## 3 Methodology
### Raw Data Processing
#### 3.2.2 Information Extraction  
The process of extracting mineral information is as follows: A total of 115 Au deposits (points) were identified in the selected area based on the aforementioned mineral geological data. The gold mineralization systems are dominantly classified into quartz vein-type, structural altered rock-type, and magmatic-hydrothermal-type deposits. These gold deposits are typically controlled by ductile shear zones and fault systems, with mineralization closely associated with Paleozoic magmatic activities and regional metamorphic-hydrothermal processes. Of these, 109 Au deposits (points) are located within the 1:200,000 stream sediment survey area. This study extracts 20-dimensional information on these deposits, including their location, stratum, structure, magmatic rock characteristics, ore body characteristics, ore-bearing structure (rock), resource quantity, and data source for each deposit (point). This allows basic geology to be utilized by a computer. The deposits, mineralization locations, stratigraphy, structural features, magmatic rocks, ore body characteristics, ore-bearing structures (rocks), resources (reserves), data sources, and other 20-dimensional information were extracted and subjected to mathematical statistical analysis using a computer.  
The extraction of geochemical information is as follows: The geochemical data from the 1:200,000 water system sediment survey in the Beishan area underwent processing, resulting in the formation of 24,715 units (samples) through gridding at a 2 km \(\times\) 2 km resolution. The background upper limit, lower limit, background value, median (mean) value, background difference rate, standard deviation, variation coefficient, skewness, kurtosis, upper limit difference rate, lower limit rate of change, goodness of fit, number of iterations, number of samples, and overall maximum and minimum values were extracted. The values, arithmetic mean, standard deviation, coefficient of variation, number of samples, and other statistical parameters were calculated for each geochemical parameter. A total of 33 geochemical parameters were considered. A stepwise regression simulation model was constructed for the Au element and other related elements. The fitted theoretical value Auh of Au was calculated to enhance the potential value of geochemical prospecting information in the quantitative and optimal research of regional prospecting target areas.  
Extraction of aeromagnetic information: The aeromagnetic survey data, collected during various periods, were leveled and processed. Data points that intersected and overlapped with different datasets were cropped, adhering to the principle of prioritizing the dataset with the largest scale. This resulted in 2,512,247 effective physical measurement points.  
Integration of geophysical and geochemical exploration data: To facilitate the simultaneous quantitative processing of geophysical and geochemical exploration data within the study area, the maximum (\(\Delta\)Td) and minimum (\(\Delta\)Tx) values of the aeromagnetic data, after adjustment, were selected as the new aeromagnetic parameters. These parameters were positioned at the center of the coordinate points of the chemical exploration data and extended 2 km on each side. This selection takes into account both positive and negative aeromagnetic anomalies, as well as the characteristics of the aeromagnetic gradient zone. After the sorting process, a \"Basic database of Au mineral exploration information (24,715 \(\times\) 42)\" was created for the study area, consisting of 24,715 samples and 42 variables (39 measured values of chemical elements, 1 fitted value of Au element Auh, and 2 aeromagnetic parameters).