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Volume 32 Issue 2
Apr.  2021
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Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9
Citation: Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9

Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach

doi: 10.1007/s12583-021-1444-9
More Information
  • As direct prospecting data, geochemical data play an important role in modelling prospect potential. Geochemical element assemblage anomalies are usually reflected by the correlation between elements. Correlation coefficients are computed from the values of two elements, which reflect only the correlation at a global level. Thus, the spatial details of the correlation structure are ignored. In fact, an element combination anomaly often exists in geological backgrounds, such as on a fault zone or within a lithological unit. This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted. To address this problem, we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements. In this method, the sampling area is first divided into a set of uniform grid cells. A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit. The local correlation in each unit is expressed by the Pearson correlation coefficient. The whole area is scanned by the moving window, which produces a correlation coefficient matrix, and the result is portrayed with a thermal diagram. The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain, Henan Province. The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots. Therefore, the local correlation method is effective in extracting geochemical element combination anomalies.
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Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach

doi: 10.1007/s12583-021-1444-9

Abstract: As direct prospecting data, geochemical data play an important role in modelling prospect potential. Geochemical element assemblage anomalies are usually reflected by the correlation between elements. Correlation coefficients are computed from the values of two elements, which reflect only the correlation at a global level. Thus, the spatial details of the correlation structure are ignored. In fact, an element combination anomaly often exists in geological backgrounds, such as on a fault zone or within a lithological unit. This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted. To address this problem, we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements. In this method, the sampling area is first divided into a set of uniform grid cells. A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit. The local correlation in each unit is expressed by the Pearson correlation coefficient. The whole area is scanned by the moving window, which produces a correlation coefficient matrix, and the result is portrayed with a thermal diagram. The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain, Henan Province. The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots. Therefore, the local correlation method is effective in extracting geochemical element combination anomalies.

Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9
Citation: Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9
  • Mineral resources play a critical role in the development of countries and have a constantly increasing demand. The theories and methods behind the highly efficient and precise extraction techniques used to meet this demand are of great importance for the wellbeing of countries and societies. Geochemical anomaly detection methods can be directly used for mineral prospecting. The geochemical anomalies identified by these methods often have a strong correlation with deposits, and some even reveal the existence of such deposits. Therefore, research on geochemical anomaly detection methods is helpful for prospecting minerals.

    Principal component analysis is a multivariable statistical method used to obtain key information from a dataset. It replaces the original features with a much smaller number of new features, which not only reduces the dimensions of the original data but also do not lose the core information. Especially for large samples and multiparameter cases, this method is simpler and more effective. Principal component analysis (PCA) is used to extract the principal components among elements for further analysis. Some elements are neglected because of the samples in other areas, but they may play an important role in extracting geochemical anomalies (Bendich et al., 2018; Jiang et al., 2018; Wang et al., 2018; Cheng et al., 2011). Independent component analysis (ICA) (Gielen, et al., 2018; Park and Kwak, 2018; Painsky et al., 2018; Isomura and Toyoizumi, 2018; Yu et al., 2014) improves the PCA method. The elements extracted through ICA are independent of each other, so the components can retain more features that reflect a deposit (Yu et al., 2012). However, the ICA method is sensitive to noise data and can be time-consuming. Attias (2000) summarized the advantages of PCA, ICA and factor analysis and proposed the independent factor analysis (IFA) (Coston and O'Rourke, 2020; Attias, 2000, 1999a, b) model, which has a better performance in noise processing and stability. Xue et al. (2011) used two or more principal components to construct 2D or 3D "degradation trajectories". Hu et al. (2013) analysed the fluoride geochemistry of groundwater with PCA. We can only find the correlation between two elements, and some of elements are definitely unrelated. We should reduce the number of elements used in the calculation. We usually determine the group of elements through a clustering algorithm. The hierarchical clustering method can be used to show the assemblage of elements in the form of a dendrogram, which is simple and intuitive (Rahimi et al., 2016). It is often used for the preliminary extraction of geochemical elements. K-means minimizes the square error of a cluster (Hartono et al., 2018). Based on the connectedness of the samples, density-based clustering uses the density to extend a cluster and describe the compactness within a neighbourhood (Scitovski, 2018). Correlation coefficients are used to find geochemical element assemblage anomalies. The geosciences have always related data with coordinates (Zhang et al., 2019; Markhvida, et al., 2018; Niven and Deutsch, 2012). The classical methods include the Pearson correlation coefficient, Kendall correlation coefficient, Spielman correlation coefficient, Mulan index, maximal information coefficient (MIC), etc. (Song et al., 2015; Smillie, 2015; Wang et al., 2015; Li et al., 2014; Simon and Tibshirani, 2013; Xu et al., 2007). These methods mainly reflect the correlation of elements at the global level. However, geochemical element assemblage anomalies often exist under certain geological backgrounds, such as fracture zones and specific rocks, which may influence the global value of correlation, and some even are submerged in global areas. To take advantage of the multifractal properties of highly concentrated metals, Cheng and Agterberg delineated such anomalies from the background through a singularity mapping method based on sliding windows (Cheng and Agterberg, 2009). Zuo et al. focused on the identification of weak geochemical anomalies by proposing an estimation algorithm capable of directly processing data containing negative values (Zuo et al., 2015). Chen and Cheng extended the analysis of singularities from being in spatial space to wavelet space to investigate the multiscale nature of geochemical patterns (Chen and Cheng, 2016).

    In this paper, a local correlation coefficient method is proposed, which aims to display the correlation of elements in each part of a region. To avoid the limitation of global-level computation, we use grid partitioning to place irregular sampling points into a regular area. It is of great significance to measure the causes of mineralization.

  • Geochemical anomalies are likely to occur under specific geological conditions, such as fault zones, and localized anomalies might be neglected globally. Thus, the global search of anomalies is less effective. To solve this problem, we propose a local correlation coefficient based on a spatial neighbourhood to reflect the global distribution of elements.

    We refer to a pair of elements as relational elements if there is an anomalous relationship between them. A set of relational elements form a candidate set C, which is denoted as follows,

    Among them, e1 is the key ore-forming element, and e2, e3 and e4 are related elements. e2, e3 and e4 can be related to e1 independently or as an assemblage. The first step of our algorithm is to determine the candidate set of e1. There are many ways to determine the relationship between them, such as R-type clustering, PCA, ICA and other component analysis methods. In this paper, the R-type clustering method is used to determine the candidate set of ore-forming elements.

    Having obtained the candidate set, we are able to construct distribution matrices of relational elements. Specifically, we fill the matrices through coordinate mapping because the elements are distributed in the form of planes. Based on the matrices, the local correlation coefficient is computed by a kernel sliding over them.

    To clarify the process, let X and Y be matrices with the shape of m×n, as we have m×n samples in total. The local correlation coefficients are then calculated on X and Y through a sliding kernel with the size of k×k (k < m, k < n).

    We perform grid partitioning to handle the samples that deviate from a straight line, as shown in Fig. 1, where the area framed by the red kernel is the target of correlation computation. The window slides over the scene in row order with the step of a blue block. The local correlation is computed for one step each time, and grids containing less than one sample are ignored.

    Figure 1.  Grid partition, each area outlined in red has a shape of 3℅3 is the area for correlating the correlation coefficient. The blue box is the length of the step for each calculation.

    The K located at Xij in X is denoted as K(Xij) and K(Yij) in Y. The size of the kernel K determines the coverage of each local correlation coefficient. Affected by the terrain, the samples are hard to align to a line. The number of sampling lines is far smaller than the number of samples on each line. If K is too large, the area that a kernel covers will be too large, and more details of the local correlation will be ignored. In extreme cases, if the size of K is the same as that of the whole sampling area, the global correlation that would be calculated. Therefore, the size of the K should be as small as possible. In addition, the size of the kernel should be odd to obtain a single centre. Considering the two mentioned factors for deciding a size of K, here it should be 3×3.

    Since the data are not normally distributed, the Spearman correlation coefficient, which has no requirement of a normal distribution, is adopted to obtain the correlation of two regions. It is formulated as follows,

    where X and Y are the values of the elements in kernel K.

    After K traverses the all X and Y pairs and the correlation coefficient is calculated, a local correlation matrix is obtained. We denote LC as the local correlation matrix, as shown in (5). It has the local correlation value of the two elements we selected. The higher the value is, the stronger the correlation is between two elements.

    The algorithm process of local correlation coefficients is shown in Table 1.

    Algorithm 1 Local correlation
    Require:
    Two sampling elements X and Y; the kernel size k;
    Ensure:
    local correlation LC;
    1: Grid partition on sampling area. Map the elements into the matrix X and Y. There are m * n boxes in matrix X and Y;
    2: Determine the related elements C;
    3: for i=1 to m do
    4:          for j=1 to n do
    5:                  for Each element in the box whose centre is [i, j] do
    6:                          if There exists elements in the box LS then
    7:                        Put the element in X into LC1.
    8:                        Put the element in Y into LC2.
    9:                end if
    10:      end for
    11:      if The size of LC1 or LC2 is less than 1 then
    12:              continue
    13:      else
    14:                          LC[i, j] < =Corr(LC1, LC2)
    15:      end if
    16:          end for
    17: end for
    18: return LC.

    Table 1.  Algorithm 1 Local correlation

  • The Zhonghe mining area is located in Henan, China. The Luoning to Sanmenxia provincial road passes through the central part of the investigated area, and the road is suitable for travel. In addition, the distribution of the sampling is regular.

    Using a map with a scale of 1 : 10 000 (covering covers 17 km2), altered rock and porphyry silver-lead ore have been discovered in this area. Sliver and vanadium were found within a type of structurally altered rock and porphyry in southern Zhonghe.

    A geochemical soil survey at a scale of 1 : 10 000 was carried out in the area, and the concentrations of Cu, Co, Bi, Ni, As, W, Zn, Sb, Mo, Au and Pb in each sample were analysed. A medium silver-lead deposit was found in this area. Figure 2 shows the geological map of the Zhonghe region.

    Figure 2.  Geological map of the Zhonghe area. There is a silver-lead-zinc deposit located in the area. The fault zone passes through the whole area. 1. Large porphyrite interbedded with andesite in the middle section of the Xushan Formation; 2. andesite with andesite porphyrite in the upper Xushan Formation; 3. Wucheng loess; 4. normal fault and occurrence; 5. regional large faults and occurrence; and 6. river.

    Another area called the Gushanling is located in the southern part of Shanxian, as shown in Fig. 3.

    Figure 3.  Geological map of the Gushanling. There is an ore deposit of silver-lead-zinc in the area. The fault zone passes through it. 1. Large porphyrite interbedded with andesite in the middle section of the Xushan Formation; 2. andesite with andesite porphyrite in the upper Xushan Formation; 3. Wucheng loess; 4. alluvium; 5. hypo-rhyolite porphyry; 6. subtrachytic porphyry dikes; 7. normal fault and occurrence; and 8. regional large faults and occurrence.

    Through the investigation of ore points and geological mapping at a scale of 1 : 10 000, several SN-trending and NE-trending tectonic belts have been found in the area. The filling materials are mostly quartz veins and fractured rocks, with different degrees of brass mineralization and blue cupric mineralization. The mineralization is strong in the northwestern mining area. In the eastern part of the mining area, ferritization is visible.

    A geochemical soil survey at a scale of 1 : 10 000 was carried out in the area, and the concentrations of Cu, Co, Bi, Ni, As, W, Zn, Sb, Mo, Au, and Pb in each sample were analysed. The main anomalous element in this area is copper, and three copper ore deposits have been identified in the sampling area. Figure 3 shows the geological map of the Gushanling area.

  • In the Zhonghe area, the candidate set is C={Ag, Pb, Zn|Ag, Pb, Zn}. We calculated three kinds of local correction coefficients. Figures 4a-4c shows the correlation counter map, and Fig. 4d is the geologic map of the Zhonghe area.

    Figure 4.  Local correlation coefficients of the elements in the candidate set of the Zhonghe area. The black line is the fault zone in the geologic map. (a) Local correlation of Pb and Zn; (b) local correlation of Pb and Ag; (c) local correlation of Ag and Zn; (d) geologic map of the Zhonghe area.

    In Fig. 4, red, yellow and blue are used to represent high moderate and low correlation coefficients between the two elements, respectively.

    The fault zone in Fig. 4d divides the Zhonghe area into two parts. The part with a high correlation coefficient (the red part in the Figs. 4a-4d)) is mainly concentrated in the left half of the three graphs compared to Fig. 4d (the deposit is only one of Zhonghe lead zinc ore belt). The two sides are of different lithologies. Pt2χ2 (Dapan andesite and andesite porphyry in the middle section of the Xushan Formation) is mainly located in the left part. It can be inferred that the geochemical anomalies in this area may be related to the rock of Pt2χ2.

    The fault zone in the Zhonghe area is abundant. It is found that high correlation coefficients are mainly concentrated in the place where the fault zone passes through. It is presumed that the geochemical anomalies in this area may be related to the fracture zone where the ore was formed.

    The field investigation shows that the deposit is consistent with the distribution of the actual silver-lead deposit.

  • In the Gushanling area, the candidate set is C={Cu|Ag, Ni, Zn, Co, Pb}. We calculated three kinds of local correction coefficients. Figures 5a-5e shows the contour map of the correction coefficients, and Fig. 5f is the geologic map of the study area.

    Figure 5.  Local correlation coefficients of the elements in the candidate set of the Gushanling area. Most of the results are strongly consistent with mineral occurrence. (a) Local correlation of Cu and Zn; (b) local correlation of Cu and Pb; (c) local correlation of Cu and Co; (d) local correlation of Cu and Ag; (e) local correlation of Cu and Ni; (f) geologic map of the Gushanling area.

    The fault zone in this area is obviously developed. In the intersection of the fault zone and the red part, there is a deposit, corresponding to Fig. 5f. In addition, where the yellow region converges with the fault zone, a copper ore occurrence has been found, as shown in Figs. 5a, 5e. Although the correlation of the yellow part is weaker than that of the red area, it also represents a certain degree of correlation, which cannot be ignored. As shown in Figs. 5a-5f, the river parts obviously have a high level of correlation, and part of the high level can reflect the existence of the ore deposit to some extent.

  • As shown in Section 1, we traverse the whole area using a kernel a the size of k. The value of k will influence the result. We have explained why we chose k=3 for the calculation of local correlation coefficients, this is calculated based on the data in Fig. 4a. In this section, we conduct an experiment on different sizes of k.

    Figure 6 shows the correlation map with different kernel sizes. As shown in Figs. 6a, 6b, in comparison with the minimum size of k in Fig. 6c, the correlation map becomes fuzzy when k becomes larger.

    Figure 6.  Local correlation coefficients of Pb and Zn with kernel sizes of 3, 5 and 7 at the Zhonghe area. (a) Local correlation of Pb and Zn with a kernel size of 3; (b) local correlation of Pb and Zn with a kernel size of 5; (c) local correlation of Pb and Zn with a kernel size of 7.

  • As direct prospecting data, geochemical data play an important role in predictive modelling. Abnormal combinations of geochemical elements usually manifest in the correlation between elements. The correlation coefficient is calculated with the values of two elements, which only reflect the correlation at the global level and ignores the spatial details of the correlation structure. To solve this problem, we propose a local correlation coefficient based on a spatial neighbourhood to reflect the global distribution of elements. We first divide the whole area into finer grid cells and then define a moving window. The local correlation of the elements in each window will be represented by the Pearson correlation coefficient. The entire area is scanned by moving the window to obtain the correlation coefficient matrix and express it with a heat map. Applying this method to the Xiaoshan area of Henan Province, the results show that the places where chemical element anomalies appear are mainly located in the fault zone, which has guiding significance for actual prospecting work.

    Empirically, geochemical anomalies tend to coexist with fault activity, rock masses and stratigraphic distributions. Evidently, it is sensitive to the known geological structure. The formation and transfer of geochemical anomalies are closely related to the structure of metallogenic epochs. Spectral clustering based on neighbourhood constraints requires a manually designated number of clusters, which usually refers to the amount of main rock masses contained in a geological map. If there are strata from diverse times in a district, clustering is performed on a few clusters, and the strata can be divided in the results. Comparatively, if the strata in a district are from the same time, the result can mostly divide the background and the anomaly. However, if the anomaly covers a relatively large area, the method can resolve a major part of the anomaly in the district, while more rigorous anomaly locations should be further extracted. Moreover, the results will vary based on whether tectonic mineralization occurs. The clustering performance is prone to changes across different investigated districts. Generally, the classification can still attain decent results on a large-area stratum background on the condition that the number of clusters is sparse. The clustering algorithm is also able to extract small geologic bodies, such as semicircles, annulations, and plaques.

  • This research was supported by the National Natural Science Foundation of China (Nos. 41272359, 210100069). We thank the No.1 Institute of Geological and Mineral resources survey of Henan for its data support. We also thank Prof. Renguang Zuo and Prof. Yongqing Chen for their comments and suggestions. The final publication is available at Springer via https://doi.org/10.1007/s12583-021-1444-9.

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