Kernel Prediction Network for Offset Domain Common Image Gather Flattening and Correction

In prestack Kirchhoff depth migration, the quality of the migration profile is determined by how well common image gathers (CIGs) are flattened and corrected. The migration velocity analysis (MVA) method is proposed to flatten the offset domain CIGs (ODCIGs) by updating the migration velocities. However, conventional MVA such as the residual curvature analysis (RCA) method is typically challenging to complex structures such as lateral velocity variations or high-dip reflectors. In addition, there are structural artifacts in ODCIGs due to the multipath ray problem even if the migration velocity is accurate. To address the above problems, we developed a kernel prediction network (KPN) for ODCIG flattening and correction. Compared with conventional neural networks, the primary advantage of the KPN is that its outputs consist of a series of predicted kernels instead of pixel vectors or matrices. These predicted kernels are capable of processing the input ODCIGs pixel by pixel and slice by slice. The KPN is built by an encoder-decoder architecture, and we modified the loss function of the KPN and introduced an extra parameter associated with the migration offset to ensure that the network is more effective for the ODCIG problem.The training samples of the KPN are acquired by a random extraction algorithm, and the corresponding labels are calculated by a convolution method.Image enhancements are also applied in training samples to improve the generalization capability of the KPN. We demonstrate the effectiveness of the KPN method by comparing it with the RCA method in both synthetic and field data examples. The results show that the KPN method can flatten the events in ODCIGs, correct the depth of the improperly migrated reflectors, remove unfocused artifacts simultaneously, and further yield high-quality migration profiles in different geological examples.