Automatic event picking in prestack migrated gathers using a probabilistic neural network

Abstract

We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made, such as hyperbolic move-out. We train a probabilistic neural network for voxel classification using event times, subsurface points, and offsets (ground truth information) picked manually by expert interpreters. The key to success is using effective features that capture the important behavior of the measured signals. We test a variety of features calculated in a local neighborhood about the voxel under analysis. Selection algorithms ensure that we use only the features that maximize class separability. This event-picking algorithm has the potential to reduce significantly the cycle time and cost of 3-D prestack depth migration while making the velocity model inversion more robust.