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In this paper we present machine-learning (ML) technology using convolutional neural networks (CNN) trained from various seismic volumes and their matching appropriate labels from the Gulf of Mexico. These CNNs, the ML brains, are used to predict the probable position of the water bottom, top of salt, and base of salt, all required inputs to create velocity models during Earth model building (EMB). The interpretation process to generate these input horizons has traditionally been time-consuming and labor-intensive, taking days to weeks with the standard available tools. The time savings gained from the interpretation of a single volume compounded over the life span of the project due to the iterative nature of seismic imaging and the continual generation of improved seismic images is considerable. We record a 60–80% turnaround time reduction of the seismic interpretation stages, compared to a manual approach that relied on gridded interpretations and the best available auto-tracking capabilities. In addition to the turnaround time reduction, the horizons are delivered at higher resolution with improved quality, benefiting from the interpreter’s ability to focus on the more geologically complex areas