## 4 Results and Discussion
### Selection of Element Association Associated with Porphyy Copper Mineralization  
Since stream sediment data belong to compositional data, it is not appropriate to conduct relevant combinatorial analysis of the data without eliminating the closure effect. Based on the original geochemical data, this paper is under the guidance of the study of geological laws, the establishment of geochemical markers, and the exploration of geochemical models (supergene). According to the porphyry deposits in the Gangdese metallogenic belt, part of the geochemical elements combined statistically have been found to be elements of the porphyry copper deposits in combination with Cu, Mo, Au, Ag, W, and Bi, which is convenient to distinguish from other types of ore deposits (Table 3). Spatial Overlay Analysis of Geochemical Singularity Index \(\alpha\)-Value of Porphyry Copper Deposit  
Recent advances in identifying weak geochemical anomalies refer to the singularity mapping technique proposed by [PERSON] [16], and it has been demonstrated as a powerful multifractal tool for identifying weak geochemical anomalies in complex geological settings or areas covered by overburden [20; 22; 23; 43]. The window-based method was used to calculate the local singularity index based on GeoDAS. Considering that the formation of porphyry copper deposits in the western Gangdese metallogenic belt is closely related to the specific geochemical composition elements, the local singularity analysis of individual elements cannot be used to meet the objective of recognizing the weak anomalies of porphyry copper deposits. Therefore, according to the geochemical composition elements of porphyry copper deposits, singularity analysis of each element has been carried out, and the local singular maps of porphyry copper deposits have been obtained by spatial overlay analysis. The spatial distribution map of \(\alpha\)-A (Figure 4b) highlights the weak anomalies relative to \(\alpha\)-Cu raster contour maps (Figure 4a). It is also shown that the spatial overlay singular values (\(\alpha\)-A) are apparently enriched in the acid intrusive rocks and volcanic rocks and spread in the NW-trending direction according to the regional faults. The anomaly threshold values of \(\alpha\)-A and \(\alpha\)-Cu were obtained by the C-A model. Furthermore, the log-log plots of the concentration (\(\alpha\)) versus the number of samples with concentration values greater than or equal to \(\alpha\) are constructed to examine whether or not the distribution of A and Cu follows a fractal. It can be observed that two straight lines can be fitted (Figure 5), suggesting they may satisfy a multifractal distribution. In order to facilitate the analysis of the advantages and disadvantages of the two methods, this paper presents a typical deposit analysis map (Figure 6) based on the known deposit and the abnormal range of the degree of fit. The analysis diagram clearly shows that the recognition degree of porphyry copper is obviously improved compared with the single element method (Figure 6(a1-a3, b1-b3)). For skarn-type copper deposits, there is a large area anomaly in the Ri a deposit. The reason is that the copper ore bodies in the Ri a deposit are produced in the porphyry bodies. Therefore, this method makes it difficult to identify or distinguish such deposits and porphyry copper mines. However, for skarn-type deposits unrelated to porphyry, the method demonstrates a certain degree of distinction, especially the Shesuo deposit (Figure 6(b5)). This method distinguishes porphyry molybdenum ore from the single element method, but both of them have anomalies, indicating that