Efficient Angle Stack Conditioning Using a Convolutional Neural Network

Summary We present a workflow to condition seismic angle stack data using a convolutional neural network. First, 2-D synthetic CDP gathers are generated with a convolutional modelling method, which utilizes randomized reflectivities, wavelets, and North Sea-inspired velocity trends. From each generated gather we form an input/target pair: the target is moveout corrected with the accurate velocity trend, while the input is moveout corrected with a perturbed, inaccurate version of this trend. The result is bending, distorted reflections for the input gathers, and flattened, less-distorted reflections for the targets. These gathers are muted and stacked to form near, mid, and far angle stacks. A 1-D CNN is trained on the pairs of triplet angle stack traces, to transform the misaligned and distorted inputs into improved outputs. The synthetic-trained CNN is then tested on North Sea field data. This dataset includes traditionally-conditioned angle stacks (RMO and trim statics correction), and well data allowing synthetic seismogram generation, showcasing a Class 3 AVO response. Applying the CNN results in alignment improvement comparable to the traditional conditioning, while retaining AVO responses and becoming more similar in character to the well synthetic seismogram. Additionally, the CNN-conditioned far stack section increases in resolution compared to the original data.