Abstract

In seismic data processing, static corrections for near-surface velocities are derived from first-break picking. The quality of the static corrections is paramount to developing an accurate shallow velocity model, a model that in turn greatly impacts the subsequent seismic processing steps. Because even small errors in first-break picking can greatly impact the seismic velocity model building, it is necessary to pick high-quality traveltimes. Whereas various artificial intelligence-based methods have been proposed to automate the process for data with medium to high signal-to-noise ratio (S/N), these methods are not applicable to low-S/N data, which still require intensive labor from skilled operators. We successfully replace 160 hours of skilled human work with 10 hours of processing by a single NVIDIA Quadro P6000 graphical processing unit by reducing the number of human picks from the usual 5%–10% to 0.19% of available gathers. High-quality inferred picks are generated by convolutional neural network-based machine learning trained from the human picks.

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