基于ASPP-UNet的地震波阻抗反演方法OA

Deep-learning-based seismic impedance inversion methods have received wide attention due to their ability to handle nonlinear mapping problems.The conventional deep-learning-based seismic impedance inver-sion methods have the problem of an overwhelming dependence on labeled data,which results in a decrease in the model's ability to extract local features and poor precision of inversion results when training data is insuffi-cient.To address these issues,a new atrous spatial pyramid pooling and U-Net(ASPP-UNet)based seismic im-pedance inversion method is proposed.The multi-scale feature extraction ability of U-Net is enhanced by the atrous spatial pyramid pooling operation.Based on this,the training datasets were constructed using seismic data and a small amount of logging data.To verify the effectiveness of the proposed method,we conducted two simu-lation experiments on the Marmousi2 and SEAM public datasets and compared the results with those of CNN,U-Net,and Attention-UNet under the same experimental conditions.The experimental results show that,under the same experimental conditions,the single-trace impedance inversion produced by the proposed method contains richer high-frequency details,and the inverted impedance profile displays smooth vertical continuity between layers and at fault locations.The inversion results also depend less on labeled data and exhibit the least informa-tion loss at positions far from the training wells,which is reflected in the strong lateral continuity between traces in the inverted impedance profile.Compared with the comparison methods,the ASPP-UNet inversion results show the best statistical indicators.To further validate the applicability of the ASPP-UNet method,it was applied to real seismic impedance inversion data from East Sichuan Province.The impedance profile obtained by ASPP-UNet is consistent with the actual geological structure.Compared with the three deep-learning methods men-tioned above,the inversion results have the highest accuracy,and the impedance profile error is the smallest.