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Volume 32 Issue 2
Apr.  2021
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Renguang Zuo. Mineral Exploration Using Subtle or Negative Geochemical Anomalies. Journal of Earth Science, 2021, 32(2): 439-454. doi: 10.1007/s12583-020-1079-2
Citation: Renguang Zuo. Mineral Exploration Using Subtle or Negative Geochemical Anomalies. Journal of Earth Science, 2021, 32(2): 439-454. doi: 10.1007/s12583-020-1079-2

Mineral Exploration Using Subtle or Negative Geochemical Anomalies

doi: 10.1007/s12583-020-1079-2
More Information
  • Mineral resources prediction and assessment is one of the most important tasks in geosciences. Geochemical anomalies, as direct indicators of the presence of mineralization, have played a significant role in the search of mineral deposits in the past several decades. In the near future, it may be possible to recognize subtle geochemical anomalies through the use of processing of geochemical exploration data using advanced approaches such as the spectrum-area multifractal model. In addition, negative geochemical anomalies can be used to locate mineralization. However, compared to positive geochemical anomalies, there has been limited research on negative geochemical anomalies in geochemical prospecting. In this study, two case studies are presented to demonstrate the identification of subtle geochemical anomalies and the significance of negative geochemical anomalies. Meanwhile, the opportunities and challenges in evaluating subtle geochemical anomalies associated with mineralization, and benefits of mapping of negative anomalies are discussed.
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Mineral Exploration Using Subtle or Negative Geochemical Anomalies

doi: 10.1007/s12583-020-1079-2

Abstract: Mineral resources prediction and assessment is one of the most important tasks in geosciences. Geochemical anomalies, as direct indicators of the presence of mineralization, have played a significant role in the search of mineral deposits in the past several decades. In the near future, it may be possible to recognize subtle geochemical anomalies through the use of processing of geochemical exploration data using advanced approaches such as the spectrum-area multifractal model. In addition, negative geochemical anomalies can be used to locate mineralization. However, compared to positive geochemical anomalies, there has been limited research on negative geochemical anomalies in geochemical prospecting. In this study, two case studies are presented to demonstrate the identification of subtle geochemical anomalies and the significance of negative geochemical anomalies. Meanwhile, the opportunities and challenges in evaluating subtle geochemical anomalies associated with mineralization, and benefits of mapping of negative anomalies are discussed.

Renguang Zuo. Mineral Exploration Using Subtle or Negative Geochemical Anomalies. Journal of Earth Science, 2021, 32(2): 439-454. doi: 10.1007/s12583-020-1079-2
Citation: Renguang Zuo. Mineral Exploration Using Subtle or Negative Geochemical Anomalies. Journal of Earth Science, 2021, 32(2): 439-454. doi: 10.1007/s12583-020-1079-2
  • Mineral exploration has always been at the frontier of geoscience research and has advanced from empirical prospecting to scientific exploration and, more recently, information exploration (Xie, 1999). Empirical prospecting is based on the visual observations and experiences of geoscientists in the field. Scientific exploration integrates mineral deposit models and various geoscientific data to study geological conditions and environments for the formation of mineralization. Information exploration combines both direct and indirect information of mineralization using various techniques. The current mineral exploration is heavily based on information, especially subtle information originating directly from concealed mineralization (Xie, 1999).

    Geochemical anomalies, as direct indicators of the presence of mineralization, have played a significant role in the search of mineral deposits over the past several decades. Geochemical prospecting focuses on the formation, location, and characteristics of geochemical anomalies (Hawkes, 1957), and has played a critical role in mineral deposit discovery. Meanwhile, there have been refinements in the methods of digestion and selective leaches, resulting in improvements in detection limits (Grunsky, 2010). Due to increased investment in geochemical surveys, geochemical datasets for various earth materials or media (e.g., rocks, stream sediments, soils, water, gas, and plants) at regional, district, to deposit scales have been established for locating mineral deposits, or studying or monitoring environmental pollution (e.g., de Caritat and Reimann, 2012; Reimann et al., 2012; Smith et al., 2012; De Vos et al., 2006; Xie et al., 1997). From such datasets, a number of mineral deposits have been discovered (Xi, 2007). For instance, 2 570 ore deposits have been identified in China through geochemical surveying, including 700 medium-large gold ore deposits with a gold reserve of more than 4 000 tons (China Geological Survey, 2016).

    A geochemical anomaly is usually described in terms of three aspects: concentration (high/low), size (large/small), and element assemblage (single element/multi-elements). Most geochemical anomalies are characterized by high element concentrations, large size, and marked coincidence of element assemblages (high-large-coincidence anomalies) and, on this basis, a large number of mineral deposits have been identified in China with a high confidence level (Zheng et al., 2014). A subtle geochemical anomaly which is an opposite term of high-large-coincidence anomaly is characterized by low concentration, and/or small size, and/or incomplete element assemblage (Fig. 1). Due to the masking effects of various covers, deeply buried mineralization usually exhibits subtle geochemical anomalies at the surface (Cheng, 2012). However, finding an mineral deposit is becoming a challenge through processing of geochemical data via traditional methods which do not consider the local spatial structure of geochemical patterns, and therefore, the obtained geochemical anomalies tend to be suppressed or smoothed out (de Mulder et al., 2016). Therefore, there is a need for more advanced approaches to process geochemical exploration data.

    Figure 1.  Cartoon diagram of subtle geochemical anomaly. (a) Low grade/small size of anomalies; and (b) compared with the right anomalies, the left weak anomalies showing lack of Cu and Mo anomalies.

    Studying negative geochemical anomalies is significant in understanding the spatial distribution of elements and mineralization (Shi and Wang, 1995). Areas with metal/element values above a certain threshold represent positive geochemical anomalies. Negative geochemical anomalies refer to areas with element contents distinctly lower than the average background concentrations (i.e., strong depletions in metal/element concentrations) (Xie, 1981) (Fig. 2). However, compared to positive geochemical anomalies, negative geochemical anomalies have traditionally not been given much attention in geochemical prospecting (Zuo and Xiong, 2018; Shi and Wang, 1995; Govett, 1983; Rose et al., 1979).

    Figure 2.  Cartoon diagram of negative geochemical anomaly.

    In this study, two stream sediment datasets were processed for identifying subtle geochemical anomalies and developing a robust index which considers both positive and negative geochemical anomalies for mapping of geochemical anomalies associated with mineralization. The opportunities and challenges for evaluating subtle geochemical anomalies associated with mineralization, and the benefits of mapping of negative anomalies were also addressed.

  • In this study, two magmatic-hydrothermal mineralized districts were chosen to study subtle and negative geochemical anomalies since a number of elements are involved in fluid-rock interaction, and some elements are enriched and others are depleted. Two stream sediment datasets used in this study were compiled from the national geochemical mapping project initiated in China in 1979 (Xie et al., 1997). The original data, which contain concentrations of 39 major and trace elements, were collected at a density of approximately 1 sample per km2. More detailed information on sampling, detection limits, and quality control for these datasets can be found in Xie et al. (1997). The original geochemical point data were composite samples consisting of mixed 4 sub-samples picked up at a density of 1 sub-sample per km2. The first dataset was compiled from the Gejiu region, Yunnan Province of China (Fig. 3). This region is an important mining district due to the occurrence of the large-scale skarn-type Sn polymetallic deposits. The existing studies revealed a close spatial relationship between geological factors such as Gejiu Formation, Geijiu Batholith, and NNE/EW trending faults, and locations of Sn polymetallic mineralization (Cheng et al., 2013; Cheng, 2012, 2007; Cheng and Mao, 2010; Mao et al., 2008). There are different mineralization sizes and intensities in the eastern and western parts of the Gejiu district, resulting in high concentration of Sn in the eastern part of Gejiu region, and low concentration of Sn in the western part of Gejiu region. Therefore, this region is an ideal study area for identifying subtle geochemical anomalies. The ore-forming elements, Sn and Cu, were evaluated in this study. The concentrations of Sn and Cu were determined by emission spectrometry and X-ray fluorescence, and the detection limits for both Sn and Cu were 1 ppm (Xie et al., 1997).

    Figure 3.  Simplified geological map and locations of skarn-type Fe polymetallic mineralization in the Gejiu region, Yunnan Province, China (modified from Zuo and Xiong, 2020).

    The second dataset was collected from the southwest part of Fujian Province in China. A number of skarn-type Fe polymetallic mineral deposits, such as the Makeng, Luoyang, and Pantian deposits, were located in this area (Han and Ge, 1983; Ge et al., 1981) (Fig. 4). These Fe polymetallic mineral deposits are spatially associated with Yanshanian intrusions, Carboniferous-Permian carbonate formations, and NE-NNE-trending faults (Xiong and Zuo, 2018; Xiong et al., 2018; Zuo, 2018; Zhang et al., 2016; Zuo et al., 2015; Zhang and Zuo, 2014). The positive geochemical anomalies associated with Fe polymetallic mineral deposits such as Fe2O3, Cu and Pb have been extensively studied (e.g., Wang J and Zuo, 2020; Xiong and Zuo, 2016; Wang H C and Zuo, 2015; Wang et al., 2015a, b). Few studies focus on negative geochemical anomalies around mineralization in this area. In this study, data for Na2O, Ba, and Mn were selected from the dataset because the first two elements are typically depleted in magmatic-hydrothermal mineralization (e.g., Liu et al., 2016), and the ratio of Mn/Ba is a useful index for geochemical prospecting (e.g., Li, 1993). The concentrations of Na2O, Ba, and Mn were determined by inductively coupled plasma-atomic emission spectrometry, and the detection limits were 0.05% for Na2O, 50 ppm for Ba, and 30 ppm for Mn.

    Figure 4.  Simplified geological map and locations of skarn-type Fe polymetallic mineralization in Southwest Fujian Province, China (modified from Zuo et al., 2015).

  • Various methods have been developed to identify geochemical anomalies. They can be categorized into frequency- based and frequency-spatial based methods (Zuo et al., 2016). These methods include classical statistical analysis (Hawkes and Webb, 1962), exploratory data analysis (Tukey, 1977), univariate/ multivariate analysis (Grunsky, 2010), geostatistics (Matheron, 1962), fractal/multifractal models (Cheng, 2007; Cheng et al., 2000, 1994), and machine learning (including deep learning) algorithms (e.g., Zuo and Xiong, 2020; Zuo et al., 2019; Xiong and Zuo, 2018, 2016; Chen et al., 2014). Some of these methods also can be applied to identify subtle geochemical anomalies and recognize negative geochemical anomalies. For instance, Shi and Wang (1995) applied the contrast values in conjunction with the moving averaging method (Xie et al., 1990) to delineate negative and positive geochemical anomalies; Cheng (2012) used the local singularity analysis (Cheng, 2007) to detect subtle geochemical anomalies in the covered areas in China.

    Different subareas vary in terms of background geochemical values and thresholds due to variations in geological units and geochemical characteristics. Traditional methods of background-anomaly separation, such as the mean plus k× standard deviation (k is a constant and ranges from 1 to 3), assume a constant threshold and, thus, do not distinguish subtle geochemical anomalies (Zuo and Wang, 2016). In contrast, multifractal models like the spectrum-area (S-A) multifractal analysis proposed by Cheng et al.(2000, 1999) can distinguish background and anomaly components based on the distinct characteristics in the frequency domain.

    The S-A analysis is based on the concentration-area fractal model (Cheng et al., 1994). The spatial patterns were first converted from the spatial domain into the frequency domain using Fourier transformation. A log-log plot of spectrum density (S) versus areas (A) with spectrum density greater than S can be then constructed. N straight lines (N≥2) can be fitted based on the S-A plot by means of the least square method. N straight lines define N+1 filters, in order to divide the original map into several components. Maps of the geochemical anomaly and background components can be obtained by converting each of these components from the frequency domain into the spatial domain with the use of inverse Fourier transformation. The principles of the S-A multifractal method are described in Cheng et al.(2000, 1999). ArcFractal (Zuo and Wang, 2020), an ArcGIS add-in, was used for S-A analysis.

    In addition, the area under the receiver operating characteristic curve (AUC) (Fawcett, 2006) was employed to evaluate the spatial correlation between the studied geochemical pattern and the locations of known mineral deposits. The AUC value ranges from 0 to 1. An AUC>0.5 suggests a positive spatial correlation, an AUC < 0.5 indicates a negative spatial correlation. The larger the AUC, the stronger the spatial correlation is.

  • The isometric log ratio (ilr) transformation (Egozcue et al., 2003) was used to process the data to reduce the effects of closure data problem. The inverse distance weighting method supported by ArcGIS 10.2 was then employed to interpolate the original point data into raster maps with a cell size of 1 km℅1 km. The spatial distributions of Sn (Fig. 5a) and Cu (Fig. 5b) show that the eastern part of the Gejiu region exhibits high Sn and Cu concentrations and a number of Sn polymetallic deposits. In contrast, the western part exhibits relatively low Sn and Cu concentrations and few Sn polymetallic deposits. Compared to the eastern part, the western part exhibited subtle concentrations of Sn and Cu. As such, traditional methods, such as frequency-based approaches, can detect geochemical anomalies in the east but cannot distinguish subtle geochemical anomalies in the west (Zuo et al., 2016).

    Figure 5.  Maps showing spatial distribution of (a) (Sn) and (b) (Cu).

    The S-A model was applied to decompose the maps of Sn and Cu into the background and anomalous components and to identify geochemical anomalies associated with Sn polymetallic mineralization. The background maps of Sn (Fig. 6a) and Cu (Fig. 6b) exhibit a similar pattern where most of high values of Sn and Cu appear in the east of Gejiu region. Sn and Cu vary in terms of geological units, and different subareas are characterized by different background values. Figure 6 shows that the eastern part of Gejiu region has a higher background value of Sn and Cu than the western part. Meanwhile, the Sn anomaly map (Fig. 7a) is similar to the Cu anomaly map (Fig. 7b). Strong geochemical anomalies occur in the eastern part of the Gejiu region. Subtle geochemical anomalies in the western part of the study area were identified. For example, in the southwestern corner of this study area, a subtle geochemical anomaly around a known Sn polymetallic deposit was detected (Fig. 7a). However, such a subtle geochemical anomaly could not be easily recognized via traditional methods.

    Figure 6.  Maps showing spatial distribution of geochemical background for (a) Sn and (b) Cu.

    Figure 7.  Maps showing spatial distribution of geochemical anomalies for (a) Sn and (b) Cu.

  • The spatial distributions of ilr-transformed Na2O (Fig. 8a) and Ba (Fig. 8b) show that most of the known Fe polymetallic mineralization occur in areas of low concentrations of Na2O and Ba. The AUC of Na2O (0.263) and Ba (0.224) are lower than 0.5, indicating that spatial patterns of Na2O and Ba are negatively correlated with the locations of Fe polymetallic mineralization. In contrast to Na2O and Ba, high values of Mn are associated with Fe polymetallic mineralization (Fig. 8c) since the AUC value of Mn is 0.853, which indicates a strong positive association of Mn with the spatial distribution of Fe polymetallic mineralization.

    Figure 8.  Maps showing spatial distributions of (a) Na2O, (b) Ba, (c) Mn and (d) RGB integrated of Na2O, Ba, and Mn.

    The RGB color combination model was used here to represent three geochemical patterns. The RGB space is expressed by a cube characterized by three components (red, green and blue). Each of these components ranges from 0 to 255. In a RGB map, the pure red, green and blue are (255, 0, 0), (0, 255, 0) and (0, 0, 255), respectively, and they denote three of the vertices of the RGB cube. The cyan, yellow and magenta are (0, 255, 255), (255, 255, 0) and (255, 0, 255), respectively, and they represent the vertices of the cube where one of the primary components assumes the value 0, and the values of other two components are 255. The white is (255, 255, 255), and it represents high values of red, green, and blue. A color composite image which integrates Na2O, Ba, and Mn was created (Fig. 8d). It represents an alternative presentation of three geochemical patterns together (Zuzolo et al., 2018). Maps of Na2O (Fig. 8a) and Ba (Fig. 8b) were reclassified into [0, 255] from high to low concertation values, and the geochemical map of Mn (Fig. 8c) was reclassified into [0, 255] from low to high concertation values using the equal interval classification method with the aid of ArcGIS. The reclassified maps of Na2O, Ba and Mn were regarded as red, green, and blue band, respectively, and these three bands were merged as a RGB map (Fig. 8d). Most known Fe polymetallic mineralization occur around either the white color areas (low Na2O and Ba, and high Mn), or cyan areas (low Ba and high Mn), yellow (low Na2O and Ba) and magenta (low Na2O high Mn) areas. This model considers three geochemical patterns which are positively and negatively correlatively with known mineralization and therefore could provide more information than a single geochemical pattern.

    The background and anomaly maps of Na2O and Ba obtained by the S-A analysis are illustrated in Figs. 9 and 10. It can be observed that the background and anomaly maps of Na2O (Fig. 9) and Ba (Fig. 10) show a close spatial relationship with Fe polymetallic mineralization and most of known Fe polymetallic mineralization appear in the areas linked to low background and anomaly values of Na2O and Ba. A new index of the ratio of Mn/Ba was constructed for mapping geochemical anomalies. The AUC of the ratio of Mn/Ba reaches 0.861, indicating a strong spatial association with known Fe mineralization. Meanwhile, the AUC value of the ratio of Mn/Ba is larger than that of Mn (0.853), and the index of the ratio of Mn/Ba (Fig. 11a) can highlight zones of hydrothermal fluid-rock interactions. Similar to Fig. 8d, two RGB maps which integrate anomalous and background maps of Na2O, Ba, and Mn are created. The white, cyan, yellow and magenta areas of Fig. 11b have a close spatial association with locations of known mineralization, indicating that such composite map which integrates positive and negative geochemical anomalies can highlight the mineralized zones and therefore can provide value information for mineral exploration. The composite image of Fig. 11c shows the geochemical background of Na2O, Ba, and Mn, which exhibits a NNE trend which is similar to the trend of faults, meaning that regional faults maybe control the spatial distribution of geochemical patterns of these elements. Meanwhile, known Fe polymetallic mineralization develops in or around areas linked to low Na2O and Ba, but high Mn.

    Figure 9.  Maps showing (a) geochemical background and (b) anomalies of Na2O.

    Figure 10.  Maps showing (a) geochemical background and (b) anomalies of Ba.

    Figure 11.  Map showing geochemical pattern of (a) Mn/Ba, (b) RGB integrated of anomalous maps of Na2O, Ba, and Mn, and (c) RGB integrated of background maps of Na2O, Ba, and Mn.

    Maps of the background (Fig. 12a) and anomaly (Fig. 12b) components of Mn/Ba were obtained by disaggregating the original map of Mn/Ba using S-A analysis. The spatial relationship of background Mn/Ba with the locations of known Fe polymetallic mineral deposits is unclear. However, positive anomalies of Mn/Ba (Fig. 12b) exhibit a strong spatial association with known Fe polymetallic mineralization. These results indicate that the ratio of Mn/Ba, which is obtained by dividing positive anomalies of Mn with negative anomalies of Ba, is a robust index for highlighting areas of Fe polymetallic mineralization. Figure 12b provides a reference map for guiding further mineral exploration for Fe polymetallic mineralization in the study area. There is a need for further studies on the ratios of elements that form positive and negative anomalies with respect to mineralization to support geological interpretation and mineral exploration.

    Figure 12.  Maps showing (a) geochemical background and (b) anomalies of Mn/Ba.

  • Quantitative assessment and prediction of deeply buried mineral resources in covered regions is significant but challenging, which requires the identification and evaluation of subtle geochemical anomalies. In future, mineral exploration in covered areas will require the identification and evaluation of subtle geochemical anomalies using advanced approaches, such as fractal/multifractal models with the support of geographic information system (GIS) (Cheng, 2007) and machine learning algorithms (Zuo, 2020; Zuo et al., 2019; Zuo and Xiong, 2018). Fortunately, several successful case studies documenting the identification of ore deposits through analysis of subtle geochemical anomalies have been reported. For example, Yu et al. (2010) identified a large-scale Ag polymetallic mineralization belt in Ajile, Inner Mongolia, China, through analysis of subtle geochemical anomalies; Cheng (2012) applied the local singularity analysis to detect subtle geochemical anomalies related to Sn polymetallic mineralization in the study area; Gonçalves and Mateus (2019) delineated subtle geochemical anomalies using the local singularity analysis in an area of Portugal with Cenozoic detrital cover, under which a deep buried deposit is located.

    Meanwhile, there are several challenges in identifying and evaluating subtle geochemical anomalies. The first challenge is the recognition of subtle geochemical signatures associated with mineralization from various subtle geochemical anomalies. The subtle anomalies may comprise weak mineralized and noise information from sampling or data processing. Noise information is usually randomly distributed across a study area, while mineralized information, such as magmatic-hydrothermal mineralization, is usually controlled by geological features, such as faults or intrusions, and therefore can exhibit a specific spatial distribution pattern. Mineralization may occur along faults, which serve as hydrothermal pathways, or around intrusions, which can provide the energy or metal source for mineralization. Intrusions and faults can control the spatial distribution of mineral deposits in a given study area. In addition, subtle geochemical anomalies associated with mineralization are usually linked to overprinting of multiple ore-forming element anomalies. For instance, porphyry Cu deposits are often associated with Cu-Mo-Pb-Zn anomalies (e.g., Zuo, 2011; Zuo et al., 2009). These two characteristics can be used for recognizing mineralization-induced subtle anomalies.

    The second challenge is the differentiation of deeply buried high-grade mineralization from less deeply buried low-grade mineralization. It is difficult to distinguish these two types of subtle geochemical anomalies because they have similar geochemical patterns. Such subtle geochemical anomalies can be distinguished on the basis of geophysical constraints.

    The third challenge is the estimation of the depth of ore deposits using geochemical methods. Geophysics is usually applied to predict and determine the shape and location of buried ore bodies based on geophysical field theory and methods. It is difficult to determine these parameters using geochemical methods. In recent years, some progress has been made on the study of the vertical distribution of geochemical elements. For instance, Cheng (2014) found that, with the decreasing depth in the regolith, some ore-forming element concentrations decreased and obeyed a power-law relation. If the grade of the ore deposit is known and the average concentration of geochemical anomalies is delineated on the surface, the depth of ore deposits can be estimated based on the power-law relation between the concentration and depth.

    In recent years, attention is focused on big data analytics and deep learning algorithms in the field of geochemical prospecting. Big data analytics represents a novel approach to mining geochemical exploration data which considers all the geochemical variables in identifying correlations between geochemical patterns and known mineralization (Zuo and Xiong, 2018). Deep learning algorithms, as a kind of multilayer neural network, can identify the optimal representation of complex geochemical patterns and therefore can identify hidden geochemical patterns associated with mineralization (Xiong and Zuo, 2020, 2016; Zuo et al., 2019). Big data analytics and deep learning can be used to identify new mineral deposits. With the development of data science in geoscience (Zuo and Wang, 2020), numerous innovative methods are anticipated for extracting subtle geochemical anomalies and reducing the effects of noises from the resulting anomalies associated with mineralization to support mineral exploration.

  • Studies of the spatial patterns of positive and negative anomalies around mineral deposits together with studies of ore genesis could improve the interpretation of ore-forming processes (Shi and Wang, 1995). Enrichments, as well as depletions of certain metals/elements, are products of fluid-rock interactions during mineralization, which could lead to the formation of economically viable mineral deposits. Many other elements, other than Na2O and Ba, are involved in fluid-rock interactions. For instance, Gong et al. (2016) revealed, through simulation of hydrothermal fluid-granite interactions, that Mo, Cd, Ni, Bi, Pb, Zn, and Cu were enriched while As was depleted in the granite. Lecumberri-Sanchez et al. (2017) reported that, due to fluid-rock interactions involved in the formation of the Panasqueira tungsten deposit (Portugal), major elements, Al and K, and trace elements, Nb, W, and Sn, were enriched while major elements, Fe and Si, were depleted.

    Enrichments and depletions in certain metals/elements are portrayed by positive and negative geochemical anomalies, and comprise the upper and lower range, respectively, in geochemical data distribution (Shi and Wang, 1995; Xie, 1981). There is a need for further study on elements that become depleted and form negative anomalies. Barsukov et al. (1981) revealed that Ba, Sr, P, Yi, Ti, Zr, and Cr moved out from the ore body and skarns during the formation of a mineral deposit, and Na can form as negative geochemical anomalies. Meanwhile, Barsukov et al. (1981) pointed out that Ba can be regarded as an element indicator for the presence of metal mineral deposits because Ba typically moved out from the ore body and precipitated on the surrounding of ore body, leading to a low content of Ba in the upper of ore body. Chen (1987) pointed out that negative geochemical anomalies in the primary geochemical halos of the gold ore body had a larger area than those of positive geochemical anomalies. These negative geochemical anomalies can be divided into two categories. The first type formed in the surrounding ore body, and from the ore body to outward, the element content gradually increased to the geochemical background. The second type was adjacent to the ore body, formed as conjugate halos. From the ore body to outward, the elemental content gradually increased to the background, then increased to the positive anomaly, and then decreased to the geochemical background. Meanwhile, Chen (1987) found that the magnitude of negative geochemical anomaly was corrected with the depth of ore body, which makes it possible to develop a robust index to evaluate the denudation depth of a mineral deposit. At a deposit scale, for instance, some researchers (e.g., Liu et al., 2016; Ma et al., 2013) reported that negative geochemical anomalies of Na2O, Ba and Sr coincided with zones of fluid-rock interaction and ore bodies. Likewise, in a study of the Anqing hydrothermal Cu deposit in China, Li (1993) found low Ba values above and surrounding the ore body indicating skarn type Cu-Fe mineralization. Therefore, geochemical prospecting should encompass both elements that form positive anomalies but also those that form negative anomalies with respect to mineral deposits. Meanwhile, negative anomalies can be delineated using the same methods that are used for identifying positive anomalies such as the S-A multifractal model.

    In the process of magmatic-hydrothermal fluid-rock interaction, some elements are enriched and others are depleted, resulting in the formation of positive and negative geochemical anomalies. Positive and negative geochemical anomalies can indicate mineralization, alteration and zones of fluid-rock interaction. Meanwhile, positive and negative anomalies have different spatial patterns, representing different geological processes. For example, Shi and Wang (1995) reported that there were three possible spatial arrangements of negative and positive single-element anomalies: (1) negative anomalies accompanied by positive anomalies; (2) only positive anomalies occurring with no negative anomaly; and (3) only negative anomalies with no positive anomaly. These three spatial arrangements may represent different geological processes. The first represents a positive correlation between negative and positive anomalies, suggesting that element concentration by lateral secretion processes may have occurred. The second suggests that the ore-forming material that formed the positive anomalies probably does not originate from the surrounding rocks, but is derived from deeper sources. The third may indicate that the formation of anomalies is related to lithogenesis.

    The ratios of elements that form positive anomalies to elements that form negative anomalies can highlight locations with mineralization and should be further investigated. In this study, the ratio of Mn/Ba, where Mn and Ba form positive and negative anomalies, respectively, was associated with known Fe polymetallic mineralization. This index should be further developed.

  • In this study, two case studies are presented to demonstrate the identification of subtle geochemical anomalies and the significance of negative geochemical anomalies. This paper discusses the opportunities and challenges in recognizing subtle geochemical anomalies and constructing a new indicator for the presence of mineral deposits which considers both positive and negative geochemical anomalies. With the rapid development of the global economy, and an increased demand for mineral resources, there is a need for improved methods to enhance the effectiveness of mineral exploration. Further research should focus on subtle geochemical anomalies and consider both negative and positive anomalies for prospecting magmatic-hydrothermal mineral deposits.

  • This study was supported by the National Natural Science Foundation of China (No. 41772344). Dr. Yihui Xiong is thanked for processing a part of data. Prof. John Carranza, Dr. Jian Wang, and Prof. Yongqing Chen are thanked for their valuable comments and suggestions on the early manuscript. The final publication is available at Springer via https://doi.org/10.1007/s12583-020-1079-2.

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