Geochemical patterns of Cu, Au, Pb and Zn in stream sediments from Tongling of East China: Compositional and geostatistical insights

Journal Pre-proof Geochemical patterns of Cu, Au, Pb and Zn in stream sediments from Tongling of East China: Compositional and geostatistical insights

Xin Lin, Yangqiang Hu, Ganggang Meng, Zhang Miaomiao PII:

S0375-6742(19)30557-6

DOI:

https://doi.org/10.1016/j.gexplo.2019.106457

Reference:

GEXPLO 106457

To appear in:

Journal of Geochemical Exploration

Received date:

27 September 2019

Revised date:

18 December 2019

Accepted date:

27 December 2019

Please cite this article as: X. Lin, Y. Hu, G. Meng, et al., Geochemical patterns of Cu, Au, Pb and Zn in stream sediments from Tongling of East China: Compositional and geostatistical insights, Journal of Geochemical Exploration (2019), https://doi.org/ 10.1016/j.gexplo.2019.106457

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© 2019 Published by Elsevier.

Journal Pre-proof

Geochemical patterns of Cu, Au, Pb and Zn in stream sediments from Tongling of East China: compositional and geostatistical insights Xin Lin a, b, c, *, Yangqiang Hu d, Ganggang Meng a, Zhang Miaomiao a a School of Earth Sciences and Resources, Chang’an University, Xi’an 710054, China b Harquail School of Earth Sciences, Laurentian University, Sudbury, ON P3E 2C6, Canada c UNESCO International Center on Global-scale Geochemistry, Langfang 065000, China d The No. 815 Geological Team, Chaohu 238000, China Abstract Geochemical patterns of elements in surficial sediments are of significance in deciphering processes and locating mineral resources. Stream sediment geochemical data of four ore-forming elements (Cu, Au, Pb and Zn) and fourteen associated elements (Ag, As, Bi, Cd, La, Mn, Mo, Nb, Sb, Th, U, W, Y

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and Zr) from the Tongling Ore Cluster District (TOCD), East China were analyzed by compositional

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multivariate and geostatistical approaches. It is shown that the median values of the four ore-forming and main associated elements including Ag, As, Bi, Cd and Sb are at least two times larger than those

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in stream sediments of China, indicating that there was a noteworthy addition of such ore minerals as (gold-rich) chalcopyrite and pyrite, galena and sphalerite into the sediments. The first three factors that

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explain 71.4% of the total variance could represent the dominant geology in the TOCD including the

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felsic intrusive rocks by F1 (-) (Zr-Th-Nb-Y-La) and F2 (-) (U-Y-Th-Nb-La), Pb and Zn-bearing strata and related mineralization by F1 (+) (Pb-Cd-Ag-Mn-Sb-Zn), skarn Cu mineralization by F2 (+) (BiCu-Au-As) and porphyry Cu mineralization by F3 (-) (Mo-W). Simultaneously, regression analysis

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exhibited a closer relationship of Cu in the stream sediments with the skarn-type Cu mineralization than the porphyry counterpart. The geostatistical semivariogram modeling reflected that the greatest

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continuity of Cu, Au, Pb and Zn is at NE4°, SE95°, NE50° and NE42°, respectively. Moreover, the factor score and balance maps from factor analysis and sequential binary partition (SBP) illustrated the geochemical patterns of the elements. According to the modeling and spatial patterns, not only the possible sources (geogenic vs. anthropogenic), but also the controlling factors have determined. High levels of Cu, Bi, Mo and W are dominated by the felsic intrusives and related mineralization. That of Au, however, is governed by combined effect of the basement fault system and felsic intrusives. In contrast, high concentrations of Pb and Zn and such associated elements as Cd and Sb are controlled by the Permian and Triassic strata and associated Pb-Zn mineralization. Exploration suggestions and targets were proposed accordingly. It demonstrates that compositional and geostatistical analyses are effective to characterize geochemical patterns of elements. Keywords: Geochemical patterns; Stream sediments; Compositional; Geostatistics; Tongling

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Corresponding author. Email address: [email protected] (X. Lin)

Journal Pre-proof 1. Introduction Careful studies of geochemical patterns associated with mineralization including forms, element

ratios, associations and distribution and dispersion processes, etc. constitute one of the great essential parts for a successful exploration geochemical survey (Rose et al., 1979). Since this was introduced, much attention has been paid to either lithogeochemistry-based primary geochemical patterns (also halos) (e.g., Fedikow and Govett, 1985; Lord, 1973; Parsapoor et al., 2017; Wang et al., 2013) or sediment/soil geochemistry-based secondary geochemical patterns (e.g., Anand et al., 2016; Cohen et al., 2010; Wang et al., 2016; Xie and Yin, 1993). They were used for finding outcropped and deeply -buried mineralization through defining element associations related to certain lithologic groups or

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ore types, characterizing their distribution, relating them to known geology particularly ore-forming, weathering and transportation processes in deep-seated and surficial environments, assessing unclear

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patterns and making useful exploration suggestions. For this, various mathematical treatments such

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as multivariate analysis (e.g., Grunsky, 2010; Lin et al., 2014; Reimann et al, 2008), fractal analysis for background/anomaly separation (e.g., Agterberg, 2001; Cheng et al., 1994; Zuo and Wang, 2015)

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and geostatistical analysis (e.g., Grunsky et al., 2014; Reis et al., 2003) have been widely applied. Plus, geochemical data that are often documented as concentrations in the form of some proportions such

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as weight percent (wt.%), parts per million (ppm) or mg/kg are typical of compositional (closed) data (CoDA) (Aitchison, 1986). Absolute element concentrations that suffer from the closure effect must

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be opened by compositional analysis (e.g., Aitchison, 1986; Buccianti et al., 2006; Egozcue et al., 2003; Filzmoser et al., 2010; Reimann et al., 2017; Pawlowsky-Glahn and Buccianti, 2011).

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As an important part of the renowned Middle-Lower Yangtze River Metallogenic Belt (MLYB) in East China, the Tongling Ore Cluster District (TOCD) has attracted a great deal of attention from geologists for years. This is because not only the mineral industry is one of the major contributors to local economy, but also the TOCD is one of East China’s most historic Cu-Au-Pb-Zn polymetallic ore districts endowed with diverse genetic types of mineral deposits (Chang et al., 1991; Mao et al., 2011; Zhou et al., 2012). Yet, the TOCD has been facing a severe situation of resource shortage since major discoveries of new reserve became scarce recently. Deep exploration in the TOCD has been prioritized. Under the circumstances, however, only few exploration geochemical studies with focus on identifying anomalous concentrations in stream sediments (e.g. Yuan et al., 2012) have been reported, which is apparently inadequate considering the diversity and complexity of mineralization. Of all the ore types, three are well-recognized that are (stratabound-) skarn Cu-Au deposits such as Tongguanshan (e.g., Guo, 1957; Cui, 1987) and Dongguashan (e.g., Wang et al., 2015), porphyry Cu -Au deposits including Dongguashan (at a depth of ca. 900 m and below) and Shujiadian, etc. (e.g., Zhou et al., 2015) and hydrothermal (controversial?) Pb-Zn deposits such as Hehuashan (Liu et al., 2018; Zhu, 2013), Baoshantao and Yaojialing (Jiang et al., 2008). Although they seem to be associated

Journal Pre-proof with Mesozoic, especially late Jurassic to Cretaceous massive igneous activities in eastern China (Mao et al., 2011), they show distinctive characteristics in mineral assemblage, spatial occurrence style and areal extent. Thus, a careful study of stream sediment geochemical patterns of Cu, Au, Pb and Zn is in need and may provide implications for future deep exploration. In this paper, based on stream sediment geochemical data in the TOCD, the main objectives are to: (1) define element associations related to the three major types of mineralization; (2) characterize the distribution of four major ore-forming elements, i.e. Cu, Au, Pb and Zn and (3) interpret their patterns geologically combined with compositional multivariate analysis (e.g. factor and regression analyses and sequential binary partition) and geostatistics (semivariogram modeling). The study aims to better understand geochemical patterns of the four major ore-forming and other 14 elements in the TOCD

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and assist in deep exploration.

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2. Geological Background

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The Tongling region, being known as an important producer of copper and gold, is tectonically situated in the north-eastern margin of the Yangtze craton (Fig.1a). Marine deposits including clastic

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sedimentary rocks, carbonates and evaporates from the Silurian to the Middle Triassic dominate the area. One exception is that the Middle-Late Devonian unit is missing. Of all the strata in this region,

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Carboniferous carbonate, Permian limestone, black shale and Triassic carbonate rocks have a close relationship with the mineralization (Chang et al., 1991). The Tongling region records two tectonic

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regimes, the Indosinian compression associated with the Middle Triassic collision between the North China craton and the Yangtze craton and the Yanshanian extension between 150 and 134 Ma with

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abundant intrusive magmatism (e.g., Liu et al., 2018). The former regime resulted in the formation of a series of NE-striking folds and NE-, NW-, NS-trending faults. Mesozoic igneous rocks (Jurassic to Cretaceous) are well developed, forming more than seventy intrusions. The intrusions comprised mainly of pyroxene diorite, quartz diorite (-monzodiorite) and granodiorite are spatially controlled by the EW-trending Tongling-Nanling basement fault (Fig.1b) (Chang et al., 1991). High-precision zircon U-Pb dating (SHRIMP and LA-ICP-MS) showed that they were formed in the Early Cretaceous (ca. 145-137 Ma) (e.g., Yang et al., 2011). Deposits being discovered in the Tongling area have been geographically grouped into five ore fields (Fig.1b) that are Tongguanshan (A), Shizishan (B), Xinqiao (C), Fenghuangshan (D) and Shatanjiao (not included). The first four ore fields are characterized by (stratabound-) skarn Cu-Au deposits, porphyry Cu-Au deposits, epithermal Au deposits and hydrothermal Pb-Zn deposits (Fig.1), whereas the latter three are generally minor and porphyry Cu-Au mineralization usually occurs at depth. Studies have shown that the porphyry and (stratabound-) skarn deposits were formed in the Early Cretaceous around 143-134 Ma (e.g., Mao et al., 2006), suggesting a genetic relation to Early Cretaceous igneous rocks (e.g., Yang et al., 2011).

Journal Pre-proof Fig.1 Maps showing (a) tectonic position of the Tongling Ore Cluster District in East China and (b) surface geology of the Tongling Ore Cluster District 3. Materials and methodology 3.1 Samples, chemical analysis and quality control Stream sediment geochemical data in the TOCD were extracted from the Regional Geochemistry

- National Reconnaissance (RGNR) database. The original samples were collect at the mouth of the first- and second-order river systems at a density of 1 to 2 samples/km2. Composite samples, each was obtained from the original samples within 4 km2, were prepared for the laboratory analysis. After being air-dried, all the sediment samples were sieved to less than 220 µm by stainless steel screen. A 100-g part of each sample was ground to less than 74 µm for the analysis. X-ray fluorescence (XRF),

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atomic fluorescence spectrometry (AFS), graphite furnace atomic absorption spectrometry (GF-AAS)

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and atomic emission spectrometry (AES), etc. were deployed to determine the concentrations of 39 elements including major oxides and trace elements such as Ag, Au, Co, Cr, Cu, Mo, Pb, Zn, etc.

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The RGNR data meets the requirements of high quality. The results of quality control and any other details about the sampling and analytics can be found in Xie et al. (2012). In this contribution, 145

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stream sediment samples covering the Tongguanshan, Shizishan, Xinqiao and Fenghuangshan ore

3.2 Data analysis

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fields of TOCD were used.

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Prior to any further analysis, geochemical data must be carefully screened to account for missing values (unreported, usually appear as blank or NULL in a spreadsheet received from the laboratory)

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and censored values (greater or less than the detection limits (DLs), often refer to values lower than the DL and be marked as “
Journal Pre-proof 3.3 Compositional multivariate analysis A classical feature of a closed number system, i.e. the constant-sum constraint, implies not only a special geometry, the Aitchison geometry on the simplex, but also an inevitable interrelationship. Thus, the ratios between all variables, not the measured concentrations, carry the exclusively relative information. Taking into account the relationship of the observations of individual variables to those of the remainders makes the follow-up statistical analysis and interpretation meaningful (Aitchison, 1986; Reimann et al., 2012). Consequently, log-ratio transformation including additive log-ratio (alr), centered log-ratio (clr) and isometric log-ratio (ilr) transformation has been proposed to resolve the closure problem of geochemical data (Buccianti et al., 2006; Egozcue et al., 2003; Pawlowsky-Glahn and Buccianti, 2011). In this paper, the authors do not intend to elaborate on equations of the three

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different types of log-ratio transformation which have been introduced in many literatures. Instead,

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we focused on a simple comparison of the three different approaches:

(1) alr-transformation: in the alr, one part (usually Ti) is chosen as the common denominator of

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all the ratios, thus this variable is sacrificed. Plus, the alr-transformation is not isometric between the simplex and the real space.

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(2) clr-transformation: in the clr, geometric mean of all variables is selected as the denominator,

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no variables are sacrificed. The clr-transformation is symmetric concerning the compositional parts and it keeps the same number of variables as the number of parts in the composition. (3) ilr-transformation: the ilr is done by way of orthonormal bases. The transformation into real

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coordinates preserves all metric properties for multivariate analysis. But, the ilr-transformation is an isometry between D and D-1 components, thus it is deprived of the direct relation to the elements.

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Proper back-transformation (e.g., to the clr space) makes it interpretable geochemically. Factor analysis (FA) is a useful multivariate statistical tool to reduce the number of variables into a smaller number of new components and highlight the correlations of variables. Each component (or element association) could reflect the operation of a single process or geochemical characteristic (e.g. Rose et al., 1979; Reimann et al., 2002). Before applying the FA, the sediment geochemical data were subjected to ilr (clr back-transformed) transformation (Filzmoser et al., 2009). The number of factors (or components) was selected based on the Kaiser criterion (Kaiser, 1958), in which only factors with eigenvalues greater than 1 were retained. The normalized varimax rotation was used to maximize the variance of the factors where only factor loadings above 0.5 were considered as reliable for the task of interpretation. Based on the element associations of the FA, multivariate regression analysis was deployed by using 9 variables (Cu, Ag, As, Au, Bi, FeT, Mo, Pb and Zn). Silver, As, Au, Bi, FeT, Mo, Pb and Zn were used as independent variables to investigate the relationships of Cu with two associations, i.e. Ag, Au, Bi and Fe (skarn-type) versus Ag, As, Au, Mo, Pb and Zn (porphyry). The alr-transformation

Journal Pre-proof was applied on the 9 variables by using Ti as the common denominator. In addition, the least square fitting (LSF) method was selected. 3.4 Sequential binary partition The ilr-transformation has theoretical advantages and practical properties, especially when the concept of balance between groups of parts is considered (Buccianti, 2015; Egozcue and Pawlowsky -Glahn, 2005). In accordance with the results of factor analysis, sequential binary partition (SBP) was implemented by using 10 variables (Ag, As, Au, Bi, Cd, Cu, Pb, Zn, Sb and Mn) in order to divide them into groups of non-overlapping elements. Balances are ilr-coordinates, and the resulting D-1 ilr-coordinates represent balances between these groups in RD-1 (e.g., Egozcue and Pawlowsky-Glahn,

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2005):

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ri+ × ri− g(ci+ ) ilri (𝐱) = √ log , i = 1, 2, … , D − 1 ri+ + ri− g(ci− )

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where ci+ and ci- are the groups of parts separated in the i-th step of the SBP; ri+ and ri- are the number of parts included in ci+ and ci-, respectively, and g(⋅) is the geometric mean of its argument. Table 1

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shows the SBP of the resulting 9 balances (from ilr-1 to ilr-9).

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Table 1 Sequential binary partition (SBP) of the 10 investigated elements in 145 stream sediment samples from the Tongling Ore Cluster District (TOCD) of Anhui, East China to obtain balances (ilr1-ilr9). Parts coded with + and - are the element associations involved in the computation of the i-th order partition, respectively. 3.5 Geostatistical semivariogram modeling

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Geostatistical semivariogram modeling which measures the strength of statistical correlation of data as a function of distance and direction was used to understand the spatial structure of the stream sediment geochemical data in the TOCD. Specific semivariogram models exhibit anisotropy of data. In another word, it enables us to understand that in what distance and direction geochemical data are best autocorrelated or show the greatest continuity through semivariogram map and parameters such as range and sill (McKillup and Dyar, 2010). The process of modeling semivariograms functions fits a semivariogram curve to your empirical data. In this paper, the modeling was applied to the stream sediment geochemical data of Cu, Au, Pb and Zn in the TOCD. Before taking up the modeling, the data were log-transformed to follow normal distribution. Ordinary kriging (OK) method which gives the best linear unbiased estimation and spherical model were used. The lag size was the same to the RGNR grid spacing, i.e. 2 km. 3.6 Visualization In this paper, after careful variogram analysis, kriged raster maps of FA element associations and SBP ilr-coordinates were created. Maps were presented in the Universal Transverse Mercator (UTM)

Journal Pre-proof projection with central meridian at 117°E longitude and datum of WGS 1984. The color scale was made in accordance with the quantiles of 2.5%, 15%, 25%, 50%, 75%, 85% and 97.5%. The value of the 85% quantile was taken as the threshold to determine anomalous patterns. 4. Results and discussion 4.1 Descriptive statistics Table 2 Descriptive statistics of the 18 elements in 145 stream sediments from the Tongling Ore Cluster District (TOCD) of Anhui, East China. Table 2 presents statistics of the 18 elements in the stream sediments from the TOCD including minimum, percentiles, median, maximum together with the detection limits and median values of the

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18 elements in the stream sediments of China. Since geochemical data are typical compositional data, arithmetic mean and standard deviation based on Euclidean distances were not included in the table.

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Median value is more robust relative to average value which could be affected by extreme outliers (Reimann et al., 2008). So, the former one is used in this study to estimate the center of the sediment

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geochemical data in the TOCD. It is apparent that the median values of the main ore-forming and

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associated elements (i.e. Cu, Au, Pb, Zn, Ag, As, Bi, Cd and Sb) in the TOCD are at least two times larger than those in China, which indicates that there was probably a noteworthy addition of such ore

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minerals as (gold-bearing) chalcopyrite and pyrite, galena and sphalerite into the sediments.

4.2.1 Factor analysis

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4.2 Compositional multivariate analysis

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Table 3 Matrix of the factor loadings for the stream sediment data in the Tongling Ore Cluster District (TOCD) (after varimax rotation of ilr-transformed data) Table 3 presents the results of compositional factor analysis. Three factors with eigenvalues lager than 1 were retained in the model. The three factors, named F1, F2 and F3, explain 47.83%, 14.98% and 8.62% variability, respectively. In total, they account for 71.43% of the total variance. Variables with the absolute value of factor loadings over 0.5 were considered reliable elements to describe the composition of each factor (shown in bold and italic in Table 3). Therefore, F1 has positive loadings in Pb, Cd, Ag, Mn, Sb and Zn and negative loadings in Zr, Th, Nb, Y and La; F2 has positive loadings in Bi, Cu, Au and As and negative loadings in U, Y, Th, Nb and La; as for F3, As and Sb occupy the positive loadings and Mo and W dominate the negative ones. The possible sources for the obtained element associations are summarized in Table 4. Table 4 Explanation of the 3 factors extracted from the compositional factor analysis of the stream sediment data in the Tongling Ore Cluster District (TOCD) Fig.2 Kriged raster maps showing the distribution of factor scores of (a) F1, (b) F2 and (c) F3

Journal Pre-proof Figs. 2(a), 2(b) and 2(c) show the kriged raster maps of factor scores of F1, F2 and F3, respectively. High factor scores (> 85% percentile, > 1.17) of F1 are distributed in the south-eastern part of the TOCD (Fig.2a) where Silurian to Triassic carbonate and clastic rocks dominate (Fig.1b). Meanwhile, the high factor scores of F1 spatially correspond to two anticlinal structures in which most Pb-Zn ore deposits in the TOCD are hosted. The most south-eastern part is overwhelmed by moderate to high factor scores (> 0.76) of F1. As for low factor scores (< 25% percentile, < -0.81), they prevail in the north-western part of the TOCD (Fig.2a) where granodiorite, granodiorite porphyry and marble are outcropped. It should also keep in mind that it does not necessarily mean that there are not intrusive rocks in regions showing none of low factor scores of F1, for instance Xinqiao and Fenghuangshan Cu-(Au) Ore Fields (C and D in Fig.1b). In addition, the Shizishan Ore Field lies in the transitional

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zone of the high and low factor scores of F1.

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High factor scores (> 85% percentile, > 0.96) of F2 are related to such known Cu-(Au) ore fields as Tongguanshan, Shizishan, Xinqiao and Fenghuangshan from the west to the east (Figs.1 and 2b).

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The intrusive rocks in the aforesaid areas are also characterized by high concentrations of Cu and Au (e.g., AHBGMR, 1987). In addition, the factor scores generally decrease outwards from the centers

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of the ore fields. Low factor scores (< 25% percentile, < -0.66) are scattered in the north and south of

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the TOCD where Quaternary sediments such as alluvium are developed. Thus, negative F2 seems to be a reflection of accumulation and preservation of incompatible High Field Strength Elements (HFSEs) in the stream sediments probably due to weathering of the intrusive rocks.

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In the F3 factor score map (Fig.2c), high factor scores (> 85% percentile, > 0.83) are mapped in the south-central part of the TOCD, with the peak values occurred in the south of the Yongcunqiao

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anticline where Hehuashan (number 5 in Fig.1b, metal reserves larger than 0.7 Mt) and other Pb-Zn deposits/occurrences were discovered (Fig.2c). In a regional perspective, the high scores of F3 are spatially related to the Permian and Triassic strata (Fig.1b). Low factor scores (< 25% percentile, < -0.59) of F3, on the other hand, show general spatial agreement with the Tongguanshan, Xinqiao and Fenghuangshan Cu-(Au) Ore Fields. Molybdenum and W are usually used as pathfinder elements for porphyry Cu-(Mo) deposits (Rose et al., 1979). The distribution pattern of negative F3 may suggest that there is potential for porphyry Cu-(Mo) mineralization in the abovementioned three ore fields. However, there are not low F3 factor scores in the Shizishan Ore Field which hosts the Dongguashan porphyry Cu deposit. This is because: (1) the Shizishan is also dominated by high concentrations of As and Sb; and (2) compared to adjacent Tongguanshan, the Shizishan is characterized by relatively moderate levels of Mo and W. This again suggests that it does not necessarily indicate that certain areas absent of particular element associations in multivariate element maps are not developed with related processes. Moreover, it can be seen that the dominant geologic units in the TOCD (Table 4) are well demonstrated by the three factors.

Journal Pre-proof 4.2.2 Regression analysis Compositional regression analysis was implemented by using the alr-transformed data of nine elements (Cu, Ag, As, Au, Bi, FeT, Mo, Pb and Zn) in order to examine the relationships between dependent (Cu/Ti) and independent (Ag/Ti, Au/Ti, Bi/Ti, FeT/Ti) and (Ag/Ti, As/Ti, Au/Ti, Mo/Ti, Pb/Ti, Zn/Ti) alr-coordinates. In general, skarn-type Cu deposits are indicated by associations of Ag, Au, Bi and Fe and porphyry Cu deposits are associated with Ag, As, Au, Mo, Pb and Zn (e.g., Rose et al., 1979). Therefore, regression analysis was used to investigate the relationships between major component (i.e. Cu) of two types of Cu deposits in the TOCD and significant associated elements. The result showed relatively strong positive linear relationship between Cu/Ti and Ag/Ti, Au/Ti, Bi/Ti, FeT/Ti (R2=0.721) and moderate positive linear relationship between Cu/Ti and Ag/Ti, As/Ti, Au/Ti,

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Mo/Ti, Pb/Ti, Zn/Ti (R2=0.593) (Fig.3). In addition, all the independent variables contribute to the

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predicted model according to the t-test statistics. It is apparent that the positive correlation decreased from element association of skarn-type Cu deposit to that of porphyry Cu deposit. This is interpreted

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as an effect of mineralization dominance and depth. In the TOCD, (strata-bound) skarn Cu deposits outnumber porphyry Cu deposits and the former type usually occurs at a relatively shallow depth

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than the latter one. Moreover, the positive correlation between Cu/Ti and Ag/Ti, As/Ti, Au/Ti, Mo/Ti,

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Pb/Ti, Zn/Ti coupled with high levels of F2 (+) and F3 (-) (Figs. 2b and 2c) is indicative of porphyry Cu-(Mo, Au) mineralization at depth in the TOCD.

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4.2.3 Balance (ilr) maps

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Fig.3 Regression analysis of the alr-transformed Cu (dependent variable) against Ag, Au, Bi and FeT and Ag, As, Au, Mo, Pb and Zn (independent variables)

Based on the results of factor analysis, 10 variables (Ag, As, Au, Bi, Cd, Cu, Pb, Zn, Sb and Mn) have been selected to conduct sequential binary partition (SBP) and balance (ilr) maps were obtained. Here we focus on the following four balance (ilr) maps: ilr-1 (PbZnAgSbCdMn/CuAuAsBi) (Fig.4a), ilr-2 (PbZn/AgSbCdMn) (Fig.4b), ilr-7 (CuAu/AsBi) (Fig.4c) and ilr-8 (Cu/Au) (Fig.4d). Fig.4 Kriged raster balance (ilr) maps showing the distribution of (a) ilr-1, (b) ilr-2, (c) ilr-3 and (d) ilr-4 4.2.3.1 Ilr-1 map (PbZnAgSbCdMn/CuAuAsBi) The ilr-1 map reveals that higher concentrations of Pb, Zn, Ag, Sb, Cd and Mn are distributed in the south-eastern part of the TOCD (Fig.4a). This resembles the distribution pattern of F1 (+) (Fig.2a). The high concentrations spatially correspond to the Permian to Triassic carbonate rocks. As for Cu, Au, As and Bi, the high contents are observed in the well-known Tongguanshan, Shizishan, Xinqiao and Fenghuangshan Cu-Au Ore Fields, especially for the former two. In addition, there are moderate to high abundance of Cu-Au-As-Bi in the northwest of Xinqiao and the southwest of Tongguanshan.

Journal Pre-proof This again verifies the close relationship of Pb, Zn, Ag, Sb, Cd and Mn with the Late Paleozoic to Mesozoic strata and of Cu, Au, As and Bi with the Mesozoic intrusive rocks. 4.2.3.2 Ilr-2 map (PbZn/AgSbCdMn) The ilr-2 map is obtained on the basis of subdivision of Pb, Zn, Ag, Sb, Cd and Mn (Fig.4b) by taking into account the results of factor analysis (Table 4). It is apparent that the northern part of the TOCD is occupied by uniformly high Pb-Zn abundance. In contrast, the southern part is characterized by alternative distribution of high values of (Pb, Zn) and (Ag, Sb, Cd, Mn). Generally, the low-lying northern part in the TOCD covered by alluvial sediments is typical of urbanized and populated area in East China. Thus, the northern high abundance of Pb and Zn may indicate other sources of Pb and

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Zn, probably anthropogenic. Silver, Sb, Cd and Mn reflect a low-temperature element combination

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and are associated with local hydrothermal Au and Pb-Zn mineralization. 4.2.3.3 Ilr-7 map (CuAu/AsBi)

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The seventh balance map is based on the ratio of (Cu, Au) and (As, Bi) (Fig.4c). It reveals that the higher Cu and Au proportion zones are mainly related to the Cu-Au ore fields. The high proportion

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of Cu and Au in the Tongguanshan and Shizishan Ore Fields clearly extends northwesterly due to the

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effect of topography. Furthermore, a higher proportion zone of Cu and Au was identified in the northwest of Xinqiao, which has not been detected by single element maps of either Cu or Au. It

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may indicate potential in Cu-Au mineralization. The high As-Bi proportion, in contrast, is primarily distributed northeasterly crossing the TOCD.

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4.2.3.4 Ilr-8 map (Cu/Au)

The ilr-8 map is based on the ratio of Cu and Au (Fig.4d). It unveils that the higher Cu proportion zones are primarily related to the Quaternary sediments (Fig.1b) which are closer to the Mesozoic intrusive rocks in space. The high Cu abundance in the sediments results from weathering of the Cu -bearing intrusives and subsequent accumulation. One of the high Cu proportion zones is related to the Jurassic/Cretaceous volcanic basin in the northeast of the TOCD. The higher Au proportion is distributed latitudinally and covers the four ore fields. This distribution pattern is mainly related to the EW-trending Tongling-Nanling basement fault and adjacent second-order faults. 4.3 Semivariogram modeling Fig.5 Semivariogram maps for (a) Cu, (b) Au, (c) Pb and (d) Zn The variogram maps for Cu, Au, Pb and Zn are shown in Fig.5. It is clear that except for Cu where the anisotropy is geometric with direction of greatest continuity at NE4° (Fig.5a), the anisotropy is zonal. In addition, gold (Fig.5b) shows strong zonal anisotropy with direction of greatest continuity at SE95°. Lead (Fig.5c) and zinc (Fig.5d) show strong zonal anisotropy with directions of greatest

Journal Pre-proof continuity at NE50° and NE42°, respectively. The variogram models for the four elements were fitted by using the spherical model. Related parameters are tabulated in Table 5. Through the semivariogram modeling, it can be seen that the four major ore-forming elements, i.e., Cu, Au, Pb and Zn present distinct spatial structures or patterns which can be directly linked to geology in the TOCD (Fig.1b). For Cu, the direction of greatest continuity is consistent with that of outcropped areas of intrusive rocks, especially in the Tongguanshan, Shizishan and Xinqiao. At the same time, a relatively small range (Table 5) indicates that the greatest continuity or autocorrelation is confined in a limited spatial extent such as within a certain intrusive rock in this case. In contrast, the greatest continuity of gold agrees with the EW-trending basement fault in the study area and the directions of greatest continuity of Pb and Zn are related with the northeasterly Permian and Triassic

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strata mainly comprised of carbonate and clastic rocks (Fig.1b). The results are in good agreement

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with the spatial patterns from the balance (ilr) maps.

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4.4 Implications for deep exploration

The element associations defined by the compositional factor analysis could basically represent

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the dominant geology in the TOCD including the felsic intrusive rocks, Pb and Zn -rich strata and the three major types of mineralization (see above). Regression analysis revealed that Cu in the stream

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sediments, to a great extent, may be a reflection of skarn mineralization which occupies a relatively shallow depth in the TOCD. It would probably disguise the geochemical signatures associated with

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porphyry mineralization which generally occurs at depth. Considering extensive mining activities at the same time, thus secondary geochemical patterns derived from such surface sediments as soil and

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stream sediments are not the first recommended for porphyry Cu(-Au) mineralization exploration. Lithogeochemical survey, if coupled with other methods such as magnetic geophysics, may provide insight into locating buried mineralized intrusives. Furthermore, semivariogram modeling results for Cu have shown limited autocorrelation. Consequently, high-density lithogeochemistry should better be used for future Cu exploration. However, it is not exactly the case for Pb and Zn. Although known Pb-Zn deposits in the TOCD, particularly the large ones, occur at a relatively deep position, they can be vividly depicted by the stream sediment geochemical patterns. Future attention should be paid to anomalous Pb-Zn concentrations in the stratigraphic units especially the Permian and Triassic. At the same time, Ag, Cd and Sb are important associated elements. In contrast with Cu, the semivariogram modeling results for Pb and Zn suggest that relatively low-density geochemical survey is adequate. The factor score and balance (ilr) maps have demonstrated that the geochemical patterns of high levels of Cu, Bi, Mo and W are primarily dominated by the felsic intrusives and related mineralization and that of Au is governed by both the basement fault system and felsic intrusives. In contrast, the patterns of high contents of Pb and Zn and such associated elements as Ag, Cd and Sb are mainly controlled by the Permian and Triassic strata of limestone and dolostone especially those in the south-

Journal Pre-proof eastern part of the TOCD and associated Pb-Zn polymetallic mineralization. It is suggested that the known ore fields including Tongguanshan, Shizishan, Xinqiao and Fenghuangshan have a potential for porphyry Cu (-Au) mineralization. Anomalous Pb-Zn concentrations that exist in folds such as the Yongcunqiao anticline (Fig.1b) are probably surface expressions of deep Pb-Zn mineralization. Meanwhile, the ilr-7 balance map identified the northwest of Xinqiao as a potential area for Cu-Au mineralization where further field examination is needed. 5. Conclusions This study presents compositional multivariate (i.e. factor and regression analyses and sequential binary partition) and geostatistical analyses carried out on 4 major ore-forming elements (Cu, Au, Pb

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and Zn) and 14 associated elements including Ag, As, Bi, Cd, La, Mn, Mo, Nb, Sb, Th, U, W, Y and Zr in the stream sediments from the Tongling Ore Cluster District (TOCD). The element associations

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determined by compositional factor analysis could represent the dominant geology in the TOCD such

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as the Mesozoic felsic intrusives and the three major types of mineralization. Multivariate regression analysis confirmed a relatively close relationship of Cu in the stream sediments with the skarn-type

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Cu mineralization compared to the porphyry counterpart. The semivariogram modeling reflected the spatial structures of the elements including greatest continuity and range. Moreover, the factor score

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and balance (ilr) maps illustrated the geochemical patterns of the elements. Based on the modeling results and patterns, not only the possible sources (geogenic vs. anthropogenic) but also the controlling

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factors of the elements have determined. High contents of Cu, Bi, Mo and W are primarily dominated by the felsic intrusive rocks and related mineralization. That of Au is governed by both the basement

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fault system and felsic intrusives. In contrast, high levels of Pb and Zn and such associated elements as Cd and Sb are controlled by the Permian and Triassic strata and associated Pb-Zn polymetallic mineralization. Exploration suggestions and targets were proposed accordingly. This contribution demonstrates that compositional multivariate and geostatistical analyses are of great significance to characterize geochemical patterns of elements. Acknowledgements The authors thank the State Key Research & Development Project (2016YFC0600601) and the Natural Science Foundation of China (41702213) for the financial support. Special thanks are given to related people and departments involved in this project.

Journal Pre-proof References Agterberg, F. P., 2001. Multifractal simulation of geochemical map patterns. J. China Uni. Geo. 1, 31-39. Aitchison, J., 1986. The Statistical Analysis of Compositional Data. Monographs on Statistics and Applied Probability. Chapman & Hall Ltd, London. 416 pp. Anand, R. R., Aspandiar, M. F., Noble, R. R. P., 2016. A review of metal transfer mechanisms through transported cover with emphasis on vadose zone within the Australian regolith. Ore Geol. Rev. 73, 394-416. Anhui Bureau of Geology and Mineral Resources, 1987. Regional Geology of Anhui Province.

of

721pp. Buccianti, A., Mateu, F., Pawlowsky, G., 2006. Compositional Data Analysis in the Geosciences:

ro

from Theory to Practice. Geological Society of London, 264, 67-77.

Buccianti, A., Grunsky, E., 2014. Compositional data analysis in geochemistry: are we sure to see

-p

what really occurs during natural processes? J. Geochem. Explor. 141, 1-5.

re

Buccianti, A., 2015. The FOREGS repository: modelling variability in stream water on a continental scale revising classical diagrams from CoDA (compositional data analysis)

lP

perspective. J. Geochem. Explor. 154, 94-104.

Chang, Y., Liu, X., Wu, C., 1991. The copper-iron belt of the lower and middle reaches of the

na

Changjiang River. Geological Publishing House, Beijing, 379pp. Cheng, Q., Agterberg, F. P., Ballantyne, S. B., 1994. The separation of geochemical anomalies from background by fractal methods. J. Geochem. Explor. 51, 109-130. House, Beijing.

Jo ur

Chi, Q., Yan, M., 2007. Abundance of Elements for Applied Geochemistry. Geological Publishing Cohen, D. R., Kelley, D, L., Coker, W, B., 2010. Major advances in exploration geochemistry 1998-2007. Geochem. Explor. Env. A. 10, 3-16. Cui, B., 1987. The alteration zoning and origin of the Tongguanshan stratabound skarn type copper deposit. Mineral Deposit. 6, 35-44. Egozcue, J. J., Pawlowsky, G., Mateu, F., Barceló, V., 2003. Isometric logratio transformations for compositional data analysis. Math. Geol. 35 (3), 279-300. Egozcue, J. J., Pawlowsky, G., 2005. Groups of parts and their balances in compositional data analysis. Math. Geol. 37, 795–828. Fedikow, M. A. F., Govett, G. J. S., 1985. Geochemical alteration halos around the Mount Morgan gold-copper deposit, Queensland, Australia. J. Geochem. Explor. 24, 247-272. Filzmoser, P., Hron, K., Reimann, C., Garrett, R., 2009. Robust factor analysis for compositional data. Comput. Geosci. 35, 1854-1861. Filzmoser, P., Hron, K., Reimann, C., 2010. Bivariate statistical analysis of environmental

Journal Pre-proof (compositional) data. Sci. Total. Environ. 408, 4230-4238. Grunsky, E. C., 2010. The interpretation of geochemical survey data. Geochem. Explor. Environ. Anal. 10, 27-74. Grunsky, E. C., Mueller, U. A., Corrigan, D., 2014. A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: applications for predictive geological mapping. J. Geochem. Explor. 141, 15-41. Guo, W., 1957. On the genesis of Tongguanshan Copper Mine in Anhui Province. Acta Geol. Sin. 37, 316-322. Hron, K., Templ, M., Filzmoser, P., 2010. Imputation of missing values for compositional data using classical and robust methods. Comput. Stat. Anal. 54, 3095-3107.

of

Jiang, Q., Zhao, Z., Huang, J., 2008. Discovery of the Yaojialing copper-lead-zinc deposit in Nanling Anhui and its significance. Geology in China. 35, 314-321.

ro

Kaiser, H. F., 1958. The varimax criterion for analytical rotation in factor analysis. Psychometrika.

-p

23, 187-200.

Lin, X., Wang, X., Zhang, B., Yao, W., 2014. Multivariate analysis of regolith sediment

re

geochemical data from the Jinwozi gold field, north-western China. J. Geochem. Explor. 137, 48-54.

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Liu, G., Yuan, F., Deng, Y., Jowitt, S. M., Sun, W., White, N. C., Yang, D., Li, X., Zhou, T., Huizenga, J. M., 2018. The genesis of the Hehuashan Pb-Zn deposit and implication for the

na

Pb-Zn prospectivity of the Tongling district, Middle-Lower Yangtze River metallogenic Belt, Anhui Province, China. Ore Geol. Rev. 101, 105-121.

Jo ur

Lord, J. R., 1973. Surface and subsurface geochemistry of the native Bee-Jasper - Biotite copper prospects, northwest Queensland. J. Geochem. Explor. 2, 349-365. Mao, J., Wang, Y., Lehmann, B., Yu, J., Du, A., Mei, Y., Li, Y., Zang, W., Stein, H. J., Zhou, T., 2006. Molybdenite Re-Os and albite 40Ar-39Ar dating of Cu-Au-Mo and magnetite porphyry systems in the Yangtze River valley and metallogenic implications. Ore Geol. Rev. 29, 307-324. Mao, J., Xie, G., Duan, C., Pirajno, F., Ishiyama, D., Chen, Y., 2011. A tectono genetic model for porphyry-skarn-stratabound

Cu-Au-Mo-Fe

and

magnetite-apatite

deposits

along

the

Middle-Lower Yangtze River Valley, Eastern China. Ore Geol. Rev. 43 (1), 294-314. McKillup, S., Dyar, M. D., 2010. Geostatistics Explained: An Introductory Guide for Earth Scientists. Cambridge University Press. Parsapoor, A., Khalili, M., Maghami, M., 2017. Discrimination between mineralized and unmineralized alteration zones using primary geochemical haloes in the Darreh-Zar porphyry copper deposit in Kerman, southeastern Iran. J. Afr. earth sci. 132, 109-126. Pawlowsky, G., Buccianti, A., 2011. Compositional data analysis. Theory and Applications. John Wiley & Sons, Ltd, London 400 pp.

Journal Pre-proof Reimann, C., Filzmoser, P., Garrett, R. G., 2002. Factor analysis applied to regional geochemical data: problems and possibilities. Appl. Geochem. 17, 185-206. Reimann, C., Filzmoser, P., Garrett, R. G., Dutter, R., 2008. Statistical Data Analysis Explained. Applied Environmental Statistics with R. Wiley, Chichester. 343pp. Reimann, C., Filzmoser, P., Fabian, K., Hron, K., Birke, M., Demetriades, A., Dinelli, E., Ladenberger, A., GEMAS Project Team, 2012. The concept of compositional data analysis in practice total major element concentrations in agricultural and grazing land soils of Europe. Sci. Total. Environ. 426, 196-210. Reimann, C., Filzmoser, P., Hron, K., Kynčlová, P., Garrett, R. G., 2017. A new method for correlation analysis of compositional (environmental) data a worked example. Sci. Total.

of

Environ. 607, 965-971.

Reis, A. P., Sousa, A. J., Fonseca, E. C., 2003. Application of geostatistical methods in gold

ro

geochemical anomalies identification (Montemor-O-Novo, Portugal). 77, 45-63.

-p

Rose, A. W., Hawkes, H. E., Webb, J. S., 1979. Geochemistry in Mineral Exploration. Academi Press, London. 25 pp.

re

Wang, C., Carranza, E. J. M., Zhang, S., Zhang, S., Zhang, J., Liu, X., Zhang, D., Sun, X., Duan, C., 2013. Characterization of primary geochemical haloes foe gold exploration at the

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Huanxiangwa gold deposit, China. J. Geochem. Explor. 124, 40-58. Wang, S., Zhou, T., Yuan, F., Fan, Y., Zhang, L., Song, Y., 2015. Petrogenesis of Dongguashan

na

skarn-porphyry Cu-Au deposit related intrusion in the Tongling district, eastern China: Geochronological, mineralogical, geochemical and Hf isotopic evidence. Ore Geol. Rev. 64,

Jo ur

53-70.

Wang, X., Zhang, B., Lin, X., Xu, S., Yao, W., Ye, R., 2016. Geochemical challenges of diverse regolith-covered terrains for mineral exploration in China. Ore Geol. Rev. 73, 417-431. Xie, X., Yin, B., 1993. Geochemical patterns from local to global. J. Geochem. Explor. 47, 109-129.

Xie, X., Ren, T., Sun, H., 2012. Geochemical Atlas of China. Geological Publishing House, Beijing. 135 pp. Yuan, F., Li, X., Jowitt, S. M., 2012. Anomaly identification in soil geochemistry using multifractal interpolation: A case study using the distribution of Cu and Au in soils from the Tongling mining district, Yangtze metallogenic belt, Anhui province, China. J. Geochem. Explor. 116-117, 28-39. Yang, S., Jiang, S., Li, L., Sun, Y., Sun, M., Bian, L., Xiong, G., Gao, Z., 2011. Late Mesozoic magmatism of the Jiurui mineralization district in the Middle-Lower Yangtze River Metallogenic Belt Eastern China: Precise U-Pb ages and geodynamic implications. Gondwana Res. 20, 831-843.

Journal Pre-proof Zhou, T., Fan, Y., Yuan, F., Zhang, G., 2012. Progress of geological study in the Middle-Lower Yangtze River Valley metallogenic belt. Acta Petro. Sin. 28(10), 3051-3066. Zhou, T., Wang, S., Fan, Y., Yuan, F., Zhang, D., White, N. C., 2015. A review of the intracontinental porphyry deposits in the Middle-Lower Yangtze River Valley metallogenic belt, Eastern China. Ore Geol. Rev. 65, 433-456. Zhu, X., 2013. Geological characteristics and prospecting criteria of lead, zinc and silver polymetallic deposits in Hehuashan area, Tongling County, Anhui Province. The Earth, 8, 28-29. Zuo, R., Wang, J., 2015. Fractal/multifractal modeling of geochemical data: A review. Geochem.

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ro

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Explor. 164, 33-41.

Journal Pre-proof Table 1 Sequential binary partition (SBP) of the 10 investigated elements in 145 stream sediment samples from the Tongling Ore Cluster District (TOCD) of Anhui, East China to obtain balances (ilr1-ilr9). Parts coded with + and - are the element associations involved in the computation of the i-th order partition, respectively. Ag + -

Sb + -

Cd + -

Mn + -

+ +

+ -

-

-

+

-

Cu -

Au -

As -

Bi -

+ +

+ -

-

-

+

-

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Zn + + -

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ilr-1 ilr-2 ilr-3 ilr-4 ilr-5 ilr-6 ilr-7 ilr-8 ilr-9

Pb + + +

Journal Pre-proof Table 2 Descriptive statistics of the 18 elements in 145 stream sediments from the Tongling Ore Cluster District (TOCD) of Anhui, East China. Parameters Eleme nts

Detect ion Limits

Stream Sediment Samples (N=145) Mi n.

Q50

20 ng/g

40. 0

As

1 µg/g

5.5

8.9

10.5 11.7

21.1

34.7

46.0

79.8

0.6

1.6

2.4

3.0

5.4

11.6

16.7

73.8

0.26 0.30 0.39

0.60

1.38

2.32

139. 205. 272. 0 8 0

638. 0

155 1.0

20.2 25.6 30.0

44.0

62.0

34.2 38.0 40.0

44.0

Mn Mo Nb Pb Sb Th U W

842. 0

178 8.8

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57.8

847. 0

134 7.0

205 0.4

310 0.2

0.8

1.1

1.9

2.6

3.5

15.0 16.0 17.0

18.0

20.0

21.0

23.0

22.0 27.0 32.0

55.0

118. 0

201. 6

391. 4

0.61 0.70 0.88

1.50

2.70

3.62

8.44

360. 484. 571. 6 6 0 0.6

0.7

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51.0

4 µg/g

7.0

12.0 13.0 13.0

15.0

16.0

16.0

17.0

0.5 µg/g 0.5 µg/g

1.4 0

1.92 2.40 2.60

3.00

3.60

3.84

4.68

1.0

1.7

2.5

3.0

3.6

5.4

2.0

2.2

17. 24.0 26.0 28.0 30.0 33.0 34.0 0 10 34. 105. 146. 242. 345. Zn 69.2 85.6 µg/g 0 0 0 2 0 10 197 222. 252. 268. 316. 366. 389. Zr µg/g .0 8 0 0 0 4 0 Note: China: stream sediment data from Chi and Yan (2007). MAD: robust coefficient of variation (Reimann et al., 2008) Y

4.87

49.0

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La

Q95

388 8.0 214. 8

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Cu

Q85

259 6.8 102. 6

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Cd

0.1 1 72. 0 11. 1 µg/g 0 30 30. µg/g 0 30 274 µg/g .0 0.4 0.4 µg/g 12. 5 µg/g 0 20. 2 µg/g 0 0.1 0.4 µg/g 0

Q25

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Bi

0.3 ng/g 0.1 µg/g 50 ng/g

Q15

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Ag

Q75 Medi an 101. 192. 480. 59.0 85.2 0 0 0

Au

Q5

5 µg/g

36.0 626. 0 463. 8 median

Max MA . D 512 3.0 419. 1 288. 0 31.2 0 782 1.0 771. 0

CV R

158 82.6 .6 % 15. 71.1 0 % 4.4 81.5 % 0.4 66.7 % 621 97.4 .2 % 22. 50.5 2 % 7.4 16.8 63.0 % 811 521 61.6 0.0 .9 % 0.6 54.5 6.2 % 3.0 16.7 32.0 % 216 43. 78.2 0.0 0 % 40.0 1.2 80.0 0 % 1.5 10.0 20.0 % 0.6 20.0 6.00 % 0.4 16.0 10.0 % 4.4 14.7 49.0 % 134 90. 61.9 0.0 4 % 531. 71. 22.5 0 2 % absolute deviation;

Chin a Medi an 77.0 10.0 1.3 0.31 140. 0 22.0 39.0 670. 0 0.8 16.0 24.0 0.69 11.9 2.45 1.8 25.0 70.0 270. 0 CVR:

Journal Pre-proof Table 3 Matrix of the factor loadings for the stream sediment data in the Tongling Ore Cluster District (TOCD) (after varimax rotation of ilr-transformed/clr back-transformed data)

47.83

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% of variance explained

14.98

Communalities 0.737 0.711 0.611 0.753 0.626 0.798 0.770 0.628 0.740 0.822 0.842 0.775 0.869 0.450 0.622 0.867 0.428 0.810 \ \

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F3 0.21 0.51 0.24 0.04 -0.01 -0.24 -0.09 0.22 -0.85 -0.03 0.21 0.51 0.13 -0.13 -0.66 0.13 0.16 -0.08 1.552

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Factors F2 0.38 0.53 0.73 0.87 0.06 0.84 -0.52 -0.19 0.05 -0.53 -0.08 0.15 -0.54 -0.58 -0.12 -0.56 0.02 -0.45 2.697

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Ag As Au Bi Cd Cu La Mn Mo Nb Pb Sb Th U W Y Zn Zr Eigenvalues

F1 0.74 0.41 0.13 0.04 0.79 -0.18 -0.70 0.74 0.09 -0.74 0.89 0.70 -0.75 -0.31 -0.42 -0.74 0.63 -0.77 8.609

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Variables

8.62

Journal Pre-proof Table 4 Explanation of the 3 factors extracted from the compositional factor analysis of the stream sediment data in the Tongling Ore Cluster District (TOCD) Fact ors

F1

Element associations (1) Pb-Cd-Ag-Mn-S b-Zn (2) Zr-Th-Nb-Y-La (1) Bi-Cu-Au-As

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(2) Mo-W

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(1) As-Sb F3

2. Intermediate to felsic intrusive rocks (e.g., granodiorite, granodiorite porphyry and monzogranite) 1. Cu-Au ore deposits (probably skarn type) and related intrusive rocks 2. Intermediate to felsic intrusive rocks (e.g., granodiorite, granodiorite porphyry and monzogranite) 1. Stratigraphic units and epithermal ore deposits such as Pb-Zn mineralization in this region 2. Mo-(Cu) mineralization (probably porphyry-type?) or Cu deposits-related alteration

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(2) U-Y-Th-Nb-La

1. Stratigraphic units (particularly Permian and Triassic) and Pb-Zn ore deposits

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F2

Interpretation (possible sources)

Journal Pre-proof Table 5 Semivariogram models and parameters for Cu, Au, Pb and Zn Elements Pb

Zn

5343.2 Spherical

0.733 Spherical

0.231 Spherical

0.275 Spherical

5781.8

0.637

0.785

0.225

4° (3.9km, 6km)

95° (6km, 18km)

50° (12.5km, 20km)

42° (7.5km, 18km)

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Au

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Nugget Type Partial Sill Direction Range

Cu

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Conflict of Interest The authors certify that there is no conflict of interest.

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Highlights  Geochemical patterns of Cu, Au, Pb and Zn in sediments of Tongling were studied.  Compositional multivariate analysis is useful to characterize geochemical patterns.  Deep exploration suggestions and targets in Tongling were proposed.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5