Multichannel seismic impedance inversion based on Attention U-Net

Abstract

Recently, seismic inversion has made extensive use of supervised learning methods. The traditional deep learning inversion network can utilize the temporal correlation in the vertical direction. Still, it does not consider the spatial correlation in the horizontal direction of seismic data. Each seismic trace is inverted independently, which leads to noise and large geological variations in seismic data, thus leading to lateral discontinuity. Given this, the proposed method uses the spatial correlation of the seismic data in the horizontal direction. 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, to enhance the lateral continuity and anti-noise performance. Additionally, Attention U-Net is introduced in acoustic impedance inversion. Attention U-Net adds attention gate (AG) model to the skip connection between the encoding and decoding layers of the U-Net network, which can give different weights to different features, so the model can focus on the features related to the inversion task and avoid the influence of irrelevant data and noise during the inversion process. 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.