## 5 Research on Mineral Prospecting Prediction  
### Statistical Analysis of Parameters  
To study the effectiveness of the proposed method, geo-electrochemical integrated technology was employed to conduct mineral exploration predictions in the study area. The content data of the six obtained elements were statistically analyzed using GeoChem Studio 2.5.9 and SPSS 27.0 software (Table 2). Through examination of the skewness and kurtosis values of each element, it was evident that none of the elements followed a normal distribution. Consequently, a logarithmic transformation was applied to the original data. As shown in the box plot (Figure 7), although outliers remained present in Th, W, Mo, and As after logarithmic transformation, the mean values of these elements were closer to their medians, and they were centrally located within the box. This indicates the objectivity of the statistical analysis, suggesting that \"the data of major elements approximate a normal distribution, while the logarithmic-transformed data of trace elements conform to a normal distribution\" [36].  
The coefficient of variation (CV\({}_{1}\)) of the original data serves as an indicator of element dispersion; however, it may occasionally be susceptible to interference from high background values or non-mineralization factors, such as stratigraphy, weathering, and diagenetic modifications. Such interference can obscure the distinction between miner  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Category** & **Parameters** & **U** & **Th** & **W** & **Mo** & **As** & **Pb** \\ \hline \multirow{8}{*}{Original data} & Sample Size N\({}_{1}\) & 462 & 462 & 462 & 462 & 462 & 462 \\ \cline{2-8}  & Maximum & 6.72 & 11.72 & 0.56 & 0.60 & 10.58 & 82.47 \\  & Minimum & 0.02 & 0.02 & 0.01 & 0.01 & 0.01 & 0.91 \\  & Average & 0.89 & 0.76 & 0.07 & 0.05 & 0.60 & 11.58 \\  & Standard Deviation & 1.24 & 1.21 & 0.06 & 0.06 & 0.83 & 13.53 \\  & Skewness & 2.55 & 4.74 & 4.57 & 4.52 & 4.89 & 1.89 \\  & Kurtosis & 6.66 & 30.54 & 28.99 & 30.18 & 46.90 & 3.40 \\  & Coefficient of & 1.39 & 1.59 & 0.85 & 1.17 & 1.38 & 1.17 \\  & Variation CV\({}_{1}\) & & & & & & \\ \hline \multirow{8}{*}{Eliminate the data with exceptional values} & Sample Size N\({}_{2}\) & 393 & 405 & 441 & 433 & 378 & 338 \\ \cline{2-8}  & Average & 0.45 & 0.43 & 0.06 & 0.04 & 0.30 & 4.65 \\ \cline{1-1}  & Standard Deviation & 0.39 & 0.36 & 0.03 & 0.03 & 0.24 & 2.82 \\ \cline{1-1}  & Coefficient of & & & & & & \\ \cline{1-1}  & Variation CV\({}_{2}\) & & & & & & \\ \hline \multirow{8}{*}{
\begin{tabular}{c} CV\({}_{1}\)/CV\({}_{2}\) \\ \end{tabular} } & 1.60 & 1.92 & 1.81 & 1.58 & 1.68 & 1.92 \\ \hline \hline \end{tabular}  
Note: The unit for element content is ug/g.  
\end{table}
Table 2: Statistical parameters of geo-electric chemistry element contents.  
Figure 7: Box diagram of elemental content after logarithmic conversion.  
alization anomalies and uniformly distributed high backgrounds. Therefore, the ratio of the coefficient of variation of the original data (CV\({}_{1}\)) to that of the background data (CV\({}_{2}\)) provides a quantitative measure of the specific contribution to dispersion caused by mineralization. As shown in Figure 8, the CV\({}_{1}\)/CV\({}_{2}\) ratios of the primary mineralization element U, its decay product Th, and associated elements W, Mo, As, and Pb are all greater than 1. This indicates that the high-value anomaly of U in the study area is primarily attributable to mineralization or hydrothermal activity. Moreover, this anomaly not only reflects the intensity of mineralization but also exhibits synergistic behavior with elements such as Th, Mo, As, and Pb during migration and precipitation processes.