A Benchmark Study of Deep Learning Methods for Seismic Interpolation

Abstract

Summary Three distinctive deep learning algorithms have shown successful applications in the seismic interpolation task. The first, deep prior interpolation (DPI), trains a convolutional neural network (CNN) to map random noise to a complete seismic image using only the decimated image itself. The second, referred to as standard method, trains a CNN to map a decimated seismic image into a complete one using a dataset of both complete and artificially decimated images. The third is a generative adversarial network (GAN) that trains two CNNs; one generator and one discriminator, again by using a training dataset of complete and decimated images. Within this research, we compare the performance of these methods for different quantities of regular and irregular missing traces using 4 datasets. For the completeness of our benchmark study, we compare the methods with simple linear interpolation as a lower quality bound. We evaluate the results using 5 well-known metrics. Our research reports that overall the standard method performs better than the other approaches. The DPI method is competitive for a low level of regular decimation, and ranked second in the irregular cases. The GAN approach is the less effective of the three deep learning methods.