Pre‐migration diffraction separation using generative adversarial networks

Abstract

Diffraction imaging is the process of separating diffraction events from the seismic wavefield and imaging them independently, highlighting subsurface discontinuities. While there are many analytic‐based methods for diffraction imaging which use kinematic, dynamic or both, properties of the diffracted wavefield, they can be slow and require parameterization. Here, we propose an image‐to‐image generative adversarial network to automatically separate diffraction events on pre‐migrated seismic data in a fraction of the time of conventional methods. To train the generative adversarial network, plane‐wave destruction was applied to a range of synthetic and real images from field data to create training data. These training data were screened and any areas where the plane‐wave destruction did not perform well, such as synclines and areas of complex dip, were removed to prevent bias in the neural network. A total of 14,132 screened images were used to train the final generative adversarial network. The trained network has been applied across several geologically distinct field datasets, including a 3D example. Here, generative adversarial network separation is shown to be comparable to a benchmark separation created with plane‐wave destruction, and up to 12 times faster. This demonstrates the clear potential in generative adversarial networks for fast and accurate diffraction separation.

Diffraction separation and imaging using ensemble empirical mode decomposition and multichannel singular spectrum analysis

“…Lowney, Lokmer, O'Brien, Bean et al. (2021) trained the generative adversarial network (GAN) and showed that GAN separation is comparable to a benchmark separation created with PWD and performs up to 12 times faster. Lastly, Bauer et al.…”

“…Schwarz (2019) used wavefront filters for diffraction separation via the coherent summation and subtraction of seismic data. Lowney, Lokmer, O'Brien, Bean et al (2021) trained the generative adversarial network (GAN) and showed that GAN separation is comparable to a benchmark separation created with PWD and performs up to 12 times faster. Lastly, Bauer et al (2021) trained a deep convolutional neural network to decompose the wavefield of any input seismic or ground-penetrating-radar data into reflection, diffraction and noise and reported promising results.…”