## 3 Data Acquisition and Processing
### Processing and Interpretation  
The data preprocessing mainly included coordinate projection transformation, diurnal correction, lag correction, direction difference correction, International Geomagnetic Reference Field (IGRF 13 th) correction, and magnetic field leveling. The standard deviation of the magnetic field differences at the intersection points of the survey lines and tie lines after preprocessing was 2.5 nT. To eliminate the effects of magnetic inclination and declination on the aeromagnetic data, the aeromagnetic data were reduced to the pole using the magnetic inclination (47.72\({}^{\circ}\)) and declination (\(-\)0.25\({}^{\circ}\)) calculated based on the latitude, longitude, and elevation of the study area. The tilt angle was applied to the reduced-to-the-pole data. The tilt angle is a commonly used method for identifying the boundaries of magnetic bodies. The calculation formula for the tilt angle is  
\[Tilt=tan^{-1}\left(\frac{\partial f/\partial z}{\sqrt{\left(\partial f/ \partial x\right)^{2}+\left(\partial f/\partial y\right)^{2}}}\right) \tag{1}\]  
where _Tilt_ is the tilt angle, \(f\) is the magnetic field, and \(x\), \(y\), and \(z\) are the derivative directions. Zero-contour lines of the tilt angle are used to outline magnetic bodies (assuming there are no geological bodies with strong remanent magnetism within the area). The Euler deconvolution method is a widely employed inversion technique in geoscience, utilized for estimating the depth and location of source fields. This approach was first introduced by [PERSON], who utilized the Euler homogeneous function equation to invert source field information [22]. Subsequently, [PERSON] et al. expanded this method to invert source field information from gravity and magnetic grid data [23]. The three-dimensional Euler homogeneous function equation can be expressed as  
\[(x-x_{0})\frac{\partial f}{\partial x}+(y-y_{0})\frac{\partial f}{\partial y}+ (z-z_{0})\frac{\partial f}{\partial z}=SI(B-f) \tag{2}\]  
where \(f\) represents the measured magnetic field value at (\(x\), \(y\), \(z\)), (\(x_{0}\), \(y_{0}\), \(z_{0}\)) denotes the source location, \(B\) is the regional background field, and \(SI\) is the structural index. Given that the magnetic field value and measurement position are known, the partial derivatives of the magnetic field in three directions (\(\partial f/\partial x\), \(\partial f/\partial y\), \(\partial f/\partial z\)) can be calculated, representing the rate of change of the magnetic field in each direction. By specifying \(SI\), the solution set (\(x_{0}\), \(y_{0}\), \(z_{0}\)) and background field value B can be obtained by solving the Euler equation set. The Euler deconvolution method can effectively identify the boundaries of geological anomalies such as faults and steep contacts and estimate their depths. The standard Euler deconvolution involves moving a window of fixed size across the data grid and calculating the solutions for each window. Due to the window size being much smaller than the study area, a large number of solutions are produced, including many spurious solutions. To reduce the number of false solutions, one approach is to use the Euler deconvolution method based on the prior selection of the horizontal position of the magnetic source boundary (located Euler deconvolution). It first locates the positions containing peak data, then estimates the window size using the positions of adjacent peak points, and finally performs Euler deconvolution calculations using these points and windows, which removes a large number of non-peak position solutions, thereby focusing the solutions more on the area of interest. The choice of \(SI\) in Euler deconvolution has a significant impact on the solutions. We used the AN-EUL method to estimate the \(SI\) of the study area [24].