## 5 Results and Discussion
### Geochemical Anomalies Identified by Statistical Methods
#### 5.1.2 Mineralization-Related Multi Elements Anomaly Analysis Using Statistical Methods  
RFA was applied to the log-ratio transformed geochemical data to examine the associations among elements. The results are shown in the loading plot (Figure 6) and the biplot (Figure 7). The first factor accounted for 33.0% of the total variance and the second accounted for 15.0%, as shown in Figure 6. The observations from the biplot (Figure 7) revealed two closely associated groups of elements. The first group consisted of Ag, Pb, Hg, Au, Sb, and W, exhibiting positive loadings on factor 1 (F1), while the second group was comprised of Cu, Zn, Co, Cr, Mo, and Ni. The prominent portion of the variance was explained by the loadings of factor 2 (F2), which was mainly influenced by Cu, Zn, and Co. This implied that a group of elements was associated with Cu, Zn, and Co mineralization in the NQ, including the Luliangshan Cu deposit, which primarily consisted of volcanic massive sulfide deposits (VMS) and quartz vein-type Cu-Au mineralization. By applying RFA to limit the influence of outliers, two factors (F1 and F2) were differentiated, as shown in Figure 6.  
The higher correlations among the 15 elements were divided into two groups. The first principal factor (F1) consisted of Au, Ag, Pb, Sb, and Hg, while the second principal factor (F2) consisted of Cu, Zn, Co, Cr, and Zn. The factor loads for Cu, Zn, and Co were greater than 0.5 and those of Cr and Ni were less than 0.25. In addition, considering that Cu and Zn are calcophile elements based on the geological situation of the research area, it was more reasonable to reduce the combination of second main factor effective elements to Cu, Zn, and Co. This was because the VMS deposit found in this area exhibited Cu-Zn  
Figure 6: Factor-loading plots for RFA based on log-ratio-transformed data for the regional geochemical data from the NQ.  
Figure 7: Biplots of RFA based on log-ratio -transformed data for the regional geochemical data, NQ.  
mineralization and was associated with Co. For the first two principal factors, F1 and F2, the contributions of each element to the main factor are shown in Table 3.  
In this study, outliers or high values were removed from the original element table using \(X+3S\). The \(X+3S\) value from the last iteration was then used to replace the high value. The equation for this calculation is shown below:  
\[V=100X/(X+3S) \tag{17}\]  
In Equation (17), \(V\) represents the normalized elemental data, while \(X\) denotes a combination of elemental data following high-value substitution. Additionally, \(X+3S\) is an iterative value, and multiplying by 100 guarantees the normalization of all data within the range of (0, 100).  
The calculation of the combined element value is as follows:  
\[P=\sum a_{i}V_{i} \tag{18}\]  
In Equation (18), \(P\) is the combined element value of the main factor after weighting, \(a_{i}\) (\(i=1\), \(2\), \(\ldots\), \(n\)) represents the load of the element in the main factor (F) as a weight, \(V_{i}\) (\(i=1\), \(2\), \(\ldots\), \(n\)) is the single-element data value after normalization.