Article A Trace Element Classification Tree for from Oktyabrsk Deposit, Norilsk–Talnakh District, Russia: LA-ICPMS Study

Alexander E. Marfin 1,2,*, Alexei V. Ivanov 1 , Vera D. Abramova 3, Tatiana N. Anziferova 1,3, Tatiana A. Radomskaya 4, Tamara Y. Yakich 5 and Ksenia V. Bestemianova 6

1 Institute of the Earth’s Crust, Siberian Branch of the Russian Academy of Sciences, 664033 Irkutsk, Russia; [email protected] (A.V.I.); [email protected] (T.N.A.) 2 Institute of Experimental Mineralogy, Russian Academy of Sciences, 142432 Chernogolovka, Russia 3 Institute of Geology of Ore Deposits Mineralogy, Petrography, and Geochemistry, Russian Academy of Sciences, 119017 Moscow, Russia; [email protected] 4 A.P. Vinogradov Institute of Geochemistry, Siberian Branch of the Russian Academy of Sciences, 664033 Irkutsk, Russia; [email protected] 5 School of Earth Sciences & Engineering, Division for Geology, Tomsk Polytechnic University, 634050 Tomsk, Russia; [email protected] 6 Faculty of Geology and Geography, National Research Tomsk State University, 634050 Tomsk, Russia; [email protected] * Correspondence: marfi[email protected]

 Received: 13 July 2020; Accepted: 11 August 2020; Published: 14 August 2020 

Abstract: The Oktyabrsk PGE-Cu-Ni deposit is one of the largest resources in the Norilsk–Talnakh ore district, Russia, and it is viewed as an ore giant on a global scale. It contains three types of : massive, disseminated and veinlet-disseminated. The two former ore types were formed by a liquation process, whereas the latter was associated with fluid-induced selective metasomatic replacement of metamorphosed wall rocks. One of the major ore minerals in all ore types is chalcopyrite. In this study, we determined concentrations of trace elements in this using laser ablation inductively coupled plasma mass spectrometry. It appeared that standard geochemical tools, such as plotting the data in the form of diagrams of normalized concentrations, binary and ternary plots, do not allow one to distinguish chalcopyrite from visually and genetically different ore types. In contrast, more advanced statistical methods such as cluster analysis show different groupings of elements for each ore type. Based on the element clustering, a classification tree was suggested, which allowed for the differentiation of massive, disseminated and veinlet-disseminated ore types of the Oktyabrsk deposit by Se, Te, Cd and Pb concentrations in chalcopyrite with a success rate of 86%. The general feature is that chalcopyrite of veinlet-disseminated ore is poorer in these elements compared to chalcopyrite of the two other ore types. Chalcopyrite of massive ore is poorer in Se and Te when compared to chalcopyrite of disseminated ore.

Keywords: Talnakh; Siberian flood basalts; LA-ICPMS; sulfide; chalcopyrite; cluster analysis; classification tree

1. Introduction Chalcopyrite is the main ore mineral of , which is found in a wide number of deposits of various genesis on a global scale [1]. However, unlike [2–11], elemental data for chalcopyrite are limited [1,12–19]. The proposed genetic classifications in these studies were based on a limited number of elements, such as Zn, Cd and Se. In this study, we use laser ablation inductively coupled

Minerals 2020, 10, 716; doi:10.3390/min10080716 www.mdpi.com/journal/minerals Minerals 2020, 10, 716 2 of 15 plasma mass spectrometry (LA-ICPMS) to determine a wide range of trace elements (Ti, Mn, Co, Ni, Zn, Se, Mo, Ag, Cd, Sn, Te, Tl, Bi, Pb) in chalcopyrite from three major types of ore distinguished at the Oktyabrsk deposit in the Norilsk–Talnakh ore district, Russia, namely: massive, disseminated and veinlet-disseminated. The two former ore types are considered to be formed by sulfide–silicate immiscibility (liquation) [20–22], whereas the latter type is likely of contact metamorphic–metasomatic origin [23]. Using standard statistical approaches (calculation of the mean, median, the spread of the data, binary plots) and previously proposed classification schemes based on binary and ternary plots do not allow for the differentiation of the ore types by trace element abundances in chalcopyrite. Therefore, we use an advanced method based on cluster analysis and a classification tree approach, which allowed us to distinguish different ore types with a high success rate. Similar approaches were shown to be successful for the classification of garnets [24,25] and pyrite [11]. When large databases are available, such as in case of garnets [24,25], a meta-analysis may be applied [26]. Our study contributes to the topic of statistical analysis of complex data and provides new information on one of the largest global-scale Cu-Ni-PGE deposits.

2. Geological Background

2.1. Geology of the Oktyabrsk Deposit The Oktyabrsk copper– deposit with Pt-Pd mineralization is confined to the Kharaelakh intrusion (Figure1). Its age of 251.71 0.31 Ma (2σ external error including 238U decay constant) ± was determined using chemical abrasion isotope dilution thermal ionization mass-spectrometry (CA-ID-TIMS) on zircon [27]. According to paleomagnetic data, the Kharaelakh intrusion is slightly older than other ore-bearing intrusions in the Norilsk–Talnakh ore district [28], though the difference in age is too small to be resolved by U-Pb data [27]. The rocks of the Kharaelakh intrusion occur within Devonian sediments, which are represented by terrigenous, carbonate and evaporite sediments. The intrusion falls at a gentle angle to the east, in the direction of the Norilsk–Kharaelakh fault. The average thickness of the Kharaelakh intrusion is 150 m. The intrusion has a pronounced layered structure. The rocks are represented by the following petrographic varieties: contact, taxite, picrite (also named plagiodunite and plagiowehrlite) and gabbrodolerite [20,21,29–33]. A detailed analysis of the geology, petrography, geochemistry, and isotopic characteristics of the intrusion and ores is beyond the scope of this paper and can be found elsewhere [34–47].

2.2. Three Types of Sulfide Ores The following ore types are distinguished at the Oktyabrsk deposit [46]: massive, disseminated and veinlet-disseminated (from bottom to top, Figure1). The massive (rich) copper–nickel ores are found at the lower part of the intrusion, and often are localized within the metasomatic and metamorphic rocks of the lower endo- and exo-contact or even within the Devonian sedimentary rocks of the Mantur and Kureika formations. Massive ore is composed of 70–90% sulfides (, chalcopyrite, , less often , , heazlewoodite, , ). The thickness of the horizon of the massive ores varies from the first tens of centimeters to 54 m. The massive ores are also found in some other parts of the intrusion, where they are represented by small veinlet bodies up to a few meters thick. It is assumed that sulfide melts were squeezed out along tectonic cracks. The following subtypes of the massive ores are distinguished by the ratio of ore minerals: (1) pyrrhotite (Figure2a,b), (2) chalcopyrite (Figure2c), (3) galena–chalcopyrite (talnakhite, bornite), and (4) pentlandite–(heazlewoodite)–bornite–chalcocite. In subordinate quantities, calcite, gypsum, minerals of the chlorite group, , titanomagnetite, and are present as inclusions in the massive ores [45]. Minerals 2020, 10, 716 3 of 15 Minerals 2020, 10, x FOR PEER REVIEW 3 of 16

Figure 1. A schematic geologic map of the studied region (a) and a geologic cross-section of the Figure 1. A schematic geologic map of the studied region (a) and a geologic cross-section of the Oktyabrsk deposit hosted by the Kharaelakh intrusion and its metamorphic aureole (b) based on [48]. Oktyabrsk deposit hosted by the Kharaelakh intrusion and its metamorphic aureole (b) based on Gabbrodolerites of the Kharaelakh intrusion were dated by CA-ID-TIMS on zircon to 251.71 0.31 Ma [48]. Gabbrodolerites of the Kharaelakh intrusion were dated by CA-ID-TIMS on zircon to± 251.71 ± (2σ external error including 238U decay constant) [27]. Coeval, though with much large uncertainty, 0.31 Ma (2 external error including 238U decay constant) [27]. Coeval, though with much large dates were obtained by U-Pb LA-ICPMS on metamorphic minerals of the veinlet-disseminated ores [49]. uncertainty, dates were obtained by U-Pb LA-ICPMS on metamorphic minerals of the veinletThe disseminated-disseminated type ores of[49] ore. is localized sometimes in contact gabbrodolerites and olivine-free gabbrodolerites (Figure2d,e), but more often in picrites and taxtic gabbrodolerites (Figure2f). Sulfides withThe a size disseminated of 0.1–2 mm type are of found ore is in localized interstitial sometimes of silicates in contact and as separategabbrodolerites blobs upand to olivine 40–50- mm.free gabbrodoleritesDisseminated ores (Figure also 2d,e), occur but in taxites more often where in sulfide picrites blobs and taxtic are generally gabbrodolerites larger in (Figure size. 2f). with Veinlet-disseminateda size of 0.1–2 mm oresare found are found in inters in thetitial frontal of partsilicates of the and intrusion as separate and above blobs the up intrusion to 40–50 within mm. Disseminatedthe contact metamorphic ores also occur and in metasomatic taxites where aureole of blobs the Kharaelakhare generally intrusion. larger in size. Ores, which contain up toVeinlet 30–50%-disseminated of sulfides by ores volume, are found are located in the within frontal hornfels part of andthe intrusion metasomatically and above altered the Devonianintrusion withinsediments. the Bycontact sulfide metamorphic minerals, the and veinlet-disseminated metasomatic aureole ores areof (1)the pyrrhotite, Kharaelakh (2) intrusion. chalcopyrite–pyrrhotite, Ores, which contain(3) pyrrhotite–chalcopyrite up to 30–50% of sulfides (Figure by2g–j), volume, (4) pentlandite–chalcopyrite, are located within hornfels (5) millerite–pyrite–chalcopyrite,and metasomatically altered Devonian(6) millerite–bornite–chalcopyrite sediments. By sulfide and minerals, (7) сubanite–chalcopyrite the veinlet-disseminated subtypes. ores are (1) pyrrhotite, (2) chalcopyrite–pyrrhotite, (3) pyrrhotite–chalcopyrite (Figure 2g–j), (4) pentlandite–chalcopyrite, (5) millerite–pyrite–chalcopyrite, (6) millerite–bornite–chalcopyrite and (7) сubanite–chalcopyrite subtypes.

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FigureFigure 2. 2.Studied Studied ore ore types types of the of Oktyabrsk the Oktyabrsk deposit. deposit.(a–c)—massive (a–c)— oresmassive ((a,b)—pyrrhotite ores (a, b— dominated,pyrrhotite (dominated,c)—chalcopyrite c—chalcopyrite dominated); dominated) (d–f)—disseminated; (d–f)—disseminated ores ((d)—in ores contact (d—in gabbrodolerites, contact gabbrodolerites (e)—in, olivine-freee—in olivine gabbrodolerites,-free gabbrodolerites, (f)—in picrites); f—in picrites) (g–j)—veinlet-disseminated; (g–j)—veinlet-disseminated ores (pyrrhotite–chalcopyrite ores (pyrrhotite– subtype)chalcopyrite in carbonatesubtype) in skarns. carbonate In skarns (a–c):. In pyrrhotite a–c: pyrrhotite in groundmass in groundmass with with the emulsionthe emulsion phase phase of chalcopyriteof chalcopyrite is indicative is indicative of coeval of coeval precipitation precipitation (massive (massive texture) texture) at the bottom at the part bottom of the part intrusion. of the Inintrusion. (d–e): pyrrhotite–pentlandite–chalcopyrite In d–e: pyrrhotite–pentlandite–chal dropscopyrite are drops typical are for typical the upper for the contact upper of contact the intrusion. of the Inintrusion. (f): pyrrhotite–pentlandite–chalcopyrite In f: pyrrhotite–pentlandite–chalcopyrite drops of drops the intermediate of the intermediate part of part the intrusion.of the intrusion In (g–. jIn): skarn-likeg–j: skarn- rocks.like rocks.

3.3. Methods

3.1. SEM Analyses 3.1. SEM Analyses Major element analyses and back scattered electron (BSE) images were taken using a TescanVega Major element analyses and back scattered electron (BSE) images were taken using a 3 SBU scanning electron microscope (SEM) equipped with an energy dispersive spectrometer (EDS) TescanVega 3 SBU scanning electron microscope (SEM) equipped with an energy dispersive of Oxford Instruments (Abingdon, UK) with an Aztec-based system of microanalysis. The operating spectrometer (EDS) of Oxford Instruments (Abingdon, UK) with an Aztec-based system of conditions were in the high-vacuum mode (<9 10 3 Pa) at an accelerating voltage of 20 kV with a microanalysis. The operating conditions were× in − the high-vacuum mode (<9 × 10−3 Pa) at an high resolution and a distance of 15 mm. accelerating voltage of 20 kV with a high resolution and a distance of 15 mm. SEM EDS analyses and BSE images were used for the two reasons: first, for selection and a visual SEM EDS analyses and BSE images were used for the two reasons: first, for selection and a controlvisual control of minerals of minerals for the LA-ICPMSfor the LA- studyICPMS and, study second, and, second, for determination for determination of contentof iron usedcontent as an internal standard in LA-ICPMS. An example of representative BSE images of chalcopyrite from used as an internal standard in LA-ICPMS. An example of representative BSE images of chalcopyrite difromfferent different types oftype oress of is ores shown is shown in Figure in Fig3. ure 3.

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Figure 3.3.BSE BSE images images of chalcopyrite of chalcopyrite grains of massive grains (a of–c ), massive disseminated (a–c (),d –f disseminated) and veinlet-disseminated (d–f) and (veinletg–i) ores,-disseminated respectively. (g– Ccp—chalcopyrite,i) ores, respectively Po—pyrrhotite.. Ccp—chalcopyrite, Pentlandite Po—pyrrhotite. is also present Pentlandite as small-size is also flames,present whichas small are-size too flames, small to which be indicated are too small at the to selected be indicated scale. at the selected scale. 3.2. LA-ICPMS Analysis 3.2. LA-ICPMS Analysis The concentrations of 29Si, 55Mn, 59Co, 60Ni, 65Cu, 66Zn, 77Se, 95Mo, 107Ag, 111Cd, 118Sn, 125Te, The concentrations of 29Si, 55Mn, 59Co, 60Ni, 65Cu, 66Zn, 77Se, 95Mo, 107Ag, 111Cd, 118Sn, 125Te, 205Tl, 205Tl, 208Pb, 209Bi in sulfides were determined by LA-ICPMS at the Laboratory of Mineral Analysis of 208Pb, 209Bi in sulfides were determined by LA-ICPMS at the Laboratory of Mineral Analysis of Institute of Geology of Ore Deposits Mineralogy, Petrography, and Geochemistry, Russian Academy Institute of Geology of Ore Deposits Mineralogy, Petrography, and Geochemistry, Russian Academy of Sciences. The LA-ICPMS system consists of a quadrupole mass spectrometer Thermo Xseries2 of Sciences. The LA-ICPMS system consists of a quadrupole mass spectrometer Thermo Xseries2 equipped with a New Wave 213 laser instrument. Selected isotopes do not contain a significant isobaric equipped with a New Wave 213 laser instrument. Selected isotopes do not contain a significant and molecular interference. isobaric and molecular interference. For external calibration, the standard reference materials (SRM) UQAC FeS-1 (University of For external calibration, the standard reference materials (SRM) UQAC FeS-1 (University of Quebec in Chicoutimi, ), GSD-1G and STD GL-3, produced from natural sulfide powder and Quebec in Chicoutimi, Canada), GSD-1G and STD GL-3, produced from natural sulfide powder and doped with trace elements, were used. MASS-1 polymetal sulfide (USGS) was used for verification of doped with trace elements, were used. MASS-1 polymetal sulfide (USGS) was used for verification the results. of the results. LA-ICPMS analyses were conducted using an 80 µm beam diameter for spots and 40–65 µm LA-ICPMS analyses were conducted using an 80 μm beam diameter for spots and 40–65 μm for for lines, a laser frequency of 15 Hz, 5–7 J/cm2 energy density and a 7 µm/s ablation speed for lines. lines, a laser frequency of 15 Hz, 5–7 J/cm2 energy density and a 7 μm/s ablation speed for lines. The The carrier gas consists of a mixture of helium (0.7 L/min) and argon (0.85 L/min). Acquisition time in carrier gas consists of a mixture of helium (0.7 L/min) and argon (0.85 L/min). Acquisition time in a a spot mode was 30 s for the backgrounds, 60 s for the mineral analysis and 30 s of washout. Each line spot mode was 30 s for the backgrounds, 60 s for the mineral analysis and 30 s of washout. Each line profile preceded 30 s of background. The material was then analyzed using the ICPMS operating in a profile preceded 30 s of background. The material was then analyzed using the ICPMS operating in a time-resolved mode using peak jumping and a dwell time of 10 ms/peak per element. time-resolved mode using peak jumping and a dwell time of 10 ms/peak per element. Signal quantification was carried out by Iolite software using iron content in each sulfide Signal quantification was carried out by Iolite software using iron content in each sulfide determined by the SEM-EDS as an internal standard. Minerals in the profiles through intergrowths determined by the SEM-EDS as an internal standard. Minerals in the profiles through intergrowths were calculated separately, avoiding areas of the contacts. In the case of detection of microinclusions in the LA-ICPMS spectra, the corresponding part of a spectrum was cut out off-line. The detection

Minerals 2020, 10, 716 6 of 15 were calculated separately, avoiding areas of the contacts. In the case of detection of microinclusions in Minerals 2020, 10, x FOR PEER REVIEW 6 of 16 the LA-ICPMS spectra, the corresponding part of a spectrum was cut out off-line. The detection limits forlimits LA-ICPMS for LA- analysesICPMS analyses were calculated were calculated as three as sigma three times sigma the times background the background counts for counts the gas for blank. the Silicongas blank. was Silicon monitored was tomonitored exclude silicateto exclude inclusions. silicate inclusions. FigureFigure4 shows4 shows representative representative lines lines through through sulfide sulf mineralside minerals with with the background the background of BSE of images. BSE Itimages. is seen thatIt is seen chalcopyrite that chalcopyrite (Ccp), pentlandite (Ccp), pentlandite (Pn) and (Pn) pyrrhotite and pyrrhotite (Po) are (Po) easily are recognized easily recognized by their correspondingby their corresponding element spectra. element Furthermore, spectra. Further we focusmore, onwe chalcopyrite focus on chalcopyrite data only, becausedata only, this because mineral occursthis mineral in all ore occur typess in inall a ore significant types in amount.a significant amount.

FigureFigure 4. 4. AnAn example example of of two two representative representative laser laser ablation lines on sulfidesulfide mineralsminerals.. LA LA-ICPMS-ICPMS elementalelemental spectra spectra are are shown shown by by diff differenterent colors. colors. The The LA-ICPMS LA-ICP dataMS data are superimposed are superimposed on BSE on images BSE takenimages after taken the after laser the ablation laser ablation analyses. analyse Ccp—chalcopyrite,s. Ccp—chalcopyrite, Po—pyrrhotite, Po—pyrrhotite Pn—pentlandite., Pn—pentlandite.

3.3.3.3. RR PackagePackage WeWe used used free free RStudio RStudio softwaresoftware asas aa mediummedium for R language. The The used used libraries libraries were: were: “ggplot2” “ggplot2” forfor making making the the violin violin plots, plots, “cluster”“cluster” forfor clustercluster analysis, “pvclust “pvclust”” for for construction construction of of classification classification trees,trees, and and “psych” “psych” for for constructing constructing correlation correlation tables tables and and binary binary plots plots https: https://cran.r//cran.r-project.org-project.org (R (R for Windowsfor Windows v.R-4.0.2). v.R-4.0.2).

4.4. Results Results ConcentrationsConcentrations of of 14 14 trace trace elements elements were were determined determined in in chalcopyrite chalcopyrite of of veinlet-disseminated veinlet-disseminated (46 analyses),(46 analyses), disseminated disseminated (25 analyses) (25 analyses) and massive and massive (16 analyses) (16 analyses) ores (Table ores ( S1).Table Chalcopyrite S1). Chalcopyrite of massive of oresmassive (32 analyses) ores (32 was analyses) previously was analyzedpreviously by analyzed LA-ICPMS by for LA trace-ICPMS elements for trace by [ 16elements]. Our new by [16] data. Our are in goodnew agreementdata are in with good previously agreement published with previously results. However,published forresults. further However, internal for consistency, further internal we used onlyconsistency, our new data. we use Ford eachonly element our new of every data. oreFor type, each violin element plots of were every constructed ore type, (Figureviolin 5 plots, Figure were S1). constructedElement concentrations(Figure 5, Figure of chalcopyriteS1). of different ore types fully overlap. This is an unexpected result, becauseElement massive concentrations and disseminated of ores chalcopyrite are related of to different the process ore of silicate–sulfide types fully overlap.immiscibility This [is20 – an22 ], whereasunexpected veinlet-disseminated result, because ores massive are associated and disseminated with the fluid-induced ores are related selective to the metasomatic process of replacement silicate– ofsulfide metamorphosed immiscibility wall rocks [20–22], [23]. Thus,whereas the source veinlet of-disseminated metals of the two ores ore are types associated is expected with within the the magmafluid-induced itself, whereas selective veinlet-disseminated metasomatic replacement ores should of bemetamorphosed affected by host wall rocks. rocks [23]. Thus, the source of metals of the two ore types is expected within the itself, whereas veinlet-disseminated ores should be affected by host rocks.

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Figure 5. An example of violin plots for Se, Te, Cd, and Pb concentrations determined in chalcopyrite of Figure 5. An example of violin plots for Se, Te, Cd, and Pb concentrations determined in chalcopyrite the three ore types of the Oktyabrsk deposit. An explanation of the violin plot is in the low right-hand of the three ore types of the Oktyabrsk deposit. An explanation of the violin plot is in the low of the figure. Violin plots for other elements are shown in the Supplementary Figure S1. Colors are the right-hand of the figure. Violin plots for other elements are shown in the Supplementary Figure S1. same as in Figure1b for the ore types. Colors are the same as in Figure 1b for the ore types. Different elements may show coherent behavior depending on the process of crystallization of chalcopyrite.Different elements To test may this, show we appliedcoherent cluster behavior analysis, depending whose on outcomethe process is shownof crystallization in Figure6 of. Chalcopyritechalcopyrite. of To veinlet-disseminated, test this, we applied disseminated cluster analysis, and massive whose ores outcome is characterized is shown by four, in Fig twoure and 6. threeChalcopyrite clusters of elementsveinlet-disseminated, with high similarity, disseminated respectively. and massive Some ores element is characterized clustering canby four, be easily two explainedand three by clusters co-crystallization of elements of with particular high similarity, mineral phases, respectively. though Some some element spurious clustering clustering can could be alsoeasily occur explained (Table1 by). Forco-crystallization example, it is diofffi particularcult to explain mineral Ti-Sn, phases, Ti-Mn-Mo though and some Mn-Ti-Zn-Ag spurious clustering clusters forcould chalcopyrite also occur of (Table veinlet-disseminated, 1). For example, disseminatedit is difficult to and explain massive Ti-Sn, ores, Ti- respectively.Mn-Mo and Since,Mn-Ti- clusterZn-Ag analysisclusters isfor merely chalcopyrite a statistical of veinlet tool,-disseminated, it inevitably produces disseminated some and clusters massive that mayores, haverespectively. no geological Since, meaning.cluster analysis Other clusters,is merely such a statistical as Bi-Se-Te, tool Zn-Cd, it inevitably and Co-Ni produc in chalcopyritees some clusters of veinlet-disseminated that may have no ores,geological Ag-Co-Ni-Sn-Bi-Tl-Pb-Zn-Cd-Se-Te meaning. Other clusters, such inchalcopyrite as Bi-Se-Te, of Zndisseminated-Cd and Co ores-Ni and in Co-Ni-Mo-Pb chalcopyrite in of chalcopyriteveinlet-disseminated of massive ores, ores, Ag are-Co explained-Ni-Sn-Bi- byTl- thePb-Zn presence-Cd-Se- ofTe mineral in chalcopyrite phases observed of disseminated in these ores ore types,and Co which-Ni-Mo co-crystallized-Pb in chalcopyrite with chalcopyrite of massive (Tableores, are1). Seexplained and Te formby the clusters presence in all of threemineral ore phases types becauseobserved these in these two elements ore types, have which similar co- geochemicalcrystallized behavior. with chalcopyrite (Table 1). Se and Te form clusters in all three ore types because these two elements have similar geochemical behavior.

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Figure 6. Cluster analysis of trace elements in chalcopyrite of veinlet-disseminated, disseminated and Figure 6. Cluster analysis of trace elements in chalcopyrite of veinlet-disseminated, disseminated massive ores of the Oktyabrsk deposit. Colors underline elements with high similarity determined by and massive ores of the Oktyabrsk deposit. Colors underline elements with high similarity cluster analysis. determined by cluster analysis. For constructing a classification tree based on element clustering, we picked up several elements For constructing a classification tree based on element clustering, we picked up several manually, which are the most common elements in all ore types. It appeared that using a limited elements manually, which are the most common elements in all ore types. It appeared that using a number of elements, namely, Se, Te, Cd and Pb, allows satisfactory discrimination of the ore types with limited number of elements, namely, Se, Te, Cd and Pb, allows satisfactory discrimination of the ore a success rate of 86%. The suggested classification tree is shown in Figure7. types with a success rate of 86%. The suggested classification tree is shown in Figure 7.

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Figure 7. Suggested classification tree. Colors are chosen arbitrary to match the colors of ore types in Figure 7. Suggested classification tree. Colors are chosen arbitrary to match the colors of ore types in Figure1b. Figure 1b. Table 1. Results and interpretation of element clustering. Table 1. Results and interpretation of element clustering. Type of Ores Groups of Elements Interpretation Type of Ores Groups of Elements Interpretation Co-crystallization of Bi-Te phases with Bi-Se-Te Co-crystallization of Bi-Te phases with Bi-Se-Te chalcopyrite, isomorphism of Se and Te chalcopyrite, isomorphism of Se and Te TiTi-Sn-Sn SpuriousSpurious clustering?clustering? VeinletVeinlet-disseminated-disseminated Co-crystallizationCo-crystallization ofof sphaleritesphalerite withwith oreore ZnZn-Cd-Cd chalcopyrite,chalcopyrite, isomorphismisomorphism ofof CdCd andand ZnZn Co-crystallizationCo-crystallization ofof pentlanditepentlandite andand CoCo-Ni-Ni Co-pentlanditeCo-pentlandite withwith chalcopyritechalcopyrite TiTi-Mn-Mo-Mn-Mo SpuriousSpurious clustering?clustering? Disseminated ore Initial crystallization of chalcopyrite from Disseminated ore Ag-Co-Ni-Sn-Bi-Tl-Pb-Zn-Cd-Se-Te Initial crystallization of chalcopyrite from Ag-Co-Ni-Sn-Bi-Tl-Pb-Zn-Cd-Se-Te intermediate solid solution (ISS) intermediate solid solution (ISS) Mn-Ti-Zn-Ag Rutile, ilmenite and Ti-magnetite Mn-Ti-Zn-AgSe-Te Isomorphism Rutile, ilmenite of Se and and Te Ti-magnetite in chalcopyrite Massive ore Massive ore Se-Te IsomorphismCo-crystallization of Se of and pentlandite, Te in chalcopyrite galena, Co-Ni-Mo-Pb Co-crystallizationisomorphism ofof pentlandite,Pb, Mo and galena,Co Co-Ni-Mo-Pb isomorphism of Pb, Mo and Co 5. Discussion 5. DiscussionA standard approach for the interpretation of multielement data includes (a) plotting of element concentrationsA standard normalized approach for to the a referenceinterpretation composition of multielement (chondrite, data primitiveincludes (a) mantle, plotting mid of- elementoceanic concentrationsridge basalt (MORB), normalized etc.) to (e.g. a reference, [1,50]) compositionand (b) using (chondrite, binary and primitive ternary mantle, plots mid-oceanic(e.g., [51]). ridge Such basaltdiagrams (MORB), are used etc.) to (e.g.,visualize [1,50 ])differences and (b) using and binarysimilarities and ternarybetween plots different (e.g., data [51]).sets Such and diagrams to draw aregenetic used co tonclusions. visualize However, differences in andthe case similarities of our study between, none diff oferent these datasets diagrams and show to draw difference genetics conclusions.between chalcopyr However,ite of in the the three case di offferent our study, ore types none of of the these Oktyabrsk diagrams deposit. showdi Asfferences an example, between we chalcopyriteshow a MORB of the sulfide three- dinormalizedfferent ore types diagram of the (Fig Oktyabrskure 8) and deposit. a set As of an binary example, plots we with show calculated a MORB sulfide-normalizedPearson correlation diagramcoefficients (Figure (Fig8ure) and 9). aIt set was of binaryalready plots obvious with from calculated the violin Pearson plots correlation (Figure 5, coeFigurefficients S1) that (Figure such9 standard). It was alreadyplots may obvious not work. from the violin plots (Figure5, Figure S1) that such standard plots may not work.

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MineralsMinerals 20202020,, 1010,, xx FORFOR PEPEERER REVIEWREVIEW 1010 ofof 1616

FigureFigure 8. 8. 8. Mid MidMid ocean ocean ocean ridge ridge ridge basalt basalt basalt(MORB) ( (MORBMORB sulfide-normalized)) sulfidesulfide--normalizednormalized concentration concentration concentration diagram for diagram diagram chalcopyrite for for chalcopyriteofchalcopyrite the three di ofofff erent thethe threethree ore types differentdifferent of the oreore Oktyabrsk typestypes ofof thethe deposit. OktyabrskOktyabrsk Normalized deposit.deposit. values NormalizedNormalized of MORB valuesvalues sulfide ofof MORB areMORB the sulfidemeansulfide value areare thethe according mmeanean valuevalue to [52 accordingaccording]. toto [52][52]..

FigureFigure 9.9. AA fragmentfragment of ofof Pearson PearsonPearson correlation correlationcorrelation matrix matrixmatrix and andand corresponding correspondingcorresponding binary binarybinary plots plotsplots for forfor chalcopyrite chalcopyritechalcopyrite of oftheof thethe three threethree diff differenterentdifferent ore typesoreore typestypes of the ofof Oktyabrsk thethe OktyabrskOktyabrsk deposit. deposit.deposit. The full TheThe correlation fullfull correlationcorrelation matrix matrixmatrix is not providedisis notnot providedprovided due to dueitsdue size. toto itsits However, size.size. However,However, other elements other other elementselements show the showshow same thethe feature samesame that featurefeature the three thatthat the ditheff erentthreethree ore differentdifferent types cannotoreore typestypes be cannotdistinguishedcannot bebe distinguisheddistinguished from each otherfromfrom each byeach using otherother binary byby usingusing plots, binarybinary though plotsplots some,, thoughthough elements somesome show elementselements stronger showshow correlations strongstrongerer correlforcorrel chalcopyriteationsations forfor of chalcopyrite chalcopyrite disseminated of of ore disseminated disseminated (e.g., Zn-Cd, Zn-Pb) ore ore (e.g. (e.g. when,, ZnZn compared--Cd,Cd, Zn Zn to--Pb)Pb) chalcopyrite whenwhen comparedcompared of the other to to ore types. Colors in the caption (red, green, yellow) correspond to symbols of the same colors. chalcopyritechalcopyrite ofof thethe otherother ororee typestypes.. ColorsColors inin thethe captioncaption (red,(red, green,green, yellow)yellow) correspondcorrespond toto symbolssymbols ofof thethe samesame colors.colors. Previously, George et al. [1] suggested a genetic classification of chalcopyrite based on Zn and Cd concentrations (Figure 10). A stronger correlation coefficient for chalcopyrite of disseminated ore Previously,Previously, GeorgeGeorge etet al.al. [1][1] suggestedsuggested aa geneticgenetic classificationclassification ofof chalcopyritechalcopyrite basedbased onon ZnZn andand compared to the other two ore types gives hope to the idea that a Cd vs. Zn diagram could distinguish CdCd concentrationsconcentrations (Fig(Figureure 10).10). AA strstrongeronger correlationcorrelation coefficientcoefficient forfor chalcopyritechalcopyrite ofof disseminateddisseminated oreore the disseminated ore type at least. However, all three ore types overlap on this diagram. Our data comparedcompared to to the the other other two two ore ore types types give givess hopehope toto the the idea idea thatthat aa CdCd vs vs.. ZnZn diagram diagram c couldould distinguishdistinguish thethe disseminateddisseminated oreore typetype atat leastleast.. However,However, allall threethree oreore typestypes overlapoverlap onon thisthis diagram.diagram. OurOur datadata spreadspread overover mostmost ofof thethe fieldsfields withwith thethe majoritymajority ofof analyanalysseses locatedlocated inin thethe fieldfield ofof highhigh crystallizacrystallizattionion temperaturetemperature (Fig(Figureure 10).10).

Minerals 2020, 10, x FOR PEER REVIEW 11 of 16

We also tried using the Se-Ni-Cd ternary genetic classification diagram of Duran et al. [13], suggested for discrimination between magmatic and hydrothermal chalcopyrite (Figure 11). In this diagram, half of analyses of chalcopyrite from the Oktyabrsk deposit are classified as magmatic and the other half as hydrothermal, irrespective of the ore type. Similarly, half of the analyses of chalcopyrite from massive ore of the Oktyabrsk deposit [16] fall into the hydrothermal type, whereas they should be within the magmatic (Figure 11). Using our classification tree (Figure 7), 90% of analyses of chalcopyrite from the Mansur et al. [16] database are correctly classified as belonging to the massive type of ore. This shows that the classification suggested in this study works, at least for the Oktyabrsk deposit, and is not dependent on the analytical set-up used for the analysis of trace element concentrations in chalcopyrite. However, our classification tree for the dataset of chalcopyrite analyses from Duran et al. [13] does notMinerals work.2020 This, 10, 716 shows that classifications such as those previously proposed by Duran et al. [13]11 of or 15 suggested in this study are dependent on the type of particular deposits. Our classification tree will likely work for the so-called Norilsk type of deposits, but may not be applicable for other types of spread over most of the fields with the majority of analyses located in the field of high crystallization deposits. The problem of using chalcopyrite for genetic classification is in the limited amount of temperature (Figure 10). empirical data. Our study also shows that interpretation of complex trace element data in We also tried using the Se-Ni-Cd ternary genetic classification diagram of Duran et al. [13], chalcopyrite should be conducted using advanced statistical methods. suggested for discrimination between magmatic and hydrothermal chalcopyrite (Figure 11). In this Based on the suggested classification tree and known mineral associations in the different ore diagram, half of analyses of chalcopyrite from the Oktyabrsk deposit are classified as magmatic and the types, some ideas regarding the behavior of elements can be suggested. For example, chalcopyrite of other half as hydrothermal, irrespective of the ore type. Similarly, half of the analyses of chalcopyrite disseminated ore is richer on average in Cd and Pb than chalcopyrite of the other two ore types. This from massive ore of the Oktyabrsk deposit [16] fall into the hydrothermal type, whereas they should is probably because the initial sulfide melt was rich in these elements and chalcopyrite crystallized be within the magmatic field (Figure 11). before galena. Accumulation of sulfide drops to form massive ore, whose chalcopyrite is poorer on Using our classification tree (Figure7), 90% of analyses of chalcopyrite from the Mansur et al. [16] average in Cd and Pb when compared to chalcopyrite of disseminated ore, occurred after the database are correctly classified as belonging to the massive type of ore. This shows that the classification beginning of galena crystallization. It was shown experimentally that cooling of sulfide melt from suggested in this study works, at least for the Oktyabrsk deposit, and is not dependent on the analytical 700 oC to 400 oC to increase in Cu/Pb ratio in melt by a factor of 3, suggesting galena set-up used for the analysis of trace element concentrations in chalcopyrite. However, our classification crystallization at higher temperatures than chalcopyrite [53]. As for the distribution of Se and Te in tree for the dataset of chalcopyrite analyses from Duran et al. [13] does not work. This shows that chalcopyrite, it is considered that Se is homogeneously distributed between monosulfide solid classifications such as those previously proposed by Duran et al. [13] or suggested in this study are solution (MSS) and intermediate solid solution (ISS), whereas Te is favorably distributed to ISS [52]. dependent on the type of particular deposits. Our classification tree will likely work for the so-called Due to this, there is no difference between chalcopyrite of disseminated and massive ores in terms of Norilsk type of deposits, but may not be applicable for other types of deposits. The problem of using these elements because these ores are the product of the evolution of MSS. Chalcopyrite of chalcopyrite for genetic classification is in the limited amount of empirical data. Our study also shows veinlet-disseminated ores is poorer on average in Te than chalcopyrite of the other two types. This that interpretation of complex trace element data in chalcopyrite should be conducted using advanced may show that veinlet-disseminated ore is not a product of the evolution of MSS and is related to statistical methods. another process, for example, metamorphism, as suggested by the authors of [49].

FigureFigure 10. BinaryBinary classification classification diagram diagram based based on on Cd Cd and and Zn Zn concentrations concentrations in in chalcopyrite chalcopyrite from the OktyabrskOktyabrsk deposit, suggested by the authors of [1] [1]..

Based on the suggested classification tree and known mineral associations in the different ore types, some ideas regarding the behavior of elements can be suggested. For example, chalcopyrite of disseminated ore is richer on average in Cd and Pb than chalcopyrite of the other two ore types. This is probably because the initial sulfide melt was rich in these elements and chalcopyrite crystallized before galena. Accumulation of sulfide drops to form massive ore, whose chalcopyrite is poorer on average in Cd and Pb when compared to chalcopyrite of disseminated ore, occurred after the beginning of galena crystallization. It was shown experimentally that cooling of sulfide melt from 700 ◦C to 400 ◦C leads to increase in Cu/Pb ratio in melt by a factor of 3, suggesting galena crystallization at higher temperatures than chalcopyrite [53]. As for the distribution of Se and Te in chalcopyrite, it is considered that Se is homogeneously distributed between monosulfide solid solution (MSS) and intermediate solid solution (ISS), whereas Te is favorably distributed to ISS [52]. Due to this, there is no difference Minerals 2020, 10, 716 12 of 15 between chalcopyrite of disseminated and massive ores in terms of these elements because these ores are the product of the evolution of MSS. Chalcopyrite of veinlet-disseminated ores is poorer on average in Te than chalcopyrite of the other two types. This may show that veinlet-disseminated ore is not a product of the evolution of MSS and is related to another process, for example, metamorphism, asMinerals suggested 2020, 10 by, x FOR the authorsPEER REVIEW of [49 ]. 12 of 16

Figure 11. TernaryTernary classification classification diagram diagram based based on onconcentrations concentrations of Cd, of Se Cd, and Se Ni and, according Ni, according to the toauthors the authors of [13]. of The [13 ]. major The idea major of idea this classificationof this classification is that the is that apexes the apexestrending trending to Ni and to Ni Cd and are Cdcharacteristics are characteristics of magmatic of magmatic and hydrothermal and hydrothermal deposits, deposits,respectively. respectively. The apex trending The apex towards trending Se towardswas not given Se was a genetic not given explanation a genetic by explanation Duran et al. by [13] Duran. The et thin al. [dotted13]. The line thin is for dotted the composition line is for the of compositionchalcopyrite of from chalcopyrite massive ore from of massive the Oktyabrsk ore of the deposit Oktyabrsk according deposit to according[16]. The to bold [16 ]. dotted The bold line dotteddiscriminate line discriminatess hydrothermal hydrothermal and magmatic and magmatic chalcopyrite chalcopyrite according according to [13]. Different to [13]. Di symbolsfferent symbols (circle, (circle,triangle triangle and rhomb) and rhomb) are used are used for fordifferent different ore ore types, types, respectively, respectively, disseminated, disseminated, massive massive and and veinlet-disseminated.veinlet-disseminated. (a)) disseminateddisseminated ore;ore; ((bb)) massivemassive oreore ((cc)) veinlet-disseminatedveinlet-disseminated ore.ore. 6. Conclusions 6. Conclusions Trace element concentrations in chalcopyrite of massive, disseminated and veinlet-disseminated Trace element concentrations in chalcopyrite of massive, disseminated and ores of the Oktyabrsk deposit of the Norilsk–Talnakh ore district are characterized by a large range of veinlet-disseminated ores of the Oktyabrsk deposit of the Norilsk–Talnakh ore district are values. Conventional methods of representation of the trace element data such as plotting normalized characterized by a large range of values. Conventional methods of representation of the trace concentrations or constructing binary and ternary diagrams do not allow for the separation of different element data such as plotting normalized concentrations or constructing binary and ternary ore types by trace element data in chalcopyrite. Thus, we suggested using more advanced statistics diagrams do not allow for the separation of different ore types by trace element data in chalcopyrite. based on cluster analysis and a classification tree to distinguish chalcopyrite of different types of ores. Thus, we suggested using more advanced statistics based on cluster analysis and a classification tree Using the suggested classification tree allows separation of chalcopyrite from three types of ores with a to distinguish chalcopyrite of different types of ores. Using the suggested classification tree allows success rate of 86%. A test on an independently published trace element dataset for chalcopyrite from separation of chalcopyrite from three types of ores with a success rate of 86%. A test on an massive ore of the same deposit shows a 90% of success rate for identifying the correct ore type. Overall, independently published trace element dataset for chalcopyrite from massive ore of the same our study shows that (1) chalcopyrite is potentially useful for genetic classification and (2) advanced deposit shows a 90% of success rate for identifying the correct ore type. Overall, our study shows that (1) chalcopyrite is potentially useful for genetic classification and (2) advanced statistical methods are superior to using simple diagrams, especially when complex analytical data are interpreted. Minerals 2020, 10, 716 13 of 15 statistical methods are superior to using simple diagrams, especially when complex analytical data are interpreted.

Supplementary Materials: The following are available online at http://www.mdpi.com/2075-163X/10/8/716/s1, Figure S1: Violin-plot of three types of ores, Oktyabrsk deposit, Table S1: Trace-elements compositions of chalcopyrite from three types ores, Oktyabrsk deposit. Author Contributions: Conceptualization, A.E.M., A.V.I., V.D.A.; methodology, A.E.M., V.D.A., T.N.A., T.A.R., T.Y.Y., K.V.B.; writing—original draft preparation, review and editing. A.E.M., A.V.I., V.D.A., T.Y.Y.; funding acquisition, A.E.M., A.V.I. All authors have read and agreed to the published version of the manuscript. Funding: A.E.M. and T.Y.Y. were funded by the grant No. 19-35-90013 from the Russian Foundation for Basic Research and the Tomsk Polytechnic University Competitiveness Enhancement Program for SEM investigation. Acknowledgments: We thank two anonymous reviewers for constructive criticism and useful suggestions. Conflicts of Interest: The authors declare no conflict of interest.

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