LAUSR

The definition of reliable velocity functions is paramount for obtaining high-quality poststack seismic data. Velocity functions are commonly created with the interpreter interactively selecting high-energy peaks in velocity spectra and verifying if the derived velocity functions match the traveltime trajectories in the corresponding common midpoint (CMP) gathers. Modern software further allows the interpreter to apply resulting moveout corrections and verify if the desired overall flatness of reflection events is achieved. This very detailed process takes a significant amount of time, not necessarily delivering the globally optimal velocity function, and ultimately impacting the cost and duration of a typical seismic interpretation procedure. In this work, we present a hybrid regression approach based on convolutional neural networks (CNN) to speed up the velocity analysis workflow. The proposed methodology consists of an automatic initial velocity function estimation followed by a supervised refinement process that requires only a handful of gathers to be manually picked. Experiments performed on five field data sets show that the proposed methodology can not only produce human-grade velocity pickings but also outperform the interpreter in some cases, in a fraction of the time taken by the human counterpart.