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Article

The Application of Sulfur–Metal Mass Ratios in Metal Sulfides in Assessing Prospects for Deep Metallogeny: A Case Study of the Tongshan Copper Deposit in Heilongjiang Province, Northeast China

by
Ruixuan Lan
1,2,
Lixin Zhu
3,
Shixin Tang
1,
Zhuang Duan
1,
Yong Li
1 and
Shengming Ma
1,*
1
Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000, China
2
Chinese Academy of Geological Sciences, China University of Geosciences, Beijing 100083, China
3
Development Research Centre, China Geological Survey, Beijing 100037, China
*
Author to whom correspondence should be addressed.
Minerals 2024, 14(11), 1069; https://doi.org/10.3390/min14111069
Submission received: 4 June 2024 / Revised: 8 October 2024 / Accepted: 17 October 2024 / Published: 24 October 2024
(This article belongs to the Special Issue Geochemical Exploration for Critical Mineral Resources)
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Abstract

:
Sulfur–metal mass ratios (SMMRs) between sulfur and metal elements (Cu, Pb, Zn, Ag, Fe, etc.) in metal sulfides are fixed in idealized compositions, so they should have a relatively fixed proportion in terms of mass without considering the presence of structural defects such as vacancies or substitution elements. Rock bodies with an SMMR of S far greater than the common metal sulfides may contain additional sulfides of other metals. We studied the Tongshan copper deposit in NE China and calculated the mass transfer of various elements in drill hole ZK611 samples. The data show a S influx of 7160 g/t, a Cu influx of 5469 g/t, and an Fe influx of 8796 g/t in the Cu ore body. Below the Cu ores, the average influx is 18,600 g/t of S, 650 g/t of Cu, and 5360 g/t of Fe, which provides an SMMR far above common mineral sulfide values. Further studies indicated that this rock unit contains fine-grained sphalerite and galenite, and when Zn and Pb are included in the rock SMMR calculations, values closer to the mineral sulfides emerge. These results imply that the coordinating balance relationship of S content with Fe and other ore-forming metals could provide direct information for assessing metallogenic prospects.

1. Introduction

Geochemical exploration is an indispensable tool for prospecting endogenetic non-ferrous and noble metal deposits and has played a critical role in mineral exploration at varying scales [1,2]. In recent decades, geochemical exploration techniques have made great progress [3,4], and the advancement of analytical techniques [5], improvements in geochemical data interpretation [6], and the use of 3D geochemical-visualizing techniques [7] have strongly supported the acquisition and presentation of geochemical information. However, the shift of prospecting work from shallow to deep presents a new challenge for these techniques, and there are still some aspects to be explored and improved [8]. As it should be, this is also a hard-won development opportunity for geochemical exploration.
In exploration areas using drilling engineering, the question of how to further evaluate metallogenic prospects is of significant importance for delineating prospecting targets, prospecting work deployment, and achieving prospecting breakthroughs, so it is urgent to find an effective solution. Geochemical exploration techniques are a valuable tool, but they provide a paucity of metallogenic information at this stage. The currently utilized univocal geochemical exploration indices, unable to provide abundant information, may contribute to this issue. Therefore, based on 30 known deposits and mineral exploration areas, this study integrates geochemical anomalies of distinct attributes and their implications in metallogenic systems [9,10,11,12,13], proposes a new idea for a multi-dimensional anomaly system [14], and emphasizes the importance of the metallogenic indication of mineralizing agents represented by sulfur anomalies [15].
The element sulfur, a typical chalcophile and mineralizing agent, is indispensable for the ore-forming materials of most non-ferrous metal deposits. In ore-forming processes, the element sulfur combines with various metal elements to form metal sulfides due to its chalcophile properties. Therefore, we can use the synergistic relationship of sulfur anomalies with Fe and other ore-forming elements to judge whether mineralization stops, as well as the mineralization possibility of deep and peripheral areas. Sulfur anomaly is a kind of geochemical anomaly, which means the content of sulfur in the wall rock or geological body is higher than its background value or Clark value (260 ppm, Taylor (1964) [16]). It is mainly assessed in the primary halo study of a drill hole; if sulfur in the core sample is high (an extremely significant positive anomaly), it suggests good metallogenic potential in the area passed through by the drill hole. However, if the sulfur content is low (a weak positive anomaly), this area would develop an ore body with a lower metallogenic potential. Furthermore, many ore geologists believe that sulfur is a major component in magmatic–hydrothermal ores, including porphyry copper, skarn, and polymetallic vein deposits, where it is enriched to a greater degree than any of the ore metals themselves [17]. Individual porphyry copper deposits are crustal sulfur anomalies, commonly exceeding one billion tons of sulfur [18].
For any given metal sulfide mineral, there is a fixed sulfur–metal mass ratio between sulfur and the metal elements, which is the basis for applying a sulfur anomaly to mineralization intensity and metallogenic prospect evaluation. However, little attention has been paid to this aspect. This study enriches and improves the application of sulfur–metal mass ratios (SMMRs) of sulfur to metal elements in metal sulfides for metallogenic prospect evaluation and further validates their practical value through test results. The SMMR is calculated as S/(S+metals of interest), for example, S/(S + Cu + Fe) for chalcopyrite, and not as S/(Fe + Cu); S/(S + Fe) for pyrite, and not as S/Fe.

1.1. Geochemical Features of Sulfur

Sulfur (S), in the third period in the sixth group of the periodic table, is a typical nonmetal element [19]. Sulfides rank second in their number, followed by oxides, in nature. Most elements that combine with S are called chalcophile elements, including about 40 kinds of elements, and these can be further divided into nonmetal and metal elements; these kinds of metal elements are dominated by the siderophile element Fe. Other elements that can combine with S to form common compounds include Zn, Pb, Cu, Ag, Sb, Bi, Ni, Co, Mo, Hg, etc. The combination of H with S exerts a key role in the formation of metal sulfides. Sulfur-bearing minerals in the Earth’s crust include sulfides of the sulfur complex, sulfate salts, and sulfosalts. Sulfur combines with various elements in different valences under contrasting oxidation conditions and shows a varying trend with increasing oxidation.
Most sulfides are stable under reduced conditions and will oxidize in supergene conditions. In the sulfide oxidation processes, bacteria and electrochemical processes attract much attention besides the above geochemical features of S [20,21,22]. The oxidation of S leads to considerable S leaching, resulting in a weakening metallogenic indication of S. However, this also changes the supergene environments of mineralized sections, which may lead to the derivation of other feasible geochemical exploration methods [23].
The above geochemical features of S emphasize its dominant role in forming minerals and sulfide deposits [24,25,26,27,28,29]. S is a mineralizing agent for transporting ore materials and is also an important component of sulfide. This also highlights the role of S in geochemical exploration. Therefore, the SMMR of S with other ore-forming elements in metal sulfide minerals and S anomaly in rocks can be used to evaluate the mineralization possibility of metal elements and thus predict the metallogenic prospects.

1.2. Sulfur–Metal Mass Ratios of Common Metal Sulfide Minerals

Most ore-forming minerals of non-ferrous metal deposits are metal sulfide minerals. Ore-forming minerals for different deposits are distinct in ore-forming elements and associated elements but are similar in S. Table 1 lists some metal elements and metal sulfide minerals that can combine with S to form common compounds. It indicates that these metal sulfide minerals always contain S. The SMMR for some metal sulfide minerals based on the crystal chemical formula of common compounds is also shown in Table 1. It implies that the SMMR of S is the largest (35.0 wt.%) for chalcopyrite among copper-bearing sulfide minerals, while that for chalcocite is the smallest (only 20.1 wt.%). The SMMR for lead-bearing sulfide minerals is commonly small; the highest is found for jamesonite (23.1 wt.%), and the smallest is found for galena (13.4 wt.%). The SMMRs of S for sphalerite and wurtzite are generally the same (32.9 wt.%). Argentite contains 12.9 wt.% S, ranking the lowest in our statistical results, and pyrite contains 53.4 wt.% S, ranking the highest in our statistical results. Pyrrhotite contains 39 wt.%–40 wt.% S. The metal sulfide minerals listed in Table 1 are the common major ore-forming minerals for corresponding metal deposits, implying that S is essential for the formation of non-ferrous metal deposits and that the S content and S anomaly can be utilized to predict metallogenic prospects.
The SMMR of metal sulfide minerals indicates that S anomaly has a closely positive relationship with non-ferrous mineralization intensity. The stronger the mineralization is, the more obvious the S anomaly, but conversely, if the S anomaly is obviously strong, mineralization may be weak. This is the key technical problem encountered when using a S anomaly to predict metallogenic prospects. In nature, S is more prone to combine with Fe to form pyrite or pyrrhotite than other metal elements such as Cu, Pb, and Zn. The element S occupies a high proportion in pyrite or pyrrhotite. Therefore, if Fe is in a metallogenic system, the combination of Cu, Pb, and Zn with S is constrained, and so is the mineralization. Hence, when the S anomaly is used to evaluate metallogenic prospects, the synergistic balance relationship between S and Fe and other ore elements should also be considered. In addition, the extent to which the S anomaly can forecast mineralization is also associated with certain mineral species. For instance, in a single Cu or Ag ore deposit, the content of S is highly variable, which is interpreted to depend on the high variations of Cu and Ag abundant in the crust (a three-order difference in magnitude) and a lower SMMR of S in Ag-bearing mineral (argentite) than that in Cu-bearing mineral (chalcopyrite).
Thus, S anomaly is utilized in two aspects: if metallogeny occurs in the prospects, and if the potential of metallogeny in the deep exists. In the following section, we will discuss these aspects while using the Tongshan copper deposit in Heilongjiang province as an example.

2. Geological Setting

2.1. Duobaoshan Ore Field

The Duobaoshan ore field is situated in Heilongjiang Province, China, on the eastern side of the Central Asian Orogenic Belt (CAOB), to the west of the Hegenshan–Heihe suture zone, which divides the Xing’an and Songnen blocks [30,31,32]. Multiple terrane collisions have characterized the tectonic evolution of the Xing’an–Mongolia Orogenic Belt in the Phanerozoic era (Figure 1a). The northeast section of the belt is fragmented into four blocks by the Tayuan–Xiguitu, Hegenshan–Heihe, and Mudanjiang fault zones [33,34,35,36]. From northwest to southeast, these four blocks are Ergun, Xing’an, Songnen, and Jiamusi [37]. Since the Early Paleozoic, the Ergun Block has remained stable [38]. In the Middle Paleozoic, the Xing’an Block gathered along the Tayuan–Xiguitu fault toward the Ergun Block [39,40]. In the Late Paleozoic, along the Hegenshan–Heihe fault, the Songnen Block collided with the Xing’an Block and Ergen Block [41]. In the Early Mesozoic, along the Mudanjiang fault, the Jiamusi Block also collided with the Xing’an Block and Ergun Block [42]. The Duobaoshan ore field is located in the Xing’an Block in the northwest of the Hegenshan–Heihe fault. The strata in this area are mainly Ordovician and Silurian, followed by Devonian and Cretaceous (Figure 1b). The main ore-bearing strata are the Tongshan formation and the Duobaoshan formation. The former is composed of tuffaceous sandstone, siltstone, tuff, and crystalline limestone, while the latter is composed of andesite, dacite, and pyroclastic rock, with interbedded marble and slate [43,44,45].

2.2. Tongshan Copper Deposit

The Tongshan copper deposit is sited in the northeastern segment of the Daxinganling fold system, and the middle part of the NW-trending tectonomagmatic belt northwest of the Xinkailing deep fault. The outcropped strata are the Middle Ordovician Tongshan formation (O2t) and Duobaoshan formation (O2d) (Figure 2). The Tongshan formation is composed of intermediate acidic tuff, dacite, and quartz sandstone, and the Duobaoshan formation is dominated by andesite and intermediate acidic tuff acting as country rocks of the Tongshan deposit [46]. The country rock alteration is chiefly potassic silicification, phyllic alteration, and propylitization, and orebodies mainly occur in the phyllic alteration belts. Intrusive rocks include the Middle Hercynian granodiorite (γδ42) and Late Hercynian trondhjemite (γδ43). Isotopic dating suggests that the mineralization principally occurred in the Late Hercynian to Indosinian. Re-Os isotopic dating indicates that ore materials may derive from the Late Ordovician, migrating from country rocks [47], and are enriched in favorable positions such as extensional structures [48]. Fluid inclusion studies indicate that the ore-forming fluids in the Tongshan deposit experienced three stages, and fluid boiling resulting from the decrease in pressure is the major mechanism for the precipitation of chalcopyrite [49].

3. Experimental Research Methods

3.1. Sample Collection

Samples from the Tongshan copper deposit were collected from drill ZK611 cores. Overall, 56 samples were collected from this hole with a depth of 604 m, and the sampling interval was 5–8 m. Within every sampling interval, we collected samples by continuously picking rock blocks, and 8–10 pieces of 20–30 g weight rock samples comprised an analytical sample with a total weight of 200–300 g. In addition, samples within a sample interval are generally the same in rock lithology, alteration, and mineralization so that the adequate representation of rocks is achieved.

3.2. Sample Processing and Analysis

Rock samples were processed through three steps of crushing. First, the samples were coarsely crushed to <3–5 mm with a jaw crusher, the jaw plate material of which is high-aluminum porcelain, then ground to <0.9 mm (20 mesh) with a disc crusher, and then ground to <0.074 mm (200 mesh) with an agate ball mill. These procedures were strictly controlled in order to avoid sample contamination and pollution.
The samples were then analyzed in the central laboratory of the Institute of Geophysical and Geochemical Exploration, CAGS. The analytical methods and detection limit are listed in Table 2. The quality control of sample analysis adopted standard substance and repeated samples, and the results show that the passing rate was 100%.

3.3. Calculation of Element Migration

The SMMR of S with metal elements is a good method used to predict metallogenic prospects; these ratios are those of element migration quality between S and ore-forming elements, which are based on element flux. However, only the element content is available currently. This poses a difficult problem: using the element content to estimate its mass migration, i.e., the element influx, element efflux, and their mass. Only by solving this problem can we further discuss the SMMR between S and ore-forming elements and thus use the S anomaly to evaluate the metallogenic prospects of a mineral exploration area.
This problem stems from studying the mass balance of the geochemical open system category. Since the 1970s, many researchers have discussed this issue and achieved fruitful results, of which the Gresens equation deduced by Gresens (1967) [50] created a new situation of using rock analytical data in rock mass balance. On this basis, Grant (1986) [51] proposed the concentration line method (isocon), greatly simplifying the mass balance calculation, and achieved a widespread application in hydrothermal alteration and migmatization [52]. Deng et al. (1999) [53] derived two new expressions based on the Grant equation to discuss the quality changes and quality change rates of active elements.
This paper utilizes Grant’s concentration line equation to discuss the SMMR of S with metallogenic elements.
ΔCi = (MA/MO)CiA − CiO = (CjO/CjA)CiA − CiO
where ΔCi is the migration quantity of element i, MA is the mass of altered rocks, MO is the mass of original rocks, CiA is the content of element i in altered rocks, CiO is the content of element i in original rocks, CjO is the inert element j in original rocks, and CjA is the content of inert element j in altered rocks. According to previous studies [54,55,56], Al2O3 is usually immobile in mass transfer [57,58,59,60], so we selected Al2O3 as an inert element, and j in the equation is Al2O3, with i representing S, Fe, Cu, Pb, Zn, and Ag.
In calculating element migration quantity, the element content of original rocks for altered rocks is also needed, as opposed to mineralized or altered rocks. However, due to the widespread alteration and mineralization and varying types of rocks, unaltered original rocks were unobtainable in this study. Considering this point and integrating information from experimental studies, this study adopted different methods to ascertain the element content in original rocks. The background content of inert Al2O3 and major Fe uses the median content of Al2O3 and Fe for every drill core; the background values of S, Cu, Pb, Zn, and Ag in the Tongshan copper deposit adopt the chemical composition of andesite pyroclastic rocks, andesite, and sandstone in East China [61].
For most rock samples, this study only describes the element migration quantity for every drill hole based on the same lithology or mineralization type. Thus, the merged statistical results are based on lithology or mineralization types.

3.4. Steps and General Rules for Evaluation of Metallogenic Prospects Using Sulfur–Metal Mass Ratios

  • Based on Grant’s concentration equation and element content in altered rocks, this study used Al2O3 as the inert (immobile) element and calculated the element migration quantity of S, Cu, Pb, Zn, As, and Ag in the geochemical systems of the test areas. We obtained the content of Fe2O3 via sample analysis, so the content of Fe can be obtained using Fe = 112 × Fe2O3/160. Furthermore, the element migration quantity of Fe can be obtained using Grant’s concentration equation.
  • The element migration quantity and variations of S can be used preliminarily to judge whether there were hydrothermal processes or mineralization in drillholes, the overall mineralization intensity, and the possible mineralization sections.
  • On the premise that mineralization is judged to exist, differing element migration of S and Fe can be used to interpret element migration and S content and further to judge whether there are metallogenic conditions for forming non-ferrous metal deposits.
  • On the premise that non-ferrous metal deposits or mineralization are inferred to exist, the element migration quantity of metallogenic elements can be utilized to infer possible mineral species. Then, the identification of polished sections was performed to ensure the mineralization process and thus discover mineralization.
  • If there are good metallogenic prospects in deep or peripheral areas, exploration or prospecting engineering should be deployed to validate this.
  • When using the S anomaly to evaluate the metallogenic prospects of an exploration area, the above steps and general rules are generally progressive. Usually, if the previous condition does not exist, the following step will terminate.

4. Result

The element concentrations of S, Fe, Cu, Pb, Zn, and Ag in drill hole ZK611 in the Tongshan copper deposit are listed in Table 3. The element migration quantity of S, Fe, Cu, Pb, Zn, and Ag in the Tongshan copper deposit is calculated and listed in Table 4 in the order of lithology. The element flux of S, Fe, Cu, Pb, Zn and Ag of each sample is shown in Table 5. Generally, the S in drill hole ZK611 shows an influx feature, which provides material for mineralization, but its influx degree varies greatly. In the sections of andesite tuff at a depth of 0–272 m and andesite at a depth of 272–400 m, the influx of S is small, averaging less than 1000 g/t; in altered andesite at a depth of 400–494 m, the influx of S becomes larger, corresponding to the appearance of chalcopyrite; in the section with chloritization, sericitization, and weak copper mineralization of andesite, the influx of S appears as the greatest, averaging 18,575 g/t, which is in disagreement with the weak copper mineralization. In sandstone below the altered andesite, the influx of S is 1432 g/t, which shows an influx characteristic. However, the S influx dramatically decreased relative to the overlying andesite. It is suggested that the S influx does not closely correspond to the copper mineralization intensity. In the chloritized and sericitized andesite sections (494–581 m) with the largest S influx, the influx of Cu averages only 652 g/t, while in the copper mineralized andesite section (400–494 m) with the largest Cu influx, the S influx is 7160 g/t, about 39% of the largest S influx.

5. Discussion

5.1. Correlation of Element Migration in Different Layers

In order to explore the correlation between different elemental migration in different layers and identify the elements related to the enrichment of the main ore-forming element Cu, Pearson’s correlation coefficient was applied in the study. As shown in Figure 3, the lithology of the first layer is andesite tuff. In terms of a positive correlation, the positive correlation between ΔZn and ΔS is the highest. The correlation coefficient is the largest (R = 0.91), followed by that between ΔAg and ΔS, R = 0.87. That between ΔPb and ΔS is the third-highest, R = 0.76, and with regard to the negative correlation, the negative correlation between ΔFe and ΔPb is the highest, and the correlation coefficient is the smallest (R = −0.40). This is followed by that between ΔFe and ΔAg, R = −0.37. Third is that between ΔFe and ΔZn, R = −0.30 (Figure 3a). This indicates that, in andesite tuff, the greater the migration quantity of S, the greater that of Zn, and the greater the migration quantity of Fe, the smaller that of Pb. The lithology of the second layer is andesite. Regarding the positive correlation, the positive correlation is the highest between ΔCu and ΔS. Their correlation coefficient is the largest (R = 0.97), followed by the positive correlation between ΔCu and ΔAg, R = 0.87. Third is that between ΔS and ΔAg, R = 0.80; regarding the negative correlation, the negative correlation between ΔCu and ΔZn is the highest, and their correlation coefficient is the smallest (R = −0.24), followed by that between ΔS and ΔZn, R = −0.21 (Figure 3b). The above results show that in andesite, when the migration quantity of S is greater, the migration quantity of Cu is greater. When the migration quantity of Zn is larger, the migration quantity of Cu is smaller. The lithology of the third layer is chalcopyritized andesite. All of the six elements show a positive correlation with each other, but the degree of correlation is different. The highest is the correlation between ΔPb and ΔAg, R = 0.94. Second is that between ΔS and ΔCu, R = 0.90. Third is that between ΔS and ΔAg, R = 0.85 (Figure 3c), which reveals that the enrichment of Pb is closely related to the enrichment of Ag, and the migration quantity of S is crucial to the migration quantity of Cu in chalcopyritized andesite. Fourth is altered andesite. Most of the elements in this layer show a positive correlation with each other. The correlation between ΔS and ΔZn is the highest, R = 0.92. The one between ΔS and ΔCu is the second-highest, R = 0.82. That between ΔCu and ΔZn is the third-highest, R = 0.77 (Figure 3d), which shows that S influx is closely related to the influx of Cu and Zn, and the enrichment of Zn promotes the enrichment of Cu. From another point of view, Cu’s enrichment, as the major ore-forming element, significantly drives the enrichment of the associated ore-forming element Zn. The fifth layer is sandstone. The correlation between the six elements shows a trend of polarization. For Cu, only ΔS and ΔPb have an extremely significant negative correlation with ΔCu, and the other elements have an extremely significant positive correlation (Figure 3e). We combined all the samples and found that (Figure 3f) the positive correlation between ΔS and ΔZn is the highest, R = 0.89, and that between ΔS and ΔAg is the second-highest, R = 0.70. The above consequences indicate that the influx of S is the most supportive of Zn enrichment, followed by Ag. For Cu, ΔFe has the highest positive correlation, suggesting that Fe may be the best-associated ore-forming element.

5.2. Balance of S-Cu-(Fe, Pb, Zn, and Ag)

The relationship between the S concentration, Cu concentration, and other associated ore-forming elements’ (Fe, Pb, Zn, and Ag) concentrations can be seen in Figure 4, and there is a close relationship between the three. Because there are three variables in each subgraph, concentrations of S, Fe, and Cu are three variables in Figure 4a; concentrations of S, Pb, and Cu are three variables in Figure 4b; concentrations of S, Zn, and Cu are three variables in Figure 4c; and concentrations of S, Ag, and Cu are three variables in Figure 4d. In order to display the distribution of three variables and determine the concentration of the other two elements when the Cu concentration is the highest, Figure 4 was drawn using OriginPro 2024b Software (OriginLab Corp., Northampton, MA, USA).
When S is in a relatively low concentration (<10,000 ppm), there is no significant correlation between Cu and Fe concentrations (Figure 4a). For instance, when the S concentration is ca. 100 ppm, the Fe concentration rises from 4 wt.% to 8 wt.%, and the Cu concentration does not increase significantly. It is always below 3000 ppm. When the S concentration is around 10,000 ppm, the increase in the Fe concentration will lead to a significant increase in the concentration of Cu. In contrast, the S concentration is higher than 10,000 ppm; this trend disappears again, indicating that in this study, when the S concentration is only around 10,000 ppm, the concentration of Cu will increase significantly, and the increase in the Fe concentration will maximize the enrichment of Cu. This rule also applies to the S-Cu-Pb system (Figure 4b). When the concentration of S is only about 10,000 ppm, a small increase in the Pb concentration (from 1 ppm to 100 ppm) will contribute to a substantial increase in the Cu concentration. On the other hand, when Cu is in a constant low concentration (<3000 ppm), when the S concentration increases from 100 ppm to 100,000 ppm, the Pb concentration will also increase from 10 ppm to 100,000 ppm. In terms of the S-Cu-Zn system (Figure 4c), when the S concentration is only about 10,000 ppm, the Zn concentration increases slightly near 100 ppm, and the Cu concentration will increase significantly, from 3000–6000 ppm to 18,000–21,000 ppm. In other words, the Cu concentration is most sensitive in such a condition that the S concentration is about 10,000 ppm and the Zn concentration is approximately 100 ppm. Similarly, when the Cu concentration is continuously below 3000 ppm, the S concentration increases from 100 ppm to 100,000 ppm, resulting in the Zn concentration also increasing from 100 ppm to 100,000 ppm. With regard to the S-Cu-Ag system (Figure 4d), when the S concentration is only about 10,000 ppm, and the Ag concentration rises from 1000 ppm to 10,000 ppm, under such a condition, Cu shows a substantial increase in concentration. Similarly, when the Cu concentration is below 3000 ppm, S shows a significant positive correlation with Ag in the concentration range from 100 ppm to 100,000 ppm.
In conclusion, in terms of mineralization and Cu as the major ore-forming element, the higher the concentration of copper, the better. The above four systems have their own optimal concentration ranges for Cu, and the optimal concentration range for the S-Cu-Fe system (Figure 4a) is that the S concentration is near 10,000 ppm, and the Fe concentration is about 5.5–8.5 wt.%. The optimal concentration range of the S-Cu-Pb system (Figure 4b) is that the S concentration is near 10,000 ppm, and the Pb concentration is about 1–100 ppm. For the S-Cu-Zn system (Figure 4c), the optimal concentration range is that the S concentration is around 10,000 ppm, and the Zn concentration is around 100 ppm. For the S-Cu-Ag system (Figure 4d), the optimal concentration range is that the S concentration is around 10,000 ppm, and the Ag concentration is around 1000–10,000 ppm. It is not difficult to recognize that no matter what kind of system above, for Cu, the optimal metallogenic concentration of S is always near the order of magnitude of 10,000 ppm.
Without considering the Cu concentration, a linear correlation analysis between the S concentration and Fe, Pb, Zn, and Ag of the samples was conducted. The results showed that the correlation between S and Ag was the most significant (coefficient of determination R2 = 0.75). At the same time, it was the weakest between S and Fe (coefficient of determination R2 = 0.02). The coefficient of determination R2 of S-Pb is 0.26, and the coefficient of determination R2 of S-Zn is 0.37. The massive influx of S could lead to an abundant influx of Ag, Pb, and Zn but not to a great influx of Fe.

5.3. Evaluating the Potential of Deep Metallogeny

The SMMR of S with Cu+Fe in chalcopyrite is 35:65, about 1:2. In the main copper-mineralized section of the ZK611 drill hole (400–494 m), the main ore-forming mineral is chalcopyrite, where the average S influx is 7160 g/t, 5469 g/t for Cu, and 8796 g/t for Fe. This essentially corresponds to the SMMR of S with Cu+Fe in chalcopyrite. Based on the test results in the known copper mineralized sections, in the sections of 491–581 m with the maximum S influx, there should be stronger mineralization. However, when logging in drill cores, we found no valuable ore signs except weak copper mineralization.
The common sense explanation is that the large S influx and no mineralization of other ore-forming elements may indicate Fe influx and pyrite formation. In fact, the influx of Fe in this section is relatively great (5359 g/t), and pyritization is widespread, partly validating the above interpretation. However, although S can combine with Cu (652 g/t) and Fe (5359 g/t) to form chalcopyrite and pyrite, there remains about 13,000 g/t S which we cannot infer where it is. As this quantity of S is significant, it is thus of great significance to further investigate its occurrence; this has a critical role in understanding mineralization types, guiding the comprehensive exploration and utilization of resources, and prospecting direction.
The large influx of S generally indicates once-strong hydrothermal activities or even mineralization, signaling a prospecting promise. The issue we should first address is in which minerals the approximately 13,000 g/t of S is hosted. The first step to solve this problem is to start with the element SMMR. Rock measurements and element migration quantity of drill hole ZK611 (Table 4) indicate that, in the section at a depth of 491–581 m, Pb, Zn, and Ag all exhibit significant influx. The average influx of Pb is 4653 g/t and 18,836 g/t for Zn, with a large influx of Ag and Cd. If S combines with Pb and Zn in the system to form common Pb and Zn sulfide minerals of galena and sphalerite, based on Pb and Zn influx and the SMMR of S with Pb and Zn (based on 1 t altered rocks), about 720 g of S is needed to combine with 4653 g of Pb, and 9400 g of S to combine with 18,836 g of Zn, plus the influx of Ag and Cd. This S influx generally agrees with that of polymetallic metallogenic elements and is consistent with the overall SMMR of S with metal elements in metal sulfide minerals. It is thus inferred that there is Pb-Zn-polymetallic mineralization in the section, and its mineralization intensity is much stronger than that of copper mineralization (Figure 5).
In order to confirm the above speculation, we performed microscopic identification of petrographic polished sections. The result shows that there is Pb-Zn mineralization in the ZK611 drill hole at depths of 494–581 m, and the mineralization is dominated by galena and sphalerite, which display a fine granular texture (Figure 6) and cannot be identified with the naked eye. This may be the reason for no discovered Pb-Zn mineralization in the drill hole, and overall, it is a successful case of applying S anomaly using a multi-dimensional anomaly system to predict metallogenic prospects.
We should mention that previous prospecting work revealed two layers of noncommercial Zn orebodies with an apparent thickness of about 20 m, but their scale was much smaller than this study’s. The discovery of Pb-Zn-polymetallic mineralization with an apparent thickness of 90 m indicates a new comprehensive evaluation of resources and prospecting direction, and the economic value should be further evaluated.
The element S in metal sulfides is easily oxidized and migrates with surface water, resulting in great uncertainty of a S anomaly in mineralized sections. Thus, this constrains the indication of S in the preliminary survey and prospecting stage. In the mineral exploration stage with drilling engineering, oxidation of S in drill cores is not strong and further disappears with an increasing drill depth, making it possible to use the S content to evaluate the mineralization intensity.
The application of SMMR of sulfur to metal elements in metal sulfides to predict metallogenic prospects has been preliminarily researched, and the results are encouraging. As it should be, the indication of a S anomaly is not independent, and it is only an important part of the multi-dimensional anomaly system of the geochemical system, the utilization of which will be most effective if combined with other anomaly systems. In addition, the occurrence of S is intimately associated with metallogenic environments, and the occurrence of S2− and S6+ may contain more metallogenic information than that of a S anomaly. Relevant studies must be carried out in a systematic and orderly fashion.

6. Conclusions

  • In the orebodies, the average S influx is 7160 g/t, that of Cu is 5469 g/t, and that of Fe is 8796 g/t. In contrast, below the orebodies, the average S influx is 18,600 g/t, that of Cu is 650 g/t, and that of Fe is 5360 g/t, which disagrees with their SMMRs.
  • The element migration quantity shows that Pb’s average influx is 4650 g/t, and Zn’s is 18,840 g/t below the orebodies. Microscopic identification reveals that Pb-Zn mineralization occurs in the ZK611 drill hole at a depth of 494–581 m, and the mineralization is dominated by galena and sphalerite, which display a fine granular texture.
  • The metallogenic indication of the SMMR is present in two aspects: whether mineralization stops, and the mineralization possibility in deep and peripheral areas. These two factors are critical for evaluating metallogenic prospects and achieving good results in the Tongshan copper deposit.

Author Contributions

Conceptualization, S.M.; data curation, R.L.; formal analysis, R.L.; funding acquisition, Y.L. and Z.D.; investigation, S.M. and S.T.; methodology, R.L., S.T., Z.D. and S.M.; resources, S.M.; software, R.L.; supervision, L.Z. and S.M.; writing—original draft, R.L.; writing—review and editing, Lixin Zhu and S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This study is financially supported by the State Key Research and Development Plan (Grant No. 2018YFE0208300), the China Geological Survey Project (DD20242247), the Science & Technology Fundamental Resources Investigation Program (Grant No. 2022FY101800), a National Nonprofit Institute Research Grant (AS2022J11), and the Scientific Research Fund for Public Welfare from China’s Ministry of Land and Resource (Grant No. 201111008).

Data Availability Statement

Data are contained within the article.

Acknowledgments

Thanks are given to the three anonymous reviewers for their careful suggestions and to the assistant editor, for their kind assistance. We also thank the academic editors for their sincere affirmations and all those who helped with data processing.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) A sketch tectonic map of the Xing’an–Mongolia orogenic belt (modified from [30]); (b) a simplified geological map of the Duobaoshan ore field (modified from [37]). In (a), F1 represents the Mudanjiang fault, F2 represents the Yitong–Yilan fault, F3 represents the Dunhua–Mishan fault, F4 represents the Chifeng–Kaiyuan fault, F5 represents the Xilamulun–Changchun fault, F6 represents the Hegenshan–Heihe fault, F7 represents the Tayuan–Xiguitu fault, and F8 represents the Mongolia–Okhotsk fault.
Figure 1. (a) A sketch tectonic map of the Xing’an–Mongolia orogenic belt (modified from [30]); (b) a simplified geological map of the Duobaoshan ore field (modified from [37]). In (a), F1 represents the Mudanjiang fault, F2 represents the Yitong–Yilan fault, F3 represents the Dunhua–Mishan fault, F4 represents the Chifeng–Kaiyuan fault, F5 represents the Xilamulun–Changchun fault, F6 represents the Hegenshan–Heihe fault, F7 represents the Tayuan–Xiguitu fault, and F8 represents the Mongolia–Okhotsk fault.
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Figure 2. Simplified geological map of the Tongshan copper deposit (modified from [15]). 1—Quaternary; 2—Middle Ordovician Duobaoshan formation, 3rd member, 1st submember; 3—Middle Ordovician Duobaoshan formation, 2nd member, 2nd submember; 4—Middle Ordovician Duobaoshan formation, 2nd member, 1st submember; 5—Middle Ordovician Duobaoshan formation, 1st member, 3rd submember; 6—Middle Ordovician Duobaoshan formation, 1st member, 2nd submember; 7—Middle Ordovician Duobaoshan formation, 1st member, 1st submember; 8—Middle Ordovician Tongshan formation, 3rd member; 9—Middle Ordovician Tongshan formation, 2nd member; 10—Plagioclase granite porphyry; 11—Granodiorite; 12—Quartz diorite; 13—Diorite; 14—Diorite porphyrite; 15—Inferred and measured fault; 16—Copper orebody; 17—Exploration line and drill hole.
Figure 2. Simplified geological map of the Tongshan copper deposit (modified from [15]). 1—Quaternary; 2—Middle Ordovician Duobaoshan formation, 3rd member, 1st submember; 3—Middle Ordovician Duobaoshan formation, 2nd member, 2nd submember; 4—Middle Ordovician Duobaoshan formation, 2nd member, 1st submember; 5—Middle Ordovician Duobaoshan formation, 1st member, 3rd submember; 6—Middle Ordovician Duobaoshan formation, 1st member, 2nd submember; 7—Middle Ordovician Duobaoshan formation, 1st member, 1st submember; 8—Middle Ordovician Tongshan formation, 3rd member; 9—Middle Ordovician Tongshan formation, 2nd member; 10—Plagioclase granite porphyry; 11—Granodiorite; 12—Quartz diorite; 13—Diorite; 14—Diorite porphyrite; 15—Inferred and measured fault; 16—Copper orebody; 17—Exploration line and drill hole.
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Figure 3. Pearson correlation coefficient of elemental migration. (a) The correlation between the six elements’ migration in andesite tuff (1st layer); (b) the correlation between the six elements’ migration in andesite (2nd layer); (c) the correlation between the six elements’ migration in chalcopyritized andesite (3rd layer); (d) the correlation between the six elements’ migration in altered andesite (4th layer); (e) the correlation between the six elements’ migration in sandstone (5th layer); (f) the correlation between the six elements’ migration in all samples.
Figure 3. Pearson correlation coefficient of elemental migration. (a) The correlation between the six elements’ migration in andesite tuff (1st layer); (b) the correlation between the six elements’ migration in andesite (2nd layer); (c) the correlation between the six elements’ migration in chalcopyritized andesite (3rd layer); (d) the correlation between the six elements’ migration in altered andesite (4th layer); (e) the correlation between the six elements’ migration in sandstone (5th layer); (f) the correlation between the six elements’ migration in all samples.
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Figure 4. The distribution of the analyses in coordinates: S (ppm)—Fe (wt.%) (a); S (ppm)—Pb (ppm) (b), S (ppm)—Zn (ppm) (c); S (ppm)—Ag (ppb) (d). The different colors of the circles in the graphs correspond to the different Cu concentrations shown in a color scale on the right side of the graphs.
Figure 4. The distribution of the analyses in coordinates: S (ppm)—Fe (wt.%) (a); S (ppm)—Pb (ppm) (b), S (ppm)—Zn (ppm) (c); S (ppm)—Ag (ppb) (d). The different colors of the circles in the graphs correspond to the different Cu concentrations shown in a color scale on the right side of the graphs.
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Figure 5. Bar chart of element flux and content in the ZK611 drill hole. 1—Ordovician Duobaoshan formation, 1st member; 2—Andesite tuff; 3—Andesite; 4—Sandstone; 5—Cu orebody; 6—Propylitization; 7—Chloritization; 8—Sericitization; 9—Influx; 10—Efflux; 11—Content.
Figure 5. Bar chart of element flux and content in the ZK611 drill hole. 1—Ordovician Duobaoshan formation, 1st member; 2—Andesite tuff; 3—Andesite; 4—Sandstone; 5—Cu orebody; 6—Propylitization; 7—Chloritization; 8—Sericitization; 9—Influx; 10—Efflux; 11—Content.
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Figure 6. Optical microscopy identification. (a) No. 59 sample: 4.0% sphalerite, 1.2% chalcopyrite, 0.1% galena, and 10% pyrite. (b) No. 60 sample: 8.0% sphalerite, 1.2% chalcopyrite, 1.0% galena, and 15–20% pyrite. No. 59 and No. 60 samples were both collected from a depth of 494–581 m in the ZK611 drill hole. Abbreviations: Py = pyrite, Ccp = chalcopyrite, Sp = sphalerite, Gn = galena.
Figure 6. Optical microscopy identification. (a) No. 59 sample: 4.0% sphalerite, 1.2% chalcopyrite, 0.1% galena, and 10% pyrite. (b) No. 60 sample: 8.0% sphalerite, 1.2% chalcopyrite, 1.0% galena, and 15–20% pyrite. No. 59 and No. 60 samples were both collected from a depth of 494–581 m in the ZK611 drill hole. Abbreviations: Py = pyrite, Ccp = chalcopyrite, Sp = sphalerite, Gn = galena.
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Table 1. Sulfur–metal mass ratios in common metal sulfide and sulfosalt minerals.
Table 1. Sulfur–metal mass ratios in common metal sulfide and sulfosalt minerals.
ElementMineralFormulaSulfur–Metal Mass Ratio (wt.%)
CuChalcopyriteCuFeS2Cu34.6Fe30.4S35.0
BorniteCu5FeS4Cu63.3Fe11.1S25.6
Tetrahedrite-(Cu)Cu12Sb4S13Cu45.9Sb29.2S24.9
ChalcociteCu2SCu79.9 S20.1
CovelliteCuSCu66.5 S33.5
PbGalenaPbSPb86.6 S13.4
JamesonitePb4FeSb6S14Pb40.1Fe2.7, Sb35.5S21.7
BournonitePbCuSbS3Pb42.4Cu13.0, Sb24.9S19.7
ZnSphaleriteZnSZn67.1 S32.9
WurtziteZnSZn67.1 S32.9
AgArgentiteAg2SAg87.1 S12.9
FePyriteFeS2Fe46.6 S53.4
PyrrhotiteFe1-xSFe61-60 S39-40
Table 2. Sample analysis methods and quality monitoring results.
Table 2. Sample analysis methods and quality monitoring results.
Analytical ItemsAnalytical
Methods		Detection LimitUnitPassing Rate of One-Order
Standard Material/%		Passing Rate of Repeated Samples/%
SWD-XRF50ppm100100
CuICP-MS1ppm100100
PbICP-MS2ppm100100
ZnICP-MS2ppm100100
AgA.C.-Arc-SES20ppb100100
Fe2O3WD-XRF0.1wt.%100100
Note: WD-XRF = Wavelength Dispersive X-Ray Fluorescence Spectrometry; ICP-MS = Inductively Coupled Plasma Mass Spectrometry; A.C.-Arc-SES = Alternating Current Arc Source Emission Spectrometry.
Table 3. The element contents (S, Fe, Cu, Pb, Zn, and Ag) of samples from the ZK611 drill hole.
Table 3. The element contents (S, Fe, Cu, Pb, Zn, and Ag) of samples from the ZK611 drill hole.
Sample NumberLocation/mS/ppmFe/wt.%Cu/ppmPb/ppmZn/ppmAg/ppb
ZK611-1282235.96 120 17 191 883
ZK611-25028994.70 162 288 1365 1669
ZK611-3694116.36 190 132 386 1095
ZK611-49022316.87 157 58 1936 1187
ZK611-5965376.07 304 28 168 274
ZK611-61211686.75 155 5 102 445
ZK611-71402926.13 117 13 187 345
ZK611-81552317.78 281 10 124 359
ZK611-91703807.55 185 12 121 186
ZK611-101911427.79 178 17 109 383
ZK611-112113408.27 254 26 142 538
ZK611-1223015396.65 633 54 265 1825
ZK611-132513584.32 134 212 157 439
ZK611-14263185 6.69 149 20 105 282
ZK611-15272127 6.59 129 7 103 210
ZK611-16281212 5.84 208 119 135 911
ZK611-1728885 4.40 64 15 96 138
ZK611-18300110 5.96 97 6 94 153
ZK611-19311485 5.64 105 24 382 545
ZK611-20320152 5.67 80 15 124 121
ZK611-21329284 5.15 104 13 100 160
ZK611-22341114 3.64 65 9 143 86
ZK611-233501528 7.16 262 23 211 382
ZK611-243628349 6.68 772 45 96 1447
ZK611-25371568 5.36 183 14 131 163
ZK611-26379635 4.68 108 30 144 244
ZK611-27388391 5.64 133 104 230 512
ZK611-28400124 4.88 42 30 173 232
ZK611-2941310,905 7.50 5522 63 154 9569
ZK611-304212756 5.86 2189 4 52 1390
ZK611-314306248 6.46 5545 4 51 2576
ZK611-324407867 5.64 4400 12 75 2337
ZK611-334492794 4.87 2318 6 62 1183
ZK611-344588340 7.63 5472 8 129 3068
ZK611-3547017,571 8.53 18,415 43 122 7745
ZK611-364737522 6.28 7011 10 97 2362
ZK611-374795769 6.80 5650 4 92 1746
ZK611-384839349 7.76 7377 6 97 4405
ZK611-394884769 7.04 1406 7 130 495
ZK611-404945223 7.57 1275 8 192 852
ZK611-415004760 6.13 238 30 3865 782
ZK611-4250611,100 6.97 149 411 3006 1396
ZK611-435121456 5.71 31 159 579 322
ZK611-4451829,360 7.13 419 1844 13,484 5486
ZK611-455215829 6.34 209 156 3658 1195
ZK611-4652720,085 7.48 703 308 16,233 4251
ZK611-4753010,296 4.01 1012 262 4080 5197
ZK611-4853626,450 6.69 1213 317 45,219 6659
ZK611-4954142,013 7.89 1881 452 49,649 8948
ZK611-5054539,931 5.38 1013 42,579 66,899 25,284
ZK611-5155113,949 5.51 331 2477 5987 3850
ZK611-5256011,288 4.79 234 765 2974 3426
ZK611-5357216,109 5.01 658 6049 7409 32,622
ZK611-545819634 4.66 496 2319 10,855 4048
ZK611-555901491 4.99 1982 45 309 2298
ZK611-566042273 5.58 265 60 270 404
Table 4. Statistics of the element migration quantity in the ZK611 drill hole.
Table 4. Statistics of the element migration quantity in the ZK611 drill hole.
LayersDrill Hole Depth (m)Main LithologyΔSΔFeΔCuΔPbΔZnΔAg
10–272Andesite tuff (15)69519,281201522970.67
2272–400Andesite (13)803−217213320600.35
3400–494Chalcopyritized andesite (12)7160879654691.03.03.2
4494–581Altered andesite (14)18,5755359652465318,8368.0
5581–604Sandstone (2)143249521010261931.8
Note: positive numbers indicate element influx in the system, and a negative number indicates element efflux. The unit of migration quantity is g/t. Figures in brackets represent the numbers of samples used for statistics.
Table 5. The element flux (S, Fe, Cu, Pb, Zn, and Ag) of samples in the ZK611 drill hole.
Table 5. The element flux (S, Fe, Cu, Pb, Zn, and Ag) of samples in the ZK611 drill hole.
Sample NumberLocation/mΔS (g/t)ΔFe (g/t)ΔCu (g/t)ΔPb (g/t)ΔZn (g/t)ΔAg (mg/t)
ZK611-128185 8753 102 −1 98 812
ZK611-2504015 16,844 212 384 1817 2289
ZK611-369390 16,228 180 118 308 1079
ZK611-4901861 9396 119 32 1553 966
ZK611-596601 22,468 343 15 111 282
ZK611-6121172 32,165 172 −11 36 493
ZK611-7140267 13,369 105 −4 103 310
ZK611-8155211 32,365 279 −7 42 335
ZK611-9170347 26,007 169 −5 33 145
ZK611-10191110 27,874 161 0 20 337
ZK611-11211301 31,707 234 8 51 484
ZK611-122301318 9446 540 30 145 1559
ZK611-13251388 1521 142 230 96 471
ZK611-14263171 23,933 148 5 27 266
ZK611-1527297 17,140 115 −10 16 170
ZK611-1628130 5228 185 114 46 935
ZK611-1728873 7622 56 8 44 155
ZK611-1830098 −2875 50 −8 −13 92
ZK611-19311276 −2485 63 10 275 485
ZK611-2032034 3996 47 2 36 82
ZK611-21329121 393 78 1 13 131
ZK611-2234164 −14,471 38 −3 70 52
ZK611-233501246 9843 208 7 99 312
ZK611-243628275 9881 743 32 −3 1418
ZK611-25371341 −6853 135 −1 25 106
ZK611-26379375 −15,549 58 13 31 171
ZK611-27388121 −11,722 69 71 88 369
ZK611-2840081 −11,241 0 15 65 172
ZK611-2941312,535 29,735 6409 60 80 11,126
ZK611-304212456 −1434 2070 −10 −50 1290
ZK611-314305466 690 4988 −10 −53 2286
ZK611-324406229 −11,849 3556 −5 −39 1860
ZK611-334492083 −18,142 1855 −9 −49 917
ZK611-344588747 23,952 5831 −5 38 3241
ZK611-3547017,525 28,182 18,538 30 23 7763
ZK611-364737388 5443 7033 −4 −2 2332
ZK611-374795807 12,947 5843 −10 −5 1768
ZK611-384838898 17,582 7139 −8 −6 4237
ZK611-394883980 3816 1192 −8 14 384
ZK611-404944801 14,625 1181 −6 83 766
ZK611-415004247 −619 182 14 3510 681
ZK611-4250611,794 17,360 121 430 3149 1458
ZK611-435121256 −850 −9 145 479 272
ZK611-4451825,885 5471 333 1624 11,880 4824
ZK611-455216356 13,345 195 161 4014 1294
ZK611-4652715,136 −814 497 222 12,295 3196
ZK611-4753013,699 −3753 1326 340 5408 6965
ZK611-4853633,899 28,282 1524 394 58,195 8534
ZK611-4954149,687 35,774 2193 523 58,854 10,575
ZK611-5054546,070 4395 1134 49,325 77,420 29,248
ZK611-5155114,657 776 312 2624 6277 4050
ZK611-5256010,887 −10,873 190 738 2821 3315
ZK611-5357215,812 −8081 614 5999 7265 32,376
ZK611-5458110,667 −5390 519 2602 12,145 4516
ZK611-5559012045532 1823 232272080
ZK611-5660416614372 198 30159273
Note: positive numbers indicate element influx in the system, and negative numbers indicate element efflux.
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Lan, R.; Zhu, L.; Tang, S.; Duan, Z.; Li, Y.; Ma, S. The Application of Sulfur–Metal Mass Ratios in Metal Sulfides in Assessing Prospects for Deep Metallogeny: A Case Study of the Tongshan Copper Deposit in Heilongjiang Province, Northeast China. Minerals 2024, 14, 1069. https://doi.org/10.3390/min14111069

AMA Style

Lan R, Zhu L, Tang S, Duan Z, Li Y, Ma S. The Application of Sulfur–Metal Mass Ratios in Metal Sulfides in Assessing Prospects for Deep Metallogeny: A Case Study of the Tongshan Copper Deposit in Heilongjiang Province, Northeast China. Minerals. 2024; 14(11):1069. https://doi.org/10.3390/min14111069

Chicago/Turabian Style

Lan, Ruixuan, Lixin Zhu, Shixin Tang, Zhuang Duan, Yong Li, and Shengming Ma. 2024. "The Application of Sulfur–Metal Mass Ratios in Metal Sulfides in Assessing Prospects for Deep Metallogeny: A Case Study of the Tongshan Copper Deposit in Heilongjiang Province, Northeast China" Minerals 14, no. 11: 1069. https://doi.org/10.3390/min14111069

APA Style

Lan, R., Zhu, L., Tang, S., Duan, Z., Li, Y., & Ma, S. (2024). The Application of Sulfur–Metal Mass Ratios in Metal Sulfides in Assessing Prospects for Deep Metallogeny: A Case Study of the Tongshan Copper Deposit in Heilongjiang Province, Northeast China. Minerals, 14(11), 1069. https://doi.org/10.3390/min14111069

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