## 2 Study Area and Data
### Mine Big Data
#### 2.2.2 Surface Sample Data--XRF, Spectroscopy, Susceptibility, Sampling  
In this study, three phases of sample collection were conducted in April 2023, August 2023, and May 2024. In the first phase, a total of 39 samples were collected, distributed across the F4 fault zone, the northeastern and western mid-segments, and the most recent mining areas. The second phase involved the collection of 337 samples, primarily located in the newly mined areas of the mining district. In the third phase, 23 samples were collected, mainly from the ore body, ore-rock mixed zones, and surrounding rocks. The spatial distribution and frequency distribution of the samples for all three time periods are shown in Figure 3. Overall, the samples from all three stages were distributed around and near the ore body, with generally low ore grades. The iron grade is typically between 10% and 20%.  
Subsequently, these samples were tested using shortwave infrared spectroscopy (SWIR), thermal infrared spectroscopy (TIR), and X-ray fluorescence (XRF), obtaining spectra for the samples.  
The shortwave infrared reflectance spectra were obtained using the OreXpress(tm) device produced by Spectral Evolution (Haverhill, MA, USA). The instrument has an effective testing wavelength range of 350 to 2500 nm and provides a good signal-to-noise ratio. The preparation of spectral samples requires the surface to be clean and dry, and each spectrum was acquired 3-5 times under the same conditions. The spectrometer features a sampling bandwidth of 1 nm and a minimum scanning speed of 100 ms. During sample testing with the contact probe of this device, the proprietary DARWin SP 1.4 was used for spectral recording. This software includes spectral libraries from the USGS and the SpecMIN mineral library, enabling spectral matching of samples and real-time mineral identification [51]. A total of 395 SWIR spectra from rock mineral samples were obtained for this study. Mineral spectral identification was conducted using the TSA algorithm in TSG 8.0, which matched the shape of the spectral curves and the positions of absorption peaks with those in the spectral library to determine mineral types and extract spectral information. The TSA algorithm generates a report explaining the spectral information, typically including individual minerals or mineral combinations, as well as spectral weight or estimated spectral abundance of minerals [52].  
The thermal infrared reflectance spectra were determined using the Agilent 4300 handheld Fourier transform infrared spectrometer, designed and manufactured by Agilent Technologies (Santa Clara, CA, USA). This instrument weighs 2.2 kg and is equipped with a diffuse reflectance sampling module, allowing measurements over a wavelength range of 4000 to 650 cm\({}^{-1}\) (approximately 2500 to 16,000 nm) with a spectral resolution range of 4 to 16 cm\({}^{-1}\). The instrument includes MicroLab 5.7 for the storage and transmission of data, methods, and spectral libraries, and a total of 134 TIR spectral data from rock mineral samples were obtained. Since TSG 8.0 contains over 500 mineral spectra ranging from VIR to TIR, the TSA algorithm was subsequently used for mineral identification [53].  
We used a portable XRF-OX44 manufactured by Fluka (Buchs, St. Gallen, Switzerland) to scan rock and mineral samples, with each sample scanned 3-5 times for 60 s per scan. After obtaining data on the major and trace element content data for 395 samples and excluding elements with a detection rate of less than 20%, we identified 23 key elements related to the mineralization of the deposit. The Self-Organizing Map algorithm was used to explore the characteristics and correlations of each element's content, thereby identifying representative indicator elements for mineral exploration [54].