## 3 Materials and Methods
### Synthetic Dataset Generation  
To effectively train the deep learning-based inversion model, a comprehensive synthetic dataset was generated using a forward modeling approach based on a three-dimensional orthogonal magnetic dipole model. These synthetic data simulate the electromagnetic and magnetic field responses of various subsurface targets, thereby allowing the network to learn diverse scenarios and achieve generalization to complex geological environments. To avoid the issue of non-uniqueness in the inversion process--where different parameter combinations could yield similar responses--the range of target parameters was carefully constrained in accordance with the specific experimental setup. Table 2 illustrates the predefined ranges for each parameter, including spatial coordinates \((x,y,z)\), magnetic polarizability values \((L_{1},L_{2},L_{3})\), and orientation angles \((\alpha,\beta)\). The data acquisition was conducted over an observation plane positioned above the target area, as depicted in Figure 4. This plane contains 49 measurement points arranged in a grid format, with a 0.5 m spacing between each point, covering a total area of 3.5 m \(*\) 6.5 m. The target parameters were generated based on systematic rules, allowing variations in spatial location, orientation, and polarizability. For each parameter set, the forward model simulated both the electromagnetic response and the magnetic field response at the observation points. To improve the model's robustness, a noise level of 5% was added to each simulated magnetic anomaly response, rather than the total geomagnetic field,ensuring that the introduced perturbation accurately represents realistic measurement uncertainties encountered in practical surveys. This approach enhances the generalization capability of the inversion network. The synthetic dataset comprises a diverse array of scenarios by systematically varying the target's spatial position, orientation angles \((\alpha,\beta)\), and magnetic polarizability components \((L_{1},L_{2},L_{3})\). This approach produced realistic time-domain electromagnetic (TEM) and magnetic survey data. The TEM data captures the transient secondary magnetic fields, whereas the magnetic data record the static magnetic field distribution. This synthetic dataset is intended to serve as an extensive training set, providing the inversion model with a broad spectrum of input-output relationships that are essential for accurate parameter recovery in real-world applications. A total of 100,000 data samples were generated using the magnetic dipole model, and part of the dataset is shown in Figure 5. It is important to note that the magnetic anomaly data in this study represent only the field perturbation generated by the target itself, excluding the Earth's background magnetic field. This ensures that the dataset focuses solely on the localized anomaly caused by the target, rather than the absolute geomagnetic field intensity. Each data sample includes the true target parameters \(\mathbf{p}\), the electromagnetic anomaly response vector \(\mathbf{Y}_{\text{EM}}\), and the magnetic anomaly response vector \(\mathbf{Y}_{\text{Mag}}\). Both \(\mathbf{Y}_{\text{EM}}\) and \(\mathbf{Y}_{\text{Mag}}\) are represented as \(1\times 49\) vectors corresponding to the measurement points on the observation plane, while \(\mathbf{p}\) contains the parameters \([x,y,z,L_{1},L_{2},L_{3},\alpha,\beta]\). Since the target is assumed to be axially symmetric, only two orientation parameters, inclination \(\alpha\) and declination \(\beta\), are required to describe its orientation, rather than a full three-axis