## 5 Results and Discussions
### Comparison Results and Discussion of Various Methods  
#### 5.4.1 Necessity and Practicality of Data Leveling  
Geochemical data leveling is especially important in mineral exploration because it directly affects the accuracy and efficiency of mineral resource assessment. During the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Elements** & **Formula** & **R\({}^{2}\)** \\ \hline \multirow{8}{*}{north} & Ag & y = 2.08x + 4.12 & 0.98 \\  & Cu & y = 2.53x \(-\) 2.81 & 0.85 \\  & Pb & y = 1.59x \(-\) 1.11 & 0.98 \\  & Zn & y = 1.73x \(-\) 2.31 & 0.98 \\  & W & y = 1.38x \(+\) 0.46 & 0.98 \\  & Sn & y = 2.28x \(+\) 0.57 & 0.98 \\  & Mo & y = 2.16x \(+\) 1.51 & 0.99 \\  & As & y = 1.42x \(-\) 0.13 & 0.98 \\  & Bi & y = 2.34x \(+\) 4.30 & 1.00 \\  & Ni & y = 1.90x \(-\) 2.36 & 0.99 \\  & Co & y = 2.09x \(-\) 1.96 & 1.00 \\  & Sb & y = 1.96x \(+\) 2.14 & 0.95 \\ \hline \multirow{8}{*}{south} & Ag & y = 1.08x + 1.10 & 0.92 \\  & Cu & y = 1.81x \(-\) 1.84 & 0.93 \\  & Pb & y = 1.44x \(-\) 0.81 & 0.91 \\  & Zn & y = 1.21x \(-\) 0.55 & 0.87 \\  & W & y = 1.65x \(+\) 0.70 & 0.97 \\  & Sn & y = 0.97x \(+\) 0.50 & 0.91 \\  & Mo & y = 1.26x \(+\) 0.65 & 0.85 \\  & As & y = 1.72x \(-\) 0.95 & 0.94 \\  & Bi & y = 1.40x \(+\) 1.29 & 0.86 \\  & Ni & y = 1.14x \(-\) 0.93 & 0.87 \\  & Co & y = 0.95x \(-\) 0.26 & 0.84 \\  & Sb & y = 1.73x \(+\) 0.33 & 0.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Fitted curve parameters for each element within the 1000 m north–south range. exploration process, data typically come from vast geographical areas and different time periods, with multiple teams using various methods and instruments for sampling and analysis. Due to these differences, unleveled data can mislead interpretation, leading to overestimation or underestimation of resource potential. Necessity: In mineral exploration, data leveling is necessary because it directly impacts the consistency and reliability of the data. Since exploration data often come from different time periods, different technical teams, and various instruments, unadjusted data may have significant systematic biases, such as differences in detection limits and measurement errors. These uncorrected biases can lead to misinterpretations of geochemical signals, especially when comparing the concentrations of ore-forming elements, potentially overlooking actual geological anomalies or mistakenly identifying normal backgrounds as anomalies. Data leveling eliminates these differences, ensuring that all data are evaluated and interpreted on the same baseline, improving the comparability of data across regions, thus providing accurate scientific evidence for mineral exploration. Practicality: From a practical perspective, data leveling enhances the application value of geochemical data in the exploration process. Leveled data are more suitable for generating geochemical anomaly maps and conducting multi-element correlation analysis, which are key tools for identifying potential mineralized areas. For instance, adjusted data can more accurately depict the surface distribution patterns of metallic elements, helping geologists pinpoint hotspot areas of mineralization, thereby guiding field exploration and drilling activities. Moreover, leveled data are crucial for establishing and applying geochemical exploration models, which can effectively predict and assess future mineral potential, thus providing quantitative support for exploration decisions. The enhanced accuracy and reliability of leveled data significantly improve resource utilization efficiency, reduce exploration risks, and provide a solid scientific foundation for mineral development.