The advent of deep learning techniques had a huge impact in the geophysical community. Convolutional Neural Networks have been investigated for interpretation tasks and, lately, for seismic imaging problems traditionally approached with analytical methods. In this manuscript we employ a state-of-the-art CycleGAN for processing migrated images. We show an application scenario in which this post-processing machine transforms migrated images into corresponding reflectivity model of the subsurface. The proposed methodology achieves promising results in both synthetic and field data. The reconstructed reflectivity model exhibits a meaningful topology. However, the results suggest that computer-vision techniques need to be adapted to tackle the complexity of seismic imaging problems. For this purpose, we believe that geophysical knowledge can be embedded into more accurate network design.
A tool for processing seismic images: Study on CycleGAN
Lipari V.;
2020-01-01
Abstract
The advent of deep learning techniques had a huge impact in the geophysical community. Convolutional Neural Networks have been investigated for interpretation tasks and, lately, for seismic imaging problems traditionally approached with analytical methods. In this manuscript we employ a state-of-the-art CycleGAN for processing migrated images. We show an application scenario in which this post-processing machine transforms migrated images into corresponding reflectivity model of the subsurface. The proposed methodology achieves promising results in both synthetic and field data. The reconstructed reflectivity model exhibits a meaningful topology. However, the results suggest that computer-vision techniques need to be adapted to tackle the complexity of seismic imaging problems. For this purpose, we believe that geophysical knowledge can be embedded into more accurate network design.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
