## 5 Results and Discussions
### Raw Data Results  
After the preliminary exploratory analysis of the original data in the previous sections, it was found that the study area is not suitable for displaying geochemical anomaly maps  
Figure 5: Schematic diagram of the boundary normalization method. using unleveled data. To compare with the results of subsequent leveling operations, the raw data geochemical maps of the elements are shown here. To ensure the validity of the data, this study does not use the method of adding the standard deviation to the mean to determine the lower limit of anomalies, as this method requires the data to generally follow a normal distribution. However, the data before leveling does not meet this condition. Therefore, this study adopts the cumulative frequency method, plotting the geochemical anomaly map by calculating the cumulative frequency values of each element at 5, 15, 25, 40, 60, 75, 85, 95, and 100 percentiles. The specific band values are shown in Table 4.  
Based on the band values of each element in Table 4, interpolation was performed using the inverse distance weighting (IDW) method to obtain the raw data single-element geochemical maps (Figure 6). From the distribution of the geochemical maps of each element, combined with the data distribution characteristics in Table 3, it can be observed that the quantile plot closely follows a normal distribution. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Elements** & **5** & **15** & **25** & **40** & **60** & **75** & **85** & **95** & **100** \\ \hline Ag & 0.04 & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 & 0.11 & 0.15 & 10.00 \\ Cu & 10.50 & 11.94 & 13.29 & 15.90 & 20.46 & 23.22 & 26.10 & 35.01 & 22,510.00 \\ Pb & 10.50 & 13.60 & 15.70 & 17.60 & 19.45 & 21.13 & 23.11 & 29.21 & 1261.00 \\ Zn & 33.59 & 42.50 & 45.20 & 50.20 & 57.21 & 64.10 & 73.50 & 107.46 & 5230.00 \\ W & 0.81 & 0.95 & 1.12 & 1.34 & 1.55 & 1.74 & 2.09 & 3.64 & 207.80 \\ Sn & 1.78 & 1.95 & 2.13 & 2.45 & 2.94 & 3.77 & 4.95 & 7.34 & 200.00 \\ Mo & 0.43 & 0.64 & 0.73 & 0.80 & 0.89 & 1.00 & 1.28 & 1.84 & 382.70 \\ As & 2.61 & 4.30 & 5.60 & 7.20 & 9.15 & 10.62 & 12.25 & 27.40 & 1912.60 \\ Bi & 0.09 & 0.10 & 0.12 & 0.17 & 0.23 & 0.28 & 0.31 & 0.48 & 584.60 \\ Cr & 10.08 & 15.13 & 24.50 & 35.56 & 46.42 & 53.31 & 58.86 & 77.92 & 1882.00 \\ Co & 1.74 & 3.13 & 5.85 & 7.61 & 9.96 & 11.20 & 12.40 & 18.14 & 101.10 \\ Sb & 0.27 & 0.37 & 0.45 & 0.63 & 0.99 & 1.14 & 1.40 & 3.77 & 946.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stratification values of the raw data using the cumulative frequency method. Figure 6: Probability density plot of different quantiles. Figure 7 shows that elements such as Ag, Mo, and Sb exhibit obvious shift phenomena. Among them, Ag and Mo primarily show higher background values in the northern region, while the Sb element shows the opposite--the background in the northern region is relatively lower. For Ag and Mo elements, the average and median values are relatively close, and the shift effect is mainly caused by the condition that the minimum values of these two elements in the northern region are much larger than those in the southern region. From the anomaly distribution map of Ag, it is clear that the boundary between the northern and southern regions is distinct, with the northern region generally exhibiting higher background values. However, based on the distribution of mineral points, this is obviously unreasonable. The reason for the higher background of Ag in the northern region is that in the raw data, although the percentiles in the northern and southern regions are similar, the minimum value in the northern region is 0.06, while the minimum value in the southern region is 0.01. The cumulative frequency of Ag starts at 0.04 (Table 3); the same logic applies to Mo. On the other hand, Sb shows the opposite trend, where the minimum values in the northern and southern regions are similar, but the median of Sb in the northern region is only half that of the southern region.