pirajno F (2010). Intracontinental strike-slip faults, associated magmatism, mineral systems and mantle dynamics: examples from NW China and Altay-Sayan (Siberia). J Geodyn, 50(3–4): 325–346

[72] Pison G, Rousseeuw P J, Filzmoser P, Croux C (2003). Robust factor analysis. J Multivariate Anal, 84(1): 145–172

[73] Peytcheva I, von Quadt A, Neubauer F, Frank M, Nedialkov R, Heinrich C, Strashimirov S (2009). U–Pb dating, Hf-isotope characteristics and trace-REE-patterns of zircons from Medet porphyry copper deposit, Bulgaria: implications for timing, duration and sources of ore-bearing magmatism. Miner Petrol, 96(1–2): 19–41

[74] Qu X, Hou Z, Zaw K, Li Y (2007). Characteristics and genesis of Gangdese porphyry copper deposits in the southern Tibetan Plateau: Preliminary geochemical and geochronological results. Ore Geol Rev, 31(1–4): 205–223

[75] 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. Environ. Geol., 39(9): 1001–1014

[76] Reimann C, Filzmoser P, Garrett R G (2002). Factor analysis applied to regional geochemical data: problems and possibilities. Appl Geochem, 17(3): 185–206

[77] Rezaei-Kahkhaei M, Galindo C, Pankhurst R J, Esmaeily D (2011). Magmatic differentiation in the calc-alkaline Khalkhab–Neshveh pluton, Central Iran. J Asian Earth Sci, 42(3): 499–514

[78] Rousseeuw P J, Driessen K V (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3): 212–223

[79] Shafiei B, Haschke M, Shahabpour J (2009). Recycling of orogenic arc crust triggers porphyry Cu mineralization in Kerman Cenozoic arc rocks, southeastern Iran. Miner Depos, 44(3): 265–283

[80] Shafiei B (2010). Lead isotope signatures of the igneous rocks and porphyry copper deposits from the Kerman Cenozoic magmatic arc (SE Iran), and their magmatic-metallogenetic implications. Ore Geol Rev, 38(1–2): 27–36

[81] Soltani F, Moarefvand P, Alinia F, Afzal P (2019). Characterization of rare earth elements by coupling multivariate analysis, factor analysis, and geostatistical simulation: case-study of Gazestan deposit, central Iran. J Min Environ, 10(4): 929–945

[82] Sillitoe R H (1972). A plate tectonic model for the origin of porphyry copper deposits. Econ Geol, 67(2): 184–197

[83] Sillitoe R H (2010). Porphyry copper systems. Econ Geol, 105(1): 3–41

[84] Singer D A, Berger V I, Moring B C (2005). Porphyry Copper Deposits of the World: Database, Maps, Grade and Tonnage Models. Reston: US Department of the Interior, US Geological Survey,1005–1060

[85] Spadoni M (2006). Geochemical mapping using a geomorphologic approach based on catchments. J Geochem Explor, 90(3): 183–196

[86] Spadoni M, Voltaggio M, Cavarretta G (2005). Recognition of areas of anomalous concentration of potentially hazardous elements by means of a subcatchment-based discriminant analysis of stream sediments. J Geochem Explor, 87(3): 83–91

[87] Srdic A, Dimitrijevic M N, Cvetic S, Dimitrijevic M D (1972). Geological Map of Baft (1: 100,000). Teheran: Geological Survey of Iran Publication

[88] Templ M, Hron K, Filzmoser P (2011). robCompositions: an R-package for robust statistical analysis of compositional data. In: Pawlowsky-Glahn V, Buccianti A, eds. Compositional Data Analysis: Theory and Applications. Chichester: Wiley, 341–355

[89] Tian M, Wang X, Nie L, Zhang C (2018). Recognition of geochemical anomalies based on geographically weighted regression: a case study across the boundary areas of China and Mongolia. J Geochem Explor, 190: 381–389

[90] Thompson M, Howarth R J (1976). Duplicate analysis in geochemical practice. Part 1: theoretical approach and estimation of analytical reproducibility. Analyst (Lond), 101(1206): 690–698

[91] Wang H, Zuo R (2015). A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies. J Geochem Explor, 155: 84–90

[92] Wang H, Cheng Q, Zuo R (2015). Quantifying the spatial characteristics of geochemical patterns via GIS-based geographically weighted statistics. J Geochem Explor, 157: 110–119

[93] Wang J, Zuo R (2016). An extended local gap statistic for identifying geochemical anomalies. J Geochem Explor, 164: 86–93

[94] Wang J, Zuo R, Caers J (2017). Discovering geochemical patterns by factor-based cluster analysis. J Geochem Explor, 181: 106–115

[95] Wang J, Zhou Y, Xiao F (2020). Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: a case study from Ag-Pb-Zn deposits in north-western Zhejiang, China. Appl Geochem: 104679

[96] Wang Z, Dong Y, Zuo R (2019). Mapping geochemical anomalies related to Fe–polymetallic mineralization using the maximum margin metric learning method. Ore Geol Rev, 107: 258–265

[97] Xiao F, Chen J, Zhang Z, Wang C, Wu G, Agterberg F P (2012). Singularity mapping and spatially weighted principal component analysis to identify geochemical anomalies associated with Ag and Pb-Zn polymetallic mineralization in northwest Zhejiang, China. J Geochem Explor, 122: 90–100

[98] Xiao F, Wang K, Hou W, Erten O (2020). Identifying geochemical anomaly through spatially anisotropic singularity mapping: a case study from silver-gold deposit in Pangxidong district, SE China. J Geochem Explor, 210: 106453

[99] Xie X, Wang X, Zhang Q, Zhou G, Cheng H, Liu D, Cheng Z, Xu S (2008). Multi-scale geochemical mapping in China. Geochem Explor Environ Anal, 8(3–4): 333–341

[100] Xie S, Cheng Q, Xing X, Bao Z, Chen Z (2010). Geochemical multifractal distribution patterns in sediments from ordered streams. Geoderma, 160(1): 36–46

[101] Xiong Y, Zuo R, Wang K, Wang J (2018). Identification of geochemical anomalies via local RX anomaly detector. J Geochem Explor, 189: 64–71

[102] Xiong Y, Zuo R (2020). Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine1. Comput Geosci, 140: 104484

[103] Yang Z, Hou Z, White N C, Chang Z, Li Z, Song Y (2009). Geology of the post-collisional porphyry copper–molybdenum deposit at Qulong, Tibet. Ore Geol Rev, 36(1–3): 133–159

[104] Yilmaz H (2003a). Exploration at the Kuscayiri Au (Cu) prospect and its implications for porphyry-related mineralization in western Turkey. J Geochem Explor, 77(2–3): 133–150

[105] Yilmaz H (2003b). Geochemical exploration for gold in western Turkey: success and failure. J Geochem Explor, 80(1): 117–135

[106] Yousefi M (2017a). Recognition of an enhanced multi-element geochemical signature of porphyry copper deposits for vectoring into mineralized zones and delimiting exploration targets in Jiroft area, SE Iran. Ore Geol Rev, 83: 200–214

[107] Yousefi M (2017b). Analysis of zoning pattern of geochemical indicators for targeting of porphyry-Cu mineralization: a pixel-based mapping approach. Nat Resour Res, 26(4): 429–441

[108] Yousefi M, Carranza E J M (2015). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Comput Geosci, 74: 97–109

[109] Yousefi M, Kamkar-Rouhani A, Carranza E J M (2012). Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. J Geochem Explor, 115: 24–35

[110] Yousefi M, Carranza E J M, Kamkar-Rouhani A (2013). Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling. J Geochem Explor, 128: 88–96

[111] Yousefi M, Kreuzer O P, Nykänen V, Hronsky J M (2019). Exploration information systems-a proposal for the future use of GIS in mineral exploration targeting. Ore Geol Rev, 111: 103005

[112] Yousefi M, Nykänen V (2017). Introduction to the special issue: GIS-based mineral potential targeting. J Afr Earth Sci (Paris), 128: 1–4

[113] Yu X, Xiao F, Zhou Y, Wang Y, Wang K (2019). Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify Ag-Au-Pb-Zn polymetallic mineralization associated geochemical anomaly in Pangxidong district. J Geochem Explor, 203: 87–95

[114] Zarasvandi A, Rezaei M, Sadeghi M, Lentz D, Adelpour M, Pourkaseb H (2015). Rare earth element signatures of economic and sub-economic porphyry copper systems in Urumieh–Dokhtar Magmatic Arc (UDMA), Iran. Ore Geol Rev, 70: 407–423

[115] Zhao J, Wang W, Dong L, Yang W, Cheng Q (2012). Application of geochemical anomaly identification methods in mapping of intermediate and felsic igneous rocks in eastern Tianshan, China. J Geochem Explor, 122: 81–89

[116] Zhou S, Zhou K, Cui Y, Wang J, Ding J (2015). Exploratory data analysis and singularity mapping in geochemical anomaly identification in Karamay, Xinjiang, China. J Geochem Explor, 154: 171–179

[117] Zhao J, Chen S, Zuo R (2016). Identifying geochemical anomalies associated with Au–Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China. J Geochem Explor, 164: 54–64

[118] Zhang C, Jordan C, Higgins A (2007). Using neighbourhood statistics and GIS to quantify and visualize spatial variation in geochemical variables: an example using Ni concentrations in the top soils of Northern Ireland. Geoderma, 137(3–4): 466–476

[119] Zhou S, Zhou K, Wang J, Yang G, Wang S (2018). Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies. Front Earth Sci, 12(3): 491–505

[120] Zheng Y, Sun X, Gao S, Wang C, Zhao Z, Wu S, Li J, Wu X (2014). Analysis of stream sediment data for exploring the Zhunuo porphyry Cu deposit, southern Tibet. J Geochem Explor, 143: 19–30

[121] Zuo R (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). J Geochem Explor, 111(1–2): 13–22

[122] Zuo R (2012). Exploring the effects of cell size in geochemical mapping. J Geochem Explor, 112: 357–367

[123] Zuo R (2014). Identification of weak geochemical anomalies using robust neighborhood statistics coupled with GIS in covered areas. J Geochem Explor, 136: 93–101

[124] Zuo R (2017). Machine learning of mineralization-related geochemical anomalies: a review of potential methods. Nat Resour Res, 26(4): 457–464

[125] Zuo R, Xiong Y (2020). Geodata science and geochemical mapping. J Geochem Explor, 209: 106431

[126] Zuo R, Cheng Q, Agterberg F P, Xia Q (2009a). Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. J Geochem Explor, 101(3): 225–235

[127] Zuo R, Cheng Q, Xia Q (2009b). Application of fractal models to characterization of vertical distribution of geochemical element concentration. J Geochem Explor, 102(1): 37–43

[128] Zuo R, Xia Q, Wang H (2013). Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Appl Geochem, 28: 202–211

[129] Zuo R, Wang J (2016). Fractal/multifractal modeling of geochemical data: a review. J Geochem Explor, 164: 33–41

Acknowledgments

The authors thank editor-in-chief and associate editors for journal of Frontiers of Earth Science for handling this work. The authors would thank five anonymous reviewers for their constructive comments and suggestions.

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