*4.2. Data-Driven CoDA and Its Based Element Association Extraction*

#### *4.2. Data-Driven CoDA and its Based Element Association Extraction* 4.2.1. Correlation Analysis

4.2.1. Correlation Analysis The primary halos were processed by cluster analysis (Figure 10). The elements can be roughly divided into two groups: Au, As, Sb, Ag, W, Hg and Cu, Zn, Bi, Pb, Co, Mo. Among them, the elements most closely associated with Au are As, Sb and Ag; the Au, As, Sb and Cl are moderate volatile elements [141] often associated in gold mineralization like Hg (indicator of volcanism). Ag is often associated to gold as electrum. Hg should be the front halo indicating element of the gold orebody; the correlation coefficient between Au and As reaches 0.8, and most of the As exists within arsenopyrite, which is an important gold-bearing mineral. Therefore, this Au-As-Sb should be the element association The primary halos were processed by cluster analysis (Figure 10). The elements can be roughly divided into two groups: Au, As, Sb, Ag, W, Hg and Cu, Zn, Bi, Pb, Co, Mo. Among them, the elements most closely associated with Au are As, Sb and Ag; the Au, As, Sb and Cl are moderate volatile elements [141] often associated in gold mineralization like Hg (indicator of volcanism). Ag is often associated to gold as electrum. Hg should be the front halo indicating element of the gold orebody; the correlation coefficient between Au and As reaches 0.8, and most of the As exists within arsenopyrite, which is an important gold-bearing mineral. Therefore, this Au-As-Sb should be the element association of mineralization reflecting a middle- and low-temperature metallogenic environment.

relationship to the orebody. W, Mo, Co and Bi have two concentrations, the first one is located near the surface and the second one is distributed near the elevation of 2500 m. The Zaozigou gold deposit has multi-phase mineralization, forming a complicated spatial distribution of elements, while the C-V model can better identify the anomaly for recog-

nizing the pattern of the primary geochemical halo in the Zaozigou gold deposit.

#### of mineralization reflecting a middle- and low-temperature metallogenic environment. 4.2.2. Element Associations Identification Based on clr-Biplot

4.2.2. Element Associations Identification Based on clr-Biplot Geochemical data are typically compositional data, and if traditional multivariate statistical methods (e.g., principal component analysis, factor analysis, etc.) are applied Geochemical data are typically compositional data, and if traditional multivariate statistical methods (e.g., principal component analysis, factor analysis, etc.) are applied directly to the raw geochemical data, it may lead to erroneous results. Therefore, the raw data should be properly transformed before data analysis is performed.

directly to the raw geochemical data, it may lead to erroneous results. Therefore, the raw data should be properly transformed before data analysis is performed. Data from 12 geochemical elements were clr-transformed, and the skewness values of the clr-transformed data were statistically obtained (Figure 11). Compared with the raw data, clr-transformed data has the lower skewness value around zero, indicating that the data distribution after clr transformation tends to be more normal in character (Figures 11 and 12).

and 12).

and 12).

and 12).

mation.

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**Figure 10.** Hierarchical dendrogram of cluster analysis in Zaozigou gold deposit. **Figure 10.** Hierarchical dendrogram of cluster analysis in Zaozigou gold deposit. **Figure 10.** Hierarchical dendrogram of cluster analysis in Zaozigou gold deposit. **Figure 10.** Hierarchical dendrogram of cluster analysis in Zaozigou gold deposit.

Data from 12 geochemical elements were clr-transformed, and the skewness values of the clr-transformed data were statistically obtained (Figure 11). Compared with the raw data, clr-transformed data has the lower skewness value around zero, indicating that the data distribution after clr transformation tends to be more normal in character (Figures 11

Data from 12 geochemical elements were clr-transformed, and the skewness values of the clr-transformed data were statistically obtained (Figure 11). Compared with the raw data, clr-transformed data has the lower skewness value around zero, indicating that the data distribution after clr transformation tends to be more normal in character (Figures 11

Data from 12 geochemical elements were clr-transformed, and the skewness values of the clr-transformed data were statistically obtained (Figure 11). Compared with the raw data, clr-transformed data has the lower skewness value around zero, indicating that the data distribution after clr transformation tends to be more normal in character (Figures 11

mation. **Figure 11.** Comparison of the skewness values of elemental data before and after clr transfor-**Figure 11.** Comparison of the skewness values of elemental data before and after clr transformation. mation.

**Figure 12.** Histogram of Au. (**a**) Raw data. (**b**) clr-transformed data. (**a**) (**b**) **Figure 12. Figure 12.** Histogram of Au Histogram of Au. ( . (**aa**) ) Raw data. ( Raw data. (**b b** ) ) clr-transformed data. clr-transformed data.

**Figure 12.** Histogram of Au. (**a**) Raw data. (**b**) clr-transformed data. Factor analysis (FA) is used to extract element associations. Four factors are extracted as element associations to indicate different geological and geochemical meanings. From the view of the loadings of FA, factor F1 (34.95%, five variables) represents Cu, Pb, Zn, Ag and Bi, which are a group of medium-temperature elements; factor F2 (12.83%, three variables) is the element association of Au, As and Sb, representing the Au-polymetallic sulfide phase, which is the most dominant phase of gold mineralization; factor F3 (11.13%, one variable) indicates Mo, which is a high-temperature element and may be related to

magmatism; factor F4 (9.26%, two variables) is Sb and Hg association, where Sb mainly exists within the form of stibnite within the quartz-stibnite veins and Hg is closely related to fractures (Figure 13). The distribution of each factor in the three-dimensional space is shown in Figure 14a. file was selected for analysis by profile cutting (Figure 14). It can be intuitively understood that the F2 factor is closely related to mineralization, and its spatial distribution is well matched with the known orebodies in the exploration profile. The F4 factor is closely related to ore-bearing fractures, so that the Sb-Hg element association could be used as evidence of deep fracture extension.

To analyze and show the geological meaning of each factor more clearly, the 85# pro-

(**a**) (**b**)

Factor analysis (FA) is used to extract element associations. Four factors are extracted as element associations to indicate different geological and geochemical meanings. From the view of the loadings of FA, factor F1 (34.95%, five variables) represents Cu, Pb, Zn, Ag and Bi, which are a group of medium-temperature elements; factor F2 (12.83%, three variables) is the element association of Au, As and Sb, representing the Au-polymetallic sulfide phase, which is the most dominant phase of gold mineralization; factor F3 (11.13%, one variable) indicates Mo, which is a high-temperature element and may be related to magmatism; factor F4 (9.26%, two variables) is Sb and Hg association, where Sb mainly exists within the form of stibnite within the quartz-stibnite veins and Hg is closely related to fractures (Figure 13). The distribution of each factor in the three-dimensional space is

**Figure 13. Figure 13.** Distribution of element Distribution of element loadings in each factor. loadings in each factor.

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**Figure 12.** Histogram of Au. (**a**) Raw data. (**b**) clr-transformed data.

shown in Figure 14a.

To analyze and show the geological meaning of each factor more clearly, the 85# profile was selected for analysis by profile cutting (Figure 14). It can be intuitively understood that the F2 factor is closely related to mineralization, and its spatial distribution is well matched with the known orebodies in the exploration profile. The F4 factor is closely related to ore-bearing fractures, so that the Sb-Hg element association could be used as evidence of deep fracture extension.

#### *4.3. Knowledge-Driven CoDA and Its Based Element Association Extraction*

From the anomaly data volume model based on the C-V method in this paper, it is clear to recognize the distribution of each element in a three-dimensional space.


Based on the analysis above, the knowledge of the element associations of the front halo, near-ore halo and tail halo can be summarized. Moreover, corresponding element associations are quantitatively extracted as compositional balances by the knowledgedriven CoDA framework (Figure 4), that is, the front halo association is B1 (As-Sb-Hg vs Au-Ag-Cu-Pb-Zn-W-Bi-Co-Mo), the near-ore halo association is B2 (Au-Ag-Cu-Pb-Zn vs As-Sb-Hg-W-Bi-Co-Mo), and the tail halo association is B3 (W-Bi-Co-Mo vs As-Sb-Hg-Au-Ag-Cu-Pb-Zn) (Figure 15).

**Figure 14.** (**a**) Factor scores distribution of each factor. (**b**) Distribution of factor scores in the 85# exploration profile. **Figure 14.** (**a**) Factor scores distribution of each factor. (**b**) Distribution of factor scores in the 85# exploration profile. *Minerals* **2022**, *12*, x FOR PEER REVIEW 18 of 32

Au-Ag-Cu-Pb-Zn-W-Bi-Co-Mo), the near-ore halo association is B2 (Au-Ag-Cu-Pb-Zn vs As-Sb-Hg-W-Bi-Co-Mo), and the tail halo association is B3 (W-Bi-Co-Mo vs As-Sb-Hg-Au-Ag-Cu-Pb-Zn) (Figure 15). **Figure 15.** Primary Geochemical halo element associations. (**a**) Front halo association B1; (**b**) Nearore halo association B2; (**c**) Tail halo association B3. **Figure 15.** Primary Geochemical halo element associations. (**a**) Front halo association B1; (**b**) Near-ore halo association B2; (**c**) Tail halo association B3.

The mineral resources prediction model is usually summarized as text, diagrams, and tables by integrating comprehensive metallogenic information, such as orebodies, ore

Orebodies are strictly controlled by fractures in the Zaozigou gold deposit. The 30m buffer zone of the fractures can effectively reflect the influence range of the fracture, which can be used as a mineral prediction indicator. Factor F4 is an element association of Sb-Hg, which has close relationship with fractures, and can be used as a favorable indicator

Geochemical element distribution, association and zonation are favorable indicators for mineral resources prediction. The geochemical anomalies are extracted by the multiple fractal C-V model in Section 4.1, among which the middle anomaly of Au can well reflect the spatial distribution of orebodies (Figure 7b) and should be used as an important quantitative indicator. Near-ore halo element association B2 is extracted by the knowledgedriven CoDA and also can express the location of orebodies well; it should be another mineral prediction indicator (Figure 15). The ratio of front halo to tail halo is an important geochemical parameter for predicting orebodies, and B1/B3 is regarded as a prediction

In summary, the geological and geochemical quantitative prediction model at the

*4.4. Geological and Geochemical Quantitative Prediction Model at Depth of Zaozigou Gold* 

depth of the Zaozigou gold deposit is constructed as in Table 3.

portant meaning for guiding mineral exploration [142].

for inferring deep fractures [143–146] (Figure 14a).

indicator accordingly [143] (Figure 16).

*Deposit*

**Ore-Forming** 

Geochemistry

Geology Fracture

Primary geochemical halo

zation prediction.

#### *4.4. Geological and Geochemical Quantitative Prediction Model at Depth of Zaozigou Gold Deposit*

The mineral resources prediction model is usually summarized as text, diagrams, and tables by integrating comprehensive metallogenic information, such as orebodies, ore deposits, ore fields and even metallogenic zones. Establishing a mineral resources prediction model is an effective way to discover potential deposits and has significantly important meaning for guiding mineral exploration [142].

Orebodies are strictly controlled by fractures in the Zaozigou gold deposit. The 30 m buffer zone of the fractures can effectively reflect the influence range of the fracture, which can be used as a mineral prediction indicator. Factor F4 is an element association of Sb-Hg, which has close relationship with fractures, and can be used as a favorable indicator for inferring deep fractures [143–146] (Figure 14a).

Geochemical element distribution, association and zonation are favorable indicators for mineral resources prediction. The geochemical anomalies are extracted by the multiple fractal C-V model in Section 4.1, among which the middle anomaly of Au can well reflect the spatial distribution of orebodies (Figure 7b) and should be used as an important quantitative indicator. Near-ore halo element association B2 is extracted by the knowledgedriven CoDA and also can express the location of orebodies well; it should be another mineral prediction indicator (Figure 15). The ratio of front halo to tail halo is an important geochemical parameter for predicting orebodies, and B1/B3 is regarded as a prediction indicator accordingly [143] (Figure 16). *Minerals* **2022**, *12*, x FOR PEER REVIEW 19 of 32

**Figure 16.** Spatial distribution of As-Sb-Hg(B1)/W-Bi-Co-Mo(B3). **Figure 16.** Spatial distribution of As-Sb-Hg(B1)/W-Bi-Co-Mo(B3).

**Table 3.** Geological and geochemical quantitative mineral resource prediction model at depth of Zaozigou gold deposit. In summary, the geological and geochemical quantitative prediction model at the depth of the Zaozigou gold deposit is constructed as in Table 3.

Element association of fracture Hg-Sb (F4)

To address the scientific problem of quantitative mineralization prediction at large depths, the previous section quantitatively extracted the deep geochemical mineralization signatures and constructed a geological and geochemical quantitative mineral resource prediction model at depth. In this section, the MaxEnt model and GMM are applied to carry out the 3D MPM to quantitatively predict deep mineral resources, and the uncertainty evaluation of the two models is performed for improving the accuracy of minerali-

Element association of near-ore halo Au-Ag-Cu-Pb-Zn (B2)

As-Sb-Hg(B1)/W-Bi-Co-Mo (B3)

*4.5. Three-Dimensional MPM Based on Machine Learning*

Ore-forming element Geochemical anomaly Au

Geochemical parameter (Front halo/tail halo)


**Table 3.** Geological and geochemical quantitative mineral resource prediction model at depth of Zaozigou gold deposit.
