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- W4285194487 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." @default.
- W4285194487 created "2022-07-14" @default.
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- W4285194487 date "2022-01-01" @default.
- W4285194487 modified "2023-09-25" @default.
- W4285194487 title "A Benchmark Study of Deep Learning Methods for Seismic Interpolation" @default.
- W4285194487 doi "https://doi.org/10.3997/2214-4609.202210725" @default.
- W4285194487 hasPublicationYear "2022" @default.
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