The new challenges of geophysical imaging applications ask for new methodologies going beyond the standard and well es- tablished techniques. In this work we propose a novel tool for seismic imaging applications based on recent advances in deep neural networks. Specifically, we use a generative adversarial network (GAN) to process seismic migrated images in order to potentially obtain different kinds of outputs depending on the application target at training stage. We demonstrate the promising features of this tool through a couple of synthetic examples. In the first example, the GAN is trained to turn a low-quality migrated image into a high-quality one, as if the acquisition geometry were much more dense than in the input. In the second example, the GAN is trained to turn a migrated image into the respective deconvolved reflectivity image.
A generative adversarial network for seismic imaging applications
Lipari V.;
Abstract
The new challenges of geophysical imaging applications ask for new methodologies going beyond the standard and well es- tablished techniques. In this work we propose a novel tool for seismic imaging applications based on recent advances in deep neural networks. Specifically, we use a generative adversarial network (GAN) to process seismic migrated images in order to potentially obtain different kinds of outputs depending on the application target at training stage. We demonstrate the promising features of this tool through a couple of synthetic examples. In the first example, the GAN is trained to turn a low-quality migrated image into a high-quality one, as if the acquisition geometry were much more dense than in the input. In the second example, the GAN is trained to turn a migrated image into the respective deconvolved reflectivity image.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
