Magnetic Data Edge Detection Method With Depth Information Based on UNet ++

Edge detection is a critical technology in processing potential field data, enabling the rapid identification of geological body edges using magnetic anomaly data. Traditional methods for detecting edges in magnetic data are known for their simplicity and efficiency; however, they suffer from low resolution, poor robustness, and a lack of depth information. In recent years, the application of deep learning (DL) to edge detection in field data has enhanced both the resolution and robustness of these methods. Nonetheless, these approaches still fail to determine the buried depths of geological body edges. To address this issue, this study has developed an edge detection method called multiconstraint DL based on UNet++, which not only identifies the edge positions but also ascertains the buried depths of geological bodies. The study proposes the multiconstraint loss function for edge detection and employs the Dice loss function and the mean square error (mse) loss function to jointly supervise the network’s training. Subsequently, the label design was revised to include depth information on the geological body, enabling the DL method to accurately determine the buried depths of geological bodies. Analysis of the test model’s detection results reveals that the edge detection method based on UNet++ can precisely identify both the edge positions and the exact buried depths of geological bodies, which makes up for the shortcoming that the traditional edge detection method does not contain the depth information. Moreover, this method resolves the issue of discontinuity found in traditional DL edge detection. Finally, the method was applied to actual aeromagnetic data from the Dandong area in Liaoning Province, successfully identifying the edge positions of the main mining area and providing targeted regions for further exploration.