Gridding and filtering method of gravity and magnetic data based on self-attention deep learning

The gridding and filtering of gravity and magnetic data directly influence the result of data processing. This paper designs a more rational deep learning model to improve the accuracy of gridding and filtering. The gridding method based on self-attention deep learning is constructed, and the self-attention mechanism layer is utilized to process the two-dimensional position embeddings. In this way, the position embeddings vector is obtained with global and local information integrated. Then the position information and anomaly information are fused to output node anomaly, thus reducing the distortion. For the random and stripe noises of gravity and magnetic data, a convolutional neural network is first employed to classify noise. The stripe noise is filtered by self-attention convolutional neural network and the random noise by convolutional autoencoder to get high-quality basic data. Model experiment shows that the gridded structure of deep learning is closer to the real result than that of the traditional method. The proposed filtering method can remove various noises, providing more accurate basic data for the follo-wing inversion. The gridding and filtering method based on deep learning applied to practical magne-tic data achieves good results, proving that it has strong feasibility and practicability. © 2022, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.