Deep Learning for Seismic Imaging in the Presence of Velocity Errors

Seismic migration is a tool to obtain images of underground structures; however, it requires accurate velocity models. Errors in estimated migration velocities lead to defocused and distorted migration images. We propose a deep learning method for accurate seismic imaging in the presence of velocity errors. Our idea is to correct the distorted common image gathers (CIGs) due to velocity errors by using a convolutional neural network (CNN). We design a CIG-to-CIG (CIG2CIG) CNN, in which both the inputs and outputs are CIGs. Furthermore, we apply velocity constraints to the CIG2CIG CNN, forming another velocity-constrained CIG2CIG (VC-CIG2CIG) CNN to perform the same task. To train the two CNNs, we create hundreds of true and wrong velocity models, which are applied to migration to produce true CIGs and distorted CIGs, respectively. Experiments demonstrate that the VC-CIG2CIG network is superior to the CIG2CIG network in correcting distorted CIGs and suppressing artifacts.