SDCNet: Stable Downward Continuation of Magnetic Anomalies by Depth Information Fusion

Downward continuation is an effective technique that enhances anomaly resolution and details. However, high-frequency noise is amplified with increasing continuation depth, which adversely affects data interpretation. A novel stable downward continuation network (SDCNet) model has been proposed to enhance the noise robustness and stability of downward continuation. By incorporating fully convolutional networks (FCN) and Transformer to introduce topographic features and depth information, the model achieves stable downward continuation of magnetic anomaly from a surface to arbitrary target plane or surface, which improves the flexibility of existing intelligent continuation methods. Tests on the synthetic model demonstrate that the proposed method maintains continuation stability while exhibiting noise robustness compared to conventional methods. For noisy data, the maximum reductions in relative error (RE) and root mean square error (RMSE) reach up to 78%. Real data application over the Decorah complex in northeastern Iowa, U. S. shows that the continuation results align well with the distribution of the rock mass, further validating the effectiveness and practicality of the SDCNet in downward continuation.