Multichannel seismic impedance inversion based on Attention U-Net - Ning, Juan; Li, Shu; Wei, Zong; Yang, Xi (Frontiers in Earth Science, 2023)
Recently, supervised learning algorithms have been widely used for seismic inversion. The traditional deep learning inversion network can utilize the temporal correlation in vertical direction, but it does not consider the spatial correlation in horizontal direction of seismic data, and each seismic trace is inverted independently, which leads to noise and large geological variations in seismic data, thus leading to lateral discontinuity. In view of this, the spatial correlation of the seismic data in the horizontal direction is utilized in the proposed method. In the network training stage, several seismic traces centered on the well-side trace and the corresponding logging curve form a set of training sample pairs for training, so as to enhance the lateral continuity and anti-noise performance. In addition, to further improve the prediction accuracy and sensitivity of the inversion network, reduce the computational overhead, and make the inversion process avoid the influence of irrelevant data and noise, Attention U-Net is introduced into the acoustic impedance inversion in this paper. The performance of the proposed method is evaluated using the Marmousi2 model and the SEAM model, and compared with other methods. The experimental results show that the proposed method has the advantages of high accuracy of acoustic impedance value inversion, good transverse continuity of inversion results, and strong anti-noise performance.