## 4 Results and Discussion
### Construction of a Quantitative Optimization Model for Prospecting Target Areas
In model II, although the contribution of the \(\Delta\)Td variable is not as significant as Au, it also exceeds 5% and holds a certain weight in the model; in model III, the contribution of the \(\Delta\)Tx variable is much higher than that of Au, also exceeding 5%, and it holds a certain weight in the model. In model I, the individual contribution of the \(\Delta\)Tx and \(\Delta\)Td aeromagnetic data variables to the optimal model is significantly greater than that of the Au element. This outcome provides definitive evidence that when aeromagnetic data are transformed into a format suitable for use with geochemical data to construct an optimal model for regional Au prospecting target areas, aeromagnetic data exhibit excellent discriminant ability. This result not only calls into question the traditional geological research conclusion that aeromagnetic results are ineffective in determining Au mineral prospecting targets, but also corroborates the assertion that geophysical information is not influenced by sampling representativeness and is a more reliable and accurate measurement than geochemical information. In conclusion, this evidence demonstrates the potential for significant insights to be gained from extensive datasets. From this, it becomes clearer that there is infinite potential in massive data information, which can only be fully explored by changing the way we think, based on the concept of big data and the method of quantitative research. Results of Quantitative Target Selection Based on Geophysical and Geochemical Prospecting Information for Au Mining Region  
Preferred models (discriminant functions) I, II, and III, as presented in Table 2, were utilized to process the \"Au mineralization prospecting information specification unit modeling database (24,824 \(\times\) 42)\" within the designated study area. Subsequently, the discriminant values (R1, R2, and R3) for each model were calculated for each research unit. Identify the inverse value of each discriminant value, calculate the sum of the discriminant values for each ore-bearing unit, and determine the RZ value (RZ represents the comprehensive discriminant value derived from a series of preferred models for each ore-bearing unit, and its magnitude directly reflects the reliability of prospecting for ore-bearing units). The delineation of the preferred ore-bearing regions is retained, while the low and isolated areas are excluded. The largest comprehensive discriminant value in each concentration area and its location are selected to represent the area. The maximum RZ values in each concentration area are sorted in descending order, and the top 300 preferred ore-bearing unit concentration areas are selected as the preferred ore target areas for the Au ore area in the Beishan area based on the geophysical and geochemical information. The 300 prospecting target areas are classified into three categories according to the \"golden section rule\". The first category, level I, comprises 57 areas, including 11 known ore-bearing target areas, with a mineralization rate of 19.29% in level-I target areas. The 93 level-II Au prospecting target areas, including the 11 mineralized target areas, exhibit a mineralization rate of 11.87% in target areas. A total of 150 level-III Au prospecting target areas were identified, of which 5 mineralized target areas were confirmed to contain ore, yielding a target area ore-finding rate of 3.33%. The ore-finding rate of each level of the target area is considerably higher than the average ore-finding rate of the anomaly unit. It is therefore postulated that the quantitative optimal model for prospecting Au in the anomaly unit is highly accurate for determining and optimizing the Au prospecting target areas in this region (Table 6). The proportion of the area of each level of preferred target area is calculated. The aggregate area of the 57 level-I prospecting target areas is 838.75 km\({}^{2}\), with the smallest area measuring 4.05 km\({}^{2}\) and the largest area measuring 29.37 km\({}^{2}\).