Deep Learning for 3-D Magnetic Inversion

The difficulty of 3D magnetic inversion is to use 2D magnetic anomaly data to obtain 3D magnetic susceptibility structure. The contribution of the underground medium to the magnetic anomaly decreases rapidly with the increase of the depth, which leads to the rapid attenuation of the inversion resolution with the depth. In this paper, artificial intelligence (AI) technology is applied to 3D magnetic inversion to predict the susceptibility model corresponding to magnetic anomaly. The inversion network built in this paper uses the method of down-sampling in the encoder to increase the receptive field and realize the feature extraction of magnetic anomaly data. In the decoder, attention fusion modules are added to fuse feature maps from different sources. Finally, we added a 3D refiner behind the decoder. The 3D refiner converts the 2D feature map from the decoder into 3D data. Based on the typical complex medium theory, this paper constructs a diverse sample set of complex 3D susceptibility models. The inversion experiment of synthetic data verifies the feasibility and versatility of the proposed network. Compared with the other methods, the distribution of susceptibility prediction obtained by our method is more accurate and more reliable in determining the magnetic body boundary. In the field example of Jinchuan Copper-nickel sulfide deposit in China, the network constructed in this paper can achieve high-precision 3D underground susceptibility imaging in this area. The susceptibility distribution is in good agreement with the borehole data and the proved deposit distribution.