Pole Transformation of Magnetic Data Using CNN-Based Deep Learning Models
Zhuo Jia, Meijia Huang, Xu Hong, Wei Du, Yabin Li · IEEE Transactions on Geoscience and Remote Sensing · 2025
Magnetic anomaly pole transformation converts magnetic field data into an equivalent response at the true magnetic pole, eliminating shifts and distortions, simplifying the interpretation of subsurface magnetic bodies, and improving data interpretation and inversion accuracy. However, the main challenge in magnetic anomaly pole transformation lies in the nonlinear nature of the signals, making traditional methods difficult to apply. The interaction between the shape, depth, and magnetic inclination of magnetic bodies, especially in high- and low-latitude regions, can distort the transformed signal, leading to unclear causal relationships. To address this, this article proposes a deep learning-based approach that automatically extracts high-dimensional features and establishes nonlinear mappings to enhance the correlation between magnetic anomaly signals and geological structures. Deep learning does not require explicit physical models and, through training with large datasets, demonstrates stronger robustness and accuracy, especially in areas where traditional methods fail. The proposed method is validated using both synthetic and measured data. Synthetic data simulates magnetic bodies of various shapes, depths, and magnetic inclinations, confirming the method’s stability and accuracy in handling complex nonlinear signals. The measured data evaluates its pole transformation advantages in typical ore deposit regions. The results indicate that the deep learning model significantly enhances the accuracy of pole transformation, particularly in areas with complex magnetic anomaly signals, effectively preventing signal distortion and demonstrating exceptional generalization capabilities.