## 6 Conclusions and Future Work  
This study introduced MagEMNet, a novel convolutional neural network (CNN)-based framework tailored for the joint inversion of electromagnetic (EM) and magnetic data. Addressing the inherent limitations of traditional single-modality geophysical inversion methods, MagEMNet effectively integrates the complementary strengths of EM and magnetic field responses, enabling precise and robust subsurface exploration while maintaining computational efficiency. The model's architecture incorporates advanced deep learning features, including Squeeze-and-Excitation (SE) blocks and a physics-constrained loss function, enhancing its capacity to capture complex spatial relationships and adhere to the underlying physical principles governing geophysical processes. The synthetic dataset generation and forward modeling processes were meticulously designed using a unified three-dimensional orthogonal magnetic dipole model. This ensured a consistent representation of subsurface properties, providing a strong foundation for model training. The effectiveness of MagEMNet was validated through comprehensive synthetic and field experiments, demonstrating its superior performance in accurately predicting key target parameters such as spatial position, dipole orientation, and magnetic polarizabilities. Quantitative assessments confirmed high predictive accuracy, with a coefficient of determination (\(R^{2}\)) exceeding 0.92 and significantly reduced error metrics compared to traditional methods like DNN, LM, and BAD. Field experiments further validated the model's practical applicability, showing its ability to resolve target parameters with depth errors of less than 10 cm and delineate target characteristics with remarkable precision. MagEMNet also showcases the capability of deep learning to overcome the non-uniqueness problem prevalent in traditional inversion methods. Unlike conventional techniques, which often require extensive prior knowledge and are susceptible to local minima, MagEMNet leverages data-driven training to achieve superior inversion results. Moreover, the introduction of a physics-constrained loss function imbues the model with physical interpretability, enhancing its robustness and preventing over-reliance on data-driven learning alone. These advancements underscore the framework's potential to address the challenges posed by the complexity and heterogeneity of subsurface targets. While MagEMNet demonstrates significant advancements in joint inversion of electromagnetic (EM) and magnetic data, several challenges remain that need to be addressed to enhance its applicability in more complex and diverse geological scenarios. These challenges and the corresponding proposed solutions are outlined below. **(1) Data Complexity and Dataset Size:** The performance of MagEMNet is highly dependent on the quality and representativeness of the training datasets. For larger or geologically complex datasets, capturing the required diversity to model subsurface heterogeneities can be a considerable challenge. To address this, future research will focus on generating synthetic datasets using advanced forward modeling techniques capable of sim \begin{table}
\begin{tabular}{l c c c c} \hline
**Sample 1** & **Target 1** & **MagEMNet** & **Target 2** & **MagEMNet** \\ \hline \(x\) (m) & 4.49 & 4.41 & 3.32 & 3.27 \\ \(y\) (m) & 7.19 & 7.23 & 3.71 & 3.76 \\ z (m) & \(-\)0.80 & \(-\)0.73 & \(-\)0.40 & \(-\)0.48 \\ \(L_{1}\) & - & 7.84 & - & 3.36 \\ \(L_{2}\) & - & 6.35 & - & 4.75 \\ \(L_{3}\) & - & 46.37 & - & 27.53 \\ \(\alpha\) (\({}^{\circ}\)) & 5.00 & 3.36 & 90.00 & 98.64 \\ \(\beta\) (\({}^{\circ}\)) & 75.00 & 70.63 & 55.00 & 47.89 \\ Time (s) & - & 0.0012 & - & 0.0009 \\ \hline \end{tabular}
\end{table} 
Table 4: Inversion results of measured sample. ulating complex geological features and various noise levels. Additionally, collaborations with research institutions to collect diverse real-world datasets will be pursued to ensure broader generalization.