Seismic images are often difficult to interpret when they are poorly focused due to velocity error. While velocity model building approaches can be used to correct for the velocity error and remigrate the image, they can require significant effort and computational resources. We present a novel and computationally-efficient approach to seismic image refocusing that relies on prestack Stolt residual depth migration and a deep convolutional neural network (CNN). Through the application of prestack Stolt residual migration to a poorly focused image, we generate many residually focused images which are then provided as input to a CNN that predicts the optimally focused regions of these images. With these optimally selected regions, a refocused image can be reconstructed that will be better suited for tasks such as automatic fault detection. We apply our refocusing approach to an unfocused image from the Gulf of Mexico and improve the intersection over union of segmented faults from 0.86 to 0.89.

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