## 4 Results and Discussion
### Construction of a Quantitative Optimization Model for Prospecting Target Areas
However, it is evident that there are notable differences in regional prospecting research based on regional geophysical and geochemical information, which serves as a valuable reference for more in-depth research on the genesis of regional Au deposits and regional prospecting. Moreover, the individual contributions of parameter variables such as SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), Na\({}_{2}\)O, \(\Delta\)Tx, and \(\Delta\)Td in preferred model I are markedly greater than the contribution of the Au element. This may indicate that Au deposits (points) in the study area are predominantly formed in the fractured granite belt, and that a majority of Au element anomalies do not result in the output of Au deposits (points). The coefficient of the Au variable in preferred model I is negative, and its individual contribution to the model is 1.84%. A preliminary investigation indicates that there is no evident correlation between the elevated geochemical anomaly of the Au element and the delineation of an Au ore target area. However, this observation merely reflects the fact that 69% of Au deposits (points) are situated in low-anomaly regions, and 9.6% of Au deposits (points) are even distributed in negative anomaly areas. Therefore, this paper argues that the anomalous Au element and associated combined anomalies alone cannot be used to optimally study regional Au metallogenesis. This conclusion requires attention and further in-depth research in future metallogenic optimization studies. Preferred model II: A total of 78 known ore-bearing units are distributed within this category of preferred ore-bearing units. Typical mineral deposits include the Xiaocahu small gold deposit in Anxi County (carlin-type) and the Xinjinchang small gold deposit in Xihu Township (structural altered rock-type), Anxi County (Table 2). The individual contribution of Au to the model in this model is as high as 18.6%, and its coefficient is negative. Combined with its R0 = \(-\)0.89 (which is negative), it is believed that the ore-bearing units identified by this model are more likely to be associated with higher Au element anomalies. Preferred model III: Only six known ore-bearing units are distributed among the preferred level-I ore-bearing units. Typical mineral deposits include the Jinchanggou gold deposit in Subei County and the Jingoujing gold deposit in Anxi County (Table 2). The Auh variable in this model alone contributes 15.3% to the model, and its coefficient is negative. Combined with its R0 = \(-\)1.41 (negative), it is believed that the adjustment and restoration of the Au element of the regional geochemical information have solved the problem of some Au deposits (points) not having corresponding Au element anomalies. At the same time, it confirms the irreplaceable and important role of the Auh parameter in the quantitative optimization of prospecting target areas, providing new ideas and methods for future research in the processing of geochemical information and optimization of metallogenesis. 5. Discussion on the contribution of aeromagnetic data in the modelsIn preferred mode I, the variable coefficient of \(\Delta\)Tx is positive, while the variable coefficient of \(\Delta\)Td is negative. The individual contributions of the two exceed 10%, and their cumulative height is 39.1%. This highlights the crucial importance of regional aeromagnetic data in identifying promising regions for gold mineral exploration, and the significant discrepancy is beneficial for prospecting. It can be inferred from the aeromagnetic geophysical characteristics that the output of Au deposits (points) in the region is closely related to the aeromagnetic gradient zone. This conclusion is consistent with the established fact that regional Au deposits are predominantly located in fault zones and contact zones between different geological bodies.