Paper Summary
In seismic imaging, reducing the turnaround time of imaging projects is essential. Machine
learning solutions offer the potential of reduced turnaround time without the loss of data quality
that comes with traditional fast-track solutions. These are achieved through faster execution times and the elimination of parameter testing. Machine learning solutions have been used to reduce turnaround in the VMB (Velocity Model Building) steps (Crawley, 2024) and in the preprocessing steps (Brusova, 2021; Roberts, 2024). Here, we leverage machine learning (ML) to accelerate and improve the efficiency of preprocessing, focusing on three key steps: swell noise removal, deghosting, and designature. We demonstrate these results on data from a recent 3D project from the Niger Delta, offshore Nigeria.