Automatic conditioning of marine seismic angle stack data with a convolutional neural network

Abstract Misalignment of reflections is a common problem in migrated prestack seismic data, which occurs due to moveout correction with incomplete knowledge of the velocity model. This issue persists in partial angle stack data often used for amplitude-variation-with offset (AVO) analyses and is traditionally treated with residual moveout (RMO) and trim static corrections. However, these methods require time-consuming velocity selection and parameter tuning and commonly lead to spurious alignment of reflections with noise. Therefore, we train a convolutional neural network (CNN) on synthetic examples to automatically perform conditioning and alignment of angle stack data. First, 2D synthetic common-depth point gathers are created using a convolutional modeling method and then made into input-target pairs, where the inputs are poorly moveout corrected (using inaccurate normal-moveout velocities) and the targets are accurately moveout corrected. These input-target examples are split into angle stacks for the near, mid, and far offsets and then used to train a CNN to transform the misaligned angle stacks into their more-aligned counterparts. In testing, the trained network increases the alignment of unseen synthetic data, improving the correlation with the target far-offset trace from 0.76 to 0.85 on average. The network is applied on two field examples from offshore Norway with class 3 and class 2P AVO responses, respectively. In both cases, the angle stack data predicted by the network indicate improved alignment, greater resolution in the far-offset stacked section, and increased correspondence to a well-tie synthetic seismogram, all while retaining the AVO responses. The CNN predictions are compared with angle stack data after traditional conditioning (RMO and trim static corrections), with the results being of similar or greater quality. This method enables high-quality conditioning of angle stack data at a fraction of the time required for conventional methods and is easily adaptable to different data sets.