Rapid Replication of Best Practices through Machine Learning. Never mind the network, look at the data!

We aim to demonstrate that machine learning (ML) can reduce turn-around substantially without sacrificing quality for seismic processing. We show this in the context of velocity model building based on Residual Moveout and focus on two specific elements: gather clean-up and RMO picking. We find that it is critical not only to curate well labelled data, but also to better condition the data as per learning problem requirements. While we optimized the supervised-learning ML networks for training, our results got a substantial boost through the label and data conditioning workflows developed for the problems. When applying on seismic data it was not trained on, the ML networks generalized well not only for other seismic data from the Gulf of Mexico and Brazil but also for deep seismic data from the Black Sea, Nigeria, and Oman. Applying the machine-learning networks reduced turn-around both for gather clean-up and for RMO picking from 1–2 weeks to less than a day. Less time is needed for tuning the workflow, testing parameters, laborious QC and climbing the learning curve. Execution on the compute is also faster. Turn-around for velocity model building was reduced both for a ray-based workflow and for a wave-equation-based workflow.