A Magnetic Anomaly Inversion Method Integrating Convolutional Block Attention Module and Physical Consistency Constraints

Magnetic anomaly inversion plays a vital role in geological exploration and subsurface structure identification; however, its inherent nonlinearity and non-uniqueness remain significant challenges. To improve inversion accuracy and model interpretability, this study proposes a magnetic anomaly inversion method that integrates the Convolutional Block Attention Module (CBAM) with physical consistency constraints. Built upon a convolutional neural network architecture, the method incorporates the CBAM module to enhance the network's attention to critical channels and spatial regions, thereby improving boundary delineation and structural reconstruction. Simultaneously, a physical consistency term based on the forward modeling kernel matrix is embedded into the mean squared error loss function to enforce conformity between the predicted results and physical laws. Extensive inversion experiments using both synthetic and field data from mining areas demonstrate that the proposed method outperforms conventional CNN models in terms of anomaly localization, morphology reconstruction, and magnetization parameter estimation. The results highlight the method's superior accuracy and stability, offering an efficient and reliable new approach to magnetic anomaly inversion.