Acquisition & imaging artifacts in raw seismic images can result in exploration risks and inaccurate rock property predictions. In this work, 3D partialâ€“full image pairs were used to train 3D convolutional neural networks (CNN) to simulate the stacking operation on a single 3D image. Compared to full images, partial images are overwhelmed by noise and poor illumination, so the learned mapping/function from partial to full images attenuates migration artifacts and partially compensates for fold/illuminated-related amplitude distortions. The trained model can be applied to either pre-stack or post-stack 3D image volumes to better reconstruct geological structures and simplify interpretation.
Presentation Date: Monday, September 16, 2019
Session Start Time: 1:50 PM
Presentation Time: 3:30 PM
Presentation Type: Oral