## 4 Results and Discussion  
### Sample Testing and Analysis  
In this study, a total of 395 samples were subjected to XRF elemental grade determination. The major elements are primarily Si and Fe (Figure 9a), while the trace elements are mainly Rb and Sr (Figure 9b). Non-ore samples with Fe content below 20% were excluded in subsequent analyses. Elemental geochemical characteristics were then studied for 218 ore samples, focusing on their content characteristics, cluster analysis, and principal component analysis results. After excluding unidentified rare earth elements, the major elements analyzed include Si, Fe, Al, Mg, Ca, K, Ti, Mn, S, and Co, while the trace elements analyzed include Sr, Rb, Cr, Zr, V, Zn, Ni, Cu, Y, Pb, Mo, Th, and Nb. Geochemical data are typical compositional data. Before data analysis, appropriate transformations such as normalization and centered log-ratio (CLR) transformation are required to ensure that the data follow a normal distribution and to eliminate the influence of the \"closure effect\" [78,79]. The composition plane of the elemental contents of the samples is presented in Figure 10. A self-organizing map (SOM) of the sample elements was constructed using a color gradient, where each color corresponds to a different value of a given variable, and each hexagon represents a neuron in the component plane. Elements with similar color patterns exhibit positive correlations, while those with distinct colors exhibit negative correlations. As illustrated in Figure 10b,c, when the number of clusters is set to 2, theDavies-Bouldin (DB) index reaches its minimum value of 0.632, indicating the optimal clustering solution. Figure 10: (**a**) SOM clustering results map; (**b**) clustering of categories map of all output space; (**c**) Davies–Bouldin index diagram. Figure 9: Statistical analysis of the (**a**) major elements and (**b**) trace elements. Based on the self-organizing map of individual sample elements shown in Figure 10a, the sample elements can be divided into two interpretable clusters. In the first cluster, elements such as Mg, Al, Si, S, K, Ca, Mn, and Fe exhibit a pattern where the neuron concentration in the lower-left region is higher, gradually decreasing towards the upper-right corner. However, elements like Ti and Co show a similar pattern to Fe, with a higher neuron concentration in the lower-left region and a gradual decrease towards the upper-right corner. Elements like Zn and Sr, on the other hand, only show small regions of high neuron concentration in the lower-left corner or small regions of lower concentration in the upper-right corner. In contrast, elements such as V, Cr, Ni, Cu, Y, Zr, Nb, Pb, and Mo show an opposite distribution, where concentration increases gradually from left to right, forming a horizontal gradient. This pattern suggests that there is a strong positive correlation among Mg, Al, Si, S, K, Ca, Mn, and Fe, a general positive correlation between Ti, Co, and Fe, a weaker positive correlation between Zn, Sr, and Fe, and a negative correlation between these elements and V, Cr, Ni, Cu, Y, Zr, Nb, Mo, and Pb. In the second cluster, the elements Rb and Th both exhibit lower neuron concentrations in the lower-middle region, with higher neuron concentrations surrounding them. This observation confirms the positive correlation between Rb and Th in this area, with almost no correlation to Fe. Currently, infrared spectroscopy technology can be divided into shortwave infrared (SWIR) and thermal infrared (TIR) based on spectral ranges. With the use of portable spectrometers, data from different spectral ranges have been widely analyzed for mineral exploration to identify and characterize alteration minerals in different regions [80; 81]. In the shortwave infrared (SWIR) range, various diagnostic absorption features based on different functional groups such as H\({}_{2}\)O, Mg-OH, Fe-OH, Al-OH, CO\({}_{3}{}^{2-}\), SO\({}_{4}{}^{2-}\), and NH\({}^{4+}\) can effectively identify hydroxyl, amine, carbonate, and sulfate minerals in geological samples.