## 4 Geochemical Characteristics of Overburden Soils
### Geochemical Characteristics of Trace Elements  
Geochemical prospecting has proven to be an effective approach in gold exploration work so far, and the study of epithermal gold deposits has garnered significant attention from numerous scholars [65; 66]. In the field of geochemical gold exploration, the analysis of coupled elemental relationships and the determination of their significance are of particular importance [67]. Studies such as the native halo of gold deposits, trace element patterns [68], and statistical relationships between various elements [69] play a crucial role in addressing gold exploration challenges. To elucidate the distribution patterns of geochemical elements within the overlying strata of the Mahuagou mining area and establish relationships among element combinations, this study conducted content analysis on soil samples containing 17 metallic and non-metallic elements closely associated with gold mineralization, including Ag, As, Be, Bi, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Sn, W, Zn, and Au. A statistical analysis was subsequently performed on the collected data. A systematic clustering analysis was conducted on the 17 metallic elements in the soil samples. It is a data analysis technique that divides objects or data points in a dataset into different groups or categories, and it finds widespread applications in many domains. Prior to analysis, the Z-score method was applied to standardize the original data, unifying data of different magnitudes onto a common scale. Calculated Z-Score values replaced the original values for analysis, ensuring data comparability. Ward's Method is used for processing sample data, and it is a systematic clustering method for dividing data points into different clusters or groups. The advantage of this method is to select, at each step, two existing clusters to merge in order to minimize the increase in their combined variance. Consequently, this method is capable of producing compact and relatively evenly-sized clusters. Employing squared Euclidean distance as the distance metric, the results of the analysis are presented in Figure 6.  
Figure 6: Phylogenetic diagram of soil elements by systematic clustering analysis. At a similarity level of 7.5 distance coefficient, all elements can be classified into four categories: The first category includes As, Sb, Mo, Bi, W, and Hg. Among them, the cooccurrence of Mo, W, and Bi elements is often associated with alkali granite related to W-Sn mineralization, consistent with the nature of soil parent rocks [70]. The combination of Hg, As, and Sb elements is closely linked to shallow, low-temperature hydrothermal mineralization, often manifesting as elements in the halo zone of hydrothermal gold deposits, serving as significant indicators for mercury, antimony, and gold deposits. This combination not only inherits the specific characteristics of the parent rock but also retains effective indicators of deep-seated mineralization. The second category encompasses Cd, Pb, Co, and Zn, which commonly occur in polymetallic sulfide hydrothermal mineralization and are also prominent lower-level elements in gold deposits [71]. The third category comprises Ag, Sn, Au, Cr, Ni, and Cu, forming a typical assemblage in medium-temperature ore-forming zones. Be elements constitute a separate category, typically acting as late-stage anomaly indicators in gold deposits. Even more, the cluster analysis displays the relationship between elements and rock types. The first category (As, Sb, Mo, Bi, W, and Hg) is closely associated with granite; it may indicate the crucial metallogenic significance of the biotite monzonitic granite (\(\gamma\)Pt\({}_{3}\)_Zx_) in the mining area. The second category (Cd, Pb, Co, Zn, Ag, Sn, Au, Cr, Ni, and Cu) is associated with mafic rocks and ultramafic rocks, and Au has displayed an affinity for such elements; this may indicate that the mafic-ultra mafic veins have a significant influence on gold mineralization.