Automatic velocity picking from semblances with a new deep-learning regression strategy: Comparison with a classification approach

Abstract

The physical basis, parameterization, and assumptions involved in root-mean-square (rms) velocity estimation have not significantly changed since they were first developed. However, these three aspects are all good targets for novel application of the recent emergence of machine learning (ML). Therefore, it is useful at this time to provide a tutorial overview of two state-of-the-art ML implementations; we have designed and evaluated classification and regression neural networks for the extraction of apparent rms velocity trajectories from semblance data. Both networks share a similar end-to-end trainable structure, except for the final layer. In the classification network, the velocity picking is performed by finding the largest amplitude trajectory through all velocity bins. The regression network, on the other hand, applies a differentiable soft-argmax function that converts the feature maps directly to apparent rms velocity values as functions of traveltime. Relative confidence maps can also be estimated from both neural networks. A large number of synthetic models with horizontal layers are created, and common-midpoint gathers are simulated from those models as training samples. Transfer learning is applied to fine-tune the networks with a small number of samples for testing with synthetic and field data from more complicated (2D) models. Tests using synthetic data show that the regression and classification networks can give reasonable velocity predictions from semblances, but the regression network gives higher accuracy.