## 6 Conclusions and Future Work
**(2) Computational Resource Requirements:** The processing and training of large-scale three-dimensional (3D) datasets significantly increase memory and computational demands. This limitation is particularly critical for real-time applications and high-resolution data inversion tasks. To overcome this, we propose adopting strategies such as distributed training, model parallelization, and computationally efficient architectures (e.g., lightweight CNN variants or transformer-based models). These optimizations aim to reduce computational costs while maintaining or improving model accuracy. **(3) Geological Assumptions and Complexity:** The training data utilized in this study assume relatively homogeneous and isotropic geological conditions. Such simplifications may not adequately represent heterogeneous or anisotropic subsurface environments, which are common in real-world settings. Future iterations of MagEMNet will integrate physics-informed priors and more sophisticated geological models to better capture these complexities. This will enhance the physical realism of training data and improve the reliability of inversion results. **(4) Model Interpretability and Generalization:** As MagEMNet is scaled to include more diverse datasets and geological conditions, maintaining model interpretability and avoiding overfitting become critical challenges. To address these issues, we plan to incorporate regularization techniques and domain adaptation methods to improve generalization. Additionally, attention-based explainability modules will be explored to provide insights into the decision-making process of the model, thereby enhancing its interpretability for practical applications. By addressing these challenges, MagEMNet can further evolve into a robust and versatile tool for joint geophysical inversion across a wider range of geological environments. These future directions will not only improve its computational efficiency and generalization capabilities but also extend its applicability to more complex and heterogeneous geological contexts, contributing to the broader field of geophysical exploration. The practical implications of MagEMNet are broad, spanning applications such as unexploded ordnance detection, subsurface resource exploration, and environmental monitoring. Its ability to detect both ferromagnetic and non-ferromagnetic metal targets, as well as derive accurate position, shape, and orientation information with sub-decimeter accuracy, confirms its versatility in field applications. Furthermore, the negligible prediction time required for subsurface anomaly characterization makes MagEMNet a viable tool for real-time exploration tasks. Future research directions include enhancing the generalization capabilities of MagEMNet by incorporating multi-scale data integration and domain adaptation techniques. Additionally, expanding the framework to include other geophysical modalities, such as gravity and seismic data, will enable a more comprehensive understanding of subsurface environments. Leveraging such multimodal approaches could further reduce solution ambiguity and improve the reliability of inversion results in complex geological settings. In summary, MagEMNet represents a significant step forward in geophysical inversion, offering a robust, efficient, and scalable solution for subsurface exploration. Its integration of advanced machine learning techniques with geophysical principles provides a powerful framework for addressing the challenges of subsurface characterization in diverse geological scenarios. **Author Contributions:** Conceptualization, Z.Q. and X.Z. ; methodology, Z.Q. ; validation, Z.Q., Y.G. and S.L. ; formal analysis, X.Z. ; investigation, Z.Q. and Y.G. ; resources, Z.Q., Y.G. and S.L. ; data curation, Z.Q. ; writing--original draft preparation, Z.Q. ; writing--review and editing, X.Z. ; visualization, Z.Q. ; supervision, S.L. ; project administration, X.Z. ; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.