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Volume 32 Issue 2
Apr.  2021
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Jiangnan Zhao, Shouyu Chen. Identification of the Ore-Forming Anomaly Component by MSVD Combined with PCA from Element Concentrations in Fracture Zones of the Laochang Ore Field, Gejiu, SW China. Journal of Earth Science, 2021, 32(2): 427-438. doi: 10.1007/s12583-021-1423-1
Citation: Jiangnan Zhao, Shouyu Chen. Identification of the Ore-Forming Anomaly Component by MSVD Combined with PCA from Element Concentrations in Fracture Zones of the Laochang Ore Field, Gejiu, SW China. Journal of Earth Science, 2021, 32(2): 427-438. doi: 10.1007/s12583-021-1423-1

Identification of the Ore-Forming Anomaly Component by MSVD Combined with PCA from Element Concentrations in Fracture Zones of the Laochang Ore Field, Gejiu, SW China

doi: 10.1007/s12583-021-1423-1
More Information
  • Fault and fractures are well-developed in the Gejiu tin-polymetallic district, and they are closely related to the formation and distribution of ores. In this paper, the principal component analysis (PCA) and multifractal singular value decomposition (MSVD) methodologies were applied for identification of the ore-forming anomaly components from element concentrations of fault rocks in the Laochang ore field, Gejiu. The results show that: (1) the wall rocks and fault rocks have anomalous concentrations of ore-forming elements, indicating that these elements are mainly derived from fluid/rock interaction in the fracture zones; (2) PCA based on clr-transformed data was used to recognize significant association anomalies of ore-forming elements, which lay a foundation for further extracting ore-forming anomaly components from the element association anomalies related to Sn-Cu mineralization; (3) MSVD could effectively explore local anomaly features and decompose ore-forming element association anomalies associated with buried mineralization in more detail. The ore-forming element anomaly components can delineate ore-finding Sn-Cu polymetallic deposits more exactly than the ore-forming element association anomalies.
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Identification of the Ore-Forming Anomaly Component by MSVD Combined with PCA from Element Concentrations in Fracture Zones of the Laochang Ore Field, Gejiu, SW China

doi: 10.1007/s12583-021-1423-1

Abstract: Fault and fractures are well-developed in the Gejiu tin-polymetallic district, and they are closely related to the formation and distribution of ores. In this paper, the principal component analysis (PCA) and multifractal singular value decomposition (MSVD) methodologies were applied for identification of the ore-forming anomaly components from element concentrations of fault rocks in the Laochang ore field, Gejiu. The results show that: (1) the wall rocks and fault rocks have anomalous concentrations of ore-forming elements, indicating that these elements are mainly derived from fluid/rock interaction in the fracture zones; (2) PCA based on clr-transformed data was used to recognize significant association anomalies of ore-forming elements, which lay a foundation for further extracting ore-forming anomaly components from the element association anomalies related to Sn-Cu mineralization; (3) MSVD could effectively explore local anomaly features and decompose ore-forming element association anomalies associated with buried mineralization in more detail. The ore-forming element anomaly components can delineate ore-finding Sn-Cu polymetallic deposits more exactly than the ore-forming element association anomalies.

Jiangnan Zhao, Shouyu Chen. Identification of the Ore-Forming Anomaly Component by MSVD Combined with PCA from Element Concentrations in Fracture Zones of the Laochang Ore Field, Gejiu, SW China. Journal of Earth Science, 2021, 32(2): 427-438. doi: 10.1007/s12583-021-1423-1
Citation: Jiangnan Zhao, Shouyu Chen. Identification of the Ore-Forming Anomaly Component by MSVD Combined with PCA from Element Concentrations in Fracture Zones of the Laochang Ore Field, Gejiu, SW China. Journal of Earth Science, 2021, 32(2): 427-438. doi: 10.1007/s12583-021-1423-1
  • Geochemical surveys are frequently used in mineral prospecting and target prediction for different types of deposits (Zuo, 2020; Zhao et al., 2015; Zuo et al., 2013; Cohen et al., 2010; Carranza, 2009). The critical challenge is how to use surface samples to explore and locate deeply concealed ores in areas with thick, transported regolith or rock cover (Gonçalves and Mateus, 2019; Cheng, 2012). To address this issue, a great progress of geochemical exploration methods has been made in the past decades from three aspects: elements migration mechanism, geochemical anomalies identification, and geochemical techniques of sampling and analytical (Cheng, 2012; Wang et al., 2012; Cohen et al., 2010; Coker, 2010; Carranza, 2009; Zhao, 2007; Cameron et al., 2004). A number of studies have been documented about the progress of elements migration and enrichment mechanism from deeply buried mineral deposits, such as electrochemical dispersion (Smee, 1983), cyclical dilatancy pumping (Cameron et al., 2004), etc. Till data, much more emphasis has been put on new mathematical models for recognition and decomposition of geochemical anomalies associated with buried mineralization (Zuo et al., 2016; Grunsky, 2010; Cheng et al., 1994). A variety of fractal/multifractal methods are constantly proposed, including concentration-area (Cheng et al., 1994), spectrum-area (Zhao et al., 2016), mapping singularity technique (Zuo et al., 2016, 2013; Cheng, 2007), and MSVD (Cheng, 2007). In recent years, machine learning has also been increasingly applied to extract meaningful elemental associations related to the buried mineralization (Zuo, 2017; Zhao et al., 2016; Carranza and Laborte, 2015; Zuo and Carranza, 2011).

    New sampling and analytical methods to enhance anomalies have attracted significant attention over the last decade (Anand and Robertson, 2012). It is widely accepted that hydrothermal fluids are extremely active in the fracture zone, usually associated with strong fluid-rock interaction (Duan et al., 2016; Micklethwaite and Cox, 2004; Sibson and Scott, 1998; Goddard and Evans, 1995). Taken fault rocks as the sampling media, the tectono-geochemistry method has widely applied and promoted the progress of tectono-geochemistry as a discipline by Chinese geologists (Han, 2013; Chen and Huang, 1984; Tu, 1984). Previous studies show that geochemical anomalies can be highlighted in samples from faults and fractures (Zhu et al., 2003; Crone et al., 1984). Such anomalies, also called primary halos along fracture zone, are widely utilized to successfully explore for concealed orebodies (Han, 2013; Han et al., 2009; Sun et al., 1987). However, these anomalies are influenced by various factors which lead to complexity of these anomalies; those factors contain not only scale of orebodies and the depth of buried, but also the superposition of anomaly component from orebodies with different properties (Chen et al., 2015). Therefore, to identify and extract the element association anomalies associated with buried mineralization plays an essential role in deep mineral exploration.

    In this study, we present a case study that identification of the ore-forming element anomaly component using MSVD combined with PCA from element association anomalies, and apply the element anomaly components to delineate ore-finding targets in the Laochang tin-polymetallic ore field, Gejiu, SW China.

  • PCA is the most frequently used method of multivariable analysis that is used to reduce high-dimensional datasets. By orthogonal transformation, a set of interrelated variables can be transformed to a new set of uncorrelated variables called principal components (PCs) in terms of their correlation or covariance matrix. Each PC only represents part of the information and the first few of these PCs are expected to account for most of the variation present in the original data set. Therefore, PCA can effectively increase information interpretability (Cheng et al., 2011). In geochemical analysis, elemental assemblage characterized by different PCs could indicate diverse geological processes, which may be used as a geochemical indicator for the products of the geo-processes (e.g., ore deposit). We considered a data matrix

    onto a new matrix y using ul

    where xi represents the n-th element of the m-th sample and yi represents the score for the i-th sample. The eigenvector ul represents the loadings of the principal component. By eigen decomposition, ul can be obtained in terms of the eigenvector u and eigenvalue λ

    where R represents a correlation matrix of original matrix x. More detailed introductions of the PCA calculation method can be seen in Grunsky (2010), Ueki and Iwamori (2017).

  • Integrate fractal and singular value decomposition theories, MSVD method has been used in multi-component identification and signal/noise separation (Chen et al., 2015). The singular value obtained by SVD is similar to the projection coefficient which quantifies the contribution of SV correlation subspace to the total energy (Strang, 1986). SVD is a factorization of a rectangular matrix X into orthogonal matrices

    U and V are orthogonal normalized matrices and the columns of U and V are called left and right singular vectors, respectively. "T" stands for transpose. S is a diagonal matrix containing the singular values, which are positive and sorted in the decreasing order. S are equal to positive square roots of the eigenvalues of the covariance matrices XXT and XTX. r presents the rank, σ is the i-th SV and weight with σ1σ2≥⋯≥σ3≥0. X can be also written as follows

    where ui and vi are left and right eigenvectors, respectively. The σi and λi can be written as follows

    Freire and Ulrych (1988) introduced band-pass, low-pass, and high-pass images according to the range of singular values that has been used. Band-pass image (XBP) is computed rejecting highly correlated and uncorrelated parts of the traces, i.e.,

    whereas low-pass eigen images are from i=1 to p and high pass eigen images are from i=q to r (Cagnoli and Ulrych, 2001). The determination of p and q depends on the singular value. Li and Cheng (2004) and Cheng (2007) proposed a power-law model between the sum of energy (E) and eigenvalues (λ) to determine the p and q values, which can be written as follows

    The log-log plot of λ-E can be divided into several parts and the break points can be regard as p and q. The reconstruction of some specific eigenvalues whose singular values in the same segment may correspond to certain geological processes (Chen and Zhao, 2012; Wang et al., 2012). More detailed description of the MSVD method and its application can be seen in Li and Cheng (2004), Zhao and Chen (2011), and Chen et al. (2015).

  • As the world's largest primary tin district, the Gejiu supergiant deposit contains a measured resource of more than 300 Mt at 1% Sn, 300 Mt at 2% Cu, 400 Mt at 2% Pb+Zn, and more than 1 000 Mt at 20% Mn (Cheng et al., 2013; Zhuang et al., 1996; Geological Survey Team 308, 1984). The district is adjacent to the convergence zone of Yangzi para-platform, South China and Lanping-Simao fold systems (Fig. 1). The major strata are the Triassic Gejiu and Falang formations. The former is dominated by thick-bedded limestone with minor interbedded dolomitic limestone. The latter is composed of shale with minor limestone. The N-S-striking Gejiu fault divides the whole Gejiu district into a western and an eastern part (Fig. 1). This study focuses on the eastern part〞the main ore concentration area.

    Figure 1.  Geographic location and geological sketch map of Gejiu polymetallic tin district, Yunnan Province, SW China (modified after Liao et al., 2014; Mao et al., 2008).

    Laochang is situated in the central of the eastern part. It is the most important mineral deposit with c. 50% of the Sn resources of the district. Laochang deposit is hosted by Gejiu Formation, which can be subdivided into three members: (1) the Kafang Member composed of limestone and intercalated dolomite; (2) the Malage Member composed of dolomite and minor dolomitic limestone; and (3) the Bainidong Member composed of limestone and minor dolomite. Granitic rocks are widely distributed magmatic rocks in this area. Concealed equigranular granite intrusions intruded into the Middle Triassic strata at approximately 200-1 800 m beneath the surface. The zircon U-Pb dating yields the ages of 85.0±0.85 Ma, indicating that the granite formed during the Late Yanshanian (Cheng et al., 2010). These granite intrusions have geochemical signatures of typical tin-bearing granites and are considered to be genetically related to Sn-Cu polymetallic mineralization (Mao et al., 2008).

    Since the formation of thick carbonate strata in the Middle Triassic, the Gejiu district has recorded a prolonged history of tectono-magmatic activity and complex deformation, promoting the development of fold and fault structures in the area. The Wuzishan anticlinorium with the axial of NE direction is the dominated fold in the eastern part. Faults and fractures are extremely well-developed in study area and dominantly strike NW-SE, NE-SW and E-W sets. These faults played a crucial role in providing both pathways for migration of fluids and sites for ore deposition (Zhao et al., 2011; Jiang et al., 1997; Zhuang et al., 1996).

  • As illustrated in the geological profile (Fig. 2), several main mineralization styles are recognized in terms of the genesis and occurrences of ores as follows: (1) placer Sn deposits resulting from supergene weathering of primary deposits; (2) contact skarn Cu-Sn deposit distributed in the contact zones between granite and marble or dolostone; (3) stratabound Cu ores hosted by basalt; (4) stratiform Cu-Sn ores hosted by carbonate; and (5) vein-type ores comprising tourmaline-quartz veins, phlogopite-diopside veins, and tourmaline-cassiterite veins (Zhuang et al., 1996; Geological Survey Team 308, 1984)). In Laochang area, the dominant ore types are skarn and vein-type ores (Fig. 3). The main metal minerals consist of pyrrhotite, sphalerite, chalcopyrite, stannite, pyrite, bismuthinite, and arsenopyrite. Quartz, calcite, and clay minerals are the dominant gangue minerals (Fig. 4). The tin-polymetallic ores are considered as the granite-related hydrothermal origin and genetically related to the nearby granitic intrusions.

    Figure 2.  Geological profile of Gejiu tin polymetallic ore deposit (modified from Cheng et al., 2013; Chen et al., 2015).

    Figure 3.  Mineralization characteristics of borehole samples with different type of ore in the Laochang orefield: (a)-(c) disseminated skarn type; (d)-(f) quartz vein type.

    Figure 4.  Photomicrograph of ore samples from boreholes in Laochang. Po. Pyrrhotite, Sph. sphalerite, Cpy. chalcopyrite, Snt. stannite, Py. pyrite, Ars. arsenopyrite.

  • Through a fault tectono-geochemical survey in 2018, a total of 592 surficial samples of fault rocks comprising breccia, cataclasite, and calcite vein fill, were collected from faults/fractures within an area of approximately 10 km2 in Laochang area. The main rock types that constituted the fracture zone were limestone, dolomite and cataclastic fault rocks. The fault rocks in this fracture zone of Laochang district can be classified into three types of fault rocks: fault gouge, fault breccias, and cataclasite. As a dominant type of fault rocks, breccia rocks mainly compose of limestone and dolomite breccia, calcium-iron or magnesium-iron cementation (Fig. 5). The breccia is irregular in shape and different in size, which is generally more than 2 mm, and the larger ones can reach tens of centimeters. The cement is mainly calcareous dolomite and limestone powder. Calcite vein is often inserted in breccia, which is the product of hydrothermal activity. The fault rock is usually distributed in a linear pattern, consistent with the strike of the fractures. Most of these samples were plot from mapped main faults while some samples collected from small faults and fractures plot away from mapped faults in Fig. 6. Through field geological survey, it also displays some mineralization points which are mainly characterized by hematite or goethite mineralization.

    Figure 5.  Photos and photomicrographs of fault rocks showing: (a) and (b) in the hand specimen, while (c) and (d) under a microscope.

    Figure 6.  Simplified geological map of the eastern Laochang district and sample location of fault rock.

    Twelve elements were measured by (1) AFS for Hg, As, Sb, and Bi; (2) AAS for Ag; and (3) ICP-MS for Mn, Cu, Zn, Mo, Cd, W, and Pb at the Mineral Resources Testing Center of Zhengzhou. The sample preparation, sample analysis, and quality control are based on the Chinese Geochemical Survey Specifications of DZ/T 0011-91 and ZD0130.6-94.

  • A statistical analysis of 592 surficial samples in fracture zones shows that the raw element data set are characterized by positive values of skewness (>8), kurtosis (>80), and coefficient of variation (>3)(Table 1). As a measure of symmetry, the high skewness values were due to the extremely values in the data set, indicating non-normal data distributions. Kurtosis is a measure of whether the data are tailed relative to a normal distribution, and high kurtosis of this geochemical data tend to have heavy tails or outliers. Coefficient of variation (CV) is an important parameter to evaluate the degree of element differentiation. The element concentrations in fracture zones are characterized by high CV values, indicating inhomogeneous distribution and the possibility of local enrichment of elements, thus they are more likely to be concentrated proximal to ore according to the sample with high abnormal ore-forming elements. Also listed are the element concentrations of Gejiu Formation derived from 12 carbonate wall rocks and the concentration factor (CF). The latter represents the ratio of the mean value to the background values of Gejiu Formation, which can be used to indicate metallic element concentrations (Zhao et al., 2015). The CF values illustrate that rocks in fracture zones are characterized by much larger concentrations of ore-forming elements compared to the wall rocks. Combining the multiple filled veins (e.g., calcite and quartz veins) and alteration minerals in the fault zone, it implies that rocks in fracture zones might occur mass transfer during fluid-rock interactions and contain much ore-forming solutions.

    Ele. Mean Lower quartile Upper Lower quartile Std. deviation Skewness Kurtosis CV Carbonate rocks of Gejiu Formation CF
    Ag 0.8 0.1 0.4 2.7 10.1 140.9 3.5 0.2 3.4
    Sn 13.1 1.4 4.1 90.3 19.0 403.0 6.9 2.1 6.4
    Bi 0.7 0.1 0.1 12.9 24.4 595.7 17.5 0.1 8.7
    Hg 78.0 11.0 48.2 300.8 10.2 123.8 3.9 12.4 6.3
    Mn 1 673.9 121.2 1 023.5 7 821.2 12.2 161.6 4.7 108.8 15.4
    Cu 29.4 3.6 10.0 362.5 24.0 581.1 12.3 4.3 6.9
    Zn 361.5 42.6 268.0 1 199.6 12.6 217.4 3.3 17.9 20.2
    Mo 2.4 0.7 0.9 12.8 12.1 162.5 5.3 1.0 2.3
    Cd 9.3 0.0 1.9 95.4 21.2 484.3 10.3 0.3 33.0
    W 2.5 1.0 1.7 8.2 10.5 122.5 3.3 0.4 6.2
    Pb 467.6 31.7 192.6 2 573.4 12.5 173.9 5.5 7.1 66.0
    As 180.8 3.8 14.5 3 815.6 24.4 592.3 21.1 1.6 115.1
    CF. Concentration factor, which is the ratio of the mean value to the average concentration of Gejiu Formation; CV. coefficient of variation, which is defined as the ratio of the standard deviation to the mean.

    Table 1.  Descriptive statistics of raw fault tectono-geochemical data in Laochang (units10-6, except Hg)

    Histograms and Q-Q plots indicate that the most of variables are symmetrically distributed (Fig. 7). These distributions may be interpreted as mixed origins of the elements due to influence by secondary geological processes such as surface leaching or weathering in fracture zones (Zhao et al., 2015). The concentrations and distributions of metal elements may demonstrate the multiphase hydrothermal fluid activities in the fracture zones, which is in agreement with the occurrences of mineralization and alteration minerals.

    Figure 7.  Histograms and Q-Q plots of log-transformed assay data.

    Another statistical analysis displays the distribution characteristics of Sn and Cu in different fracture zones. The faults were divided into four groups, which are NE-, E-W-, NW-, and N-S-trending faults and their corresponding intervals of strike are defined as 15°-75°, 75°-105°, 105°-165°, and 165°-195°. The result of box-plot reveals that the main ore controlling faults may be NE and EW directions (Fig. 8). The areas surrounding the fault intersections also enrich Sn and Cu, indicating that intersection part could be appropriate for the penetration of ore-forming fluids to form mineralization because of the higher permeability.

    Figure 8.  Box-plot of Sn and Cu concentration in different fracture zones.

  • In this research, principal components analysis (PCA) is utilized to transform the original fault tectono-geochemical data set of 592 surficial samples into a smaller set of linear combinations that represent most of the variance. To open the geochemical data and address the closure problem prior to PCA (Zuo et al., 2013; Carranza, 2011; Aitchison, 1986), the clr-transformation is applied to process the raw data by the following formula (Zuo et al., 2013; Carranza, 2011; Aitchison, 1986),

    Four principal components with eigenvalues of 1.0 and above are: PC1-PC4 and these combined, accounted for 72.6% variability in the clr-transformed data (Fig. 9a). Figure 9b shows the bar graph of the loadings for the first principal component accounting for 37.2% variance. Sn, Cu, W, Mo, and Bi are in association of PC1, representing granite-related hydrothermal elements associated with Sn-Cu ploymetallic mineralization in Laochang district (Zhao et al., 2015; Zhuang et al., 1996; Geological Survey Team 308, 1984)). Group PC2, dominated by Pb and Zn, could be interpreted to represent a group of epithermal elements associated with Pb-Zn mineralization. Group PC3, dominated by Mn and Cd, likely presents element association which associate with carbonate sources. Group PC4, dominated by Ag, Hg, and As, likely presents low temperature epithermal element association which reflect granite-related hydrothermal processes.

    Figure 9.  The results of PCA showing: (a) eigenvalues of principal components; (b) component loading of PC1.

    For generating reliable primary anomaly that can be used for further prospecting of the deep mineralization, inverse distance weighting was used as an interpolation approach for mapping Sn, Cu, W, Mo, and Bi. In order to obtain different levels of geochemical anomalies, the C-A plots consisting of the concentrations(c) versus the number of cells were obtained with the concentrations greater than or equal to c (Zhao et al., 2017). According to Fig. 10, there were more than two enrichment steps and the threshold values for each element were obtained. Then the anomaly maps with different levels for Sn, Cu, W, Mo, Bi were generated in Fig. 11, which also showed the score map of PC1. The Most of the high anomalies located in the area along the Mengzimiao fault and some secondary faults, presenting NW, NE, and EW direction.

    Figure 10.  The results of C-A double logarithmic scatter for Cu, Sn, W, Mo, and Bi.

    Figure 11.  Anomaly maps of Cu, Sn, W, Mo, and Bi inferring from C-A models, also showing the score map of PC1.

  • Mineralizations can be regarded as products of a singular process characterized by power law models (Cheng, 2007). For identify the geochemical anomalies associated with the granite-related Sn-Cu ploymetallic mineralization in the study area, further analysis was required to recognize more well-defined features that are directly related to mineralization. In our research, based on PCA results, the MSVD method was used to process the score map of PC1 presenting the main ore-forming element combination for Sn-Cu mineralization. Equation 10 represents a power law plot of λ-E modeled by line segments having different slopes to determine p and q. The curve can be divided into three parts based on their different slopes, two break points, p=4, q=12 (Fig. 12). The left segment is made of ranking from λ13 to λ78, and the percentage of its energy only takes up about 0.008 6% of total energy, which might be present data errors without any geological significance (Chen et al., 2015). The percentage of the energy of the right segment from λ1 to λ4 is about 94.96% of total energy. The corresponding reconstructed map can be regarded as low-pass filtered image, which usually indicate the regional ore forming background (Fig. 13a). The reconstructed image from the λ1 to λ4 probably demonstrates the buried deeply geological bodies. Taking -0.2 as threshold value, the positive anomaly area is mainly distributed in the east and south of the study area, while the negative anomaly is mainly distributed in the west part. The junction of positive and negative anomalies possibly indicates the boundary of deep geological bodies, such as the granite and carbonate formation.

    Figure 12.  Log-log plot of λ and E for score map of PC1.

    Figure 13.  Reconstructed geochemical component images from (a) 1st to 4th eigenvalues and (b) 5th to 12th eigenvalues in the Laochang orefield.

    The middle segment consists of ranking from λ5 to λ12, and the percentage of its energy is about 0.042% of total energy. The reconstructed map with the sum from 5th to 12th eigenvalues can be regarded as high-pass filtered image (Chen et al., 2015), which usually reflect the ore-forming elements of locally enrichment (Fig. 13b). The results show that most of the anomaly zones are distributed in NE zonal direction and are spatially coincident with the mineralization points. Consequently, the anomaly zones of the reconstructed map may be as the prospecting potential area, which have been confirmed by drilling and tunnel engineering. As shown in Fig. 12b, two ore-discovery drills are located in the anomaly area of the south of the Mengzimiao fault. Some ores are found in the core at depths of 800-1 000 m, frequently spatially localized along the fracture zone or contact zone of granite and carbonate rocks. Mineralization associated with different rock types, mostly as vein and fracture infillings and as disseminated forms. The average contents of Cu and Sn in those ores reach about 2.69% and 0.50% respectively. Therefore, these anomalies may provide prospecting guidance for the discovery of concealed orebodies.

  • The fault tectono-geochemistry method focusing on fault rocks as sampling media, have widely applied to deep ore prospecting and prediction practice in China. In this paper, the hybrid methodology combining MSVD and PCA were applied to characterize geochemical signatures and identify ore-forming anomaly component from element concentrations in fracture zones of the Laochang district. The following conclusions can be drawn.

    (1) The surficial rock in fracture zones can be applied to provide vectors to find concealed mineralization by producing significant geochemical anomalies. Principal ore-forming elements presents larger concentrations in fault rocks compared to those of the wall rocks, indicating these elements are mobile and mainly derived from fluid/rock interaction.

    (2) PCA based on clr-transformed data can be used to recognize significant geochemical signatures related to mineralization; the first principal component dominated by Sn, Cu, W, Mo, and Bi represents the granite-related Sn-Cu ploymetallic mineralization.

    (3) MSVD could effectively define local anomaly features in more detail by decomposing geochemical field. The primary anomalies in reconstructed map with the sum from 5th to 12th eigenvalues by MSVD may indicate the presence of local buried mineralization. The target areas of high anomaly associated with NE-oriented faults are consistent with the distribution of known deep ores, may represent undiscovered ores and, thus, warrant further exploration.

  • We would like to particularly thank Prof. Yongqing Chen for fruitful comments. We are also grateful to anonymous reviewers for their constructive comments that helped to significantly improve an earlier version of our manuscript. This research was jointly supported by the National Key R & D Program of China (Nos. 2016YFC0600509, 2017YFC0601504). The final publication is available at Springer via https://doi.org/10.1007/s12583-021-1423-1.

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