Seismic data interpolation with Generative Adversarial Networks

TitleSeismic data interpolation with Generative Adversarial Networks
Publication TypeSINBAD Presentation
AuthorsSiahkoohi, A, Felix J. Herrmann
PublisherSINBAD
Year of Publication2017
Abstract

In this project we implement an algorithm to predict the missing traces in the seismic shot gathers. The missing traces can be either regular or irregular. Any interpolation scheme assumes a prior knowledge on the data. Here the prior information used to interpolate the data is obtained from interaction of two trained deep neural networks, namely Generator and Discriminator. The combination of these two neural networks is called Generative Adversarial Network (GAN). GAN is trained on finely sampled seismic shot gathers. By employing the trained GAN we can project shot gathers with missing traces into the domain of the generator network. Then by computing the output of generator given the found projection, we can fill in the initial gather.

KeywordsPresentation, private, SINBAD, SINBADFALL2017, SLIM
URLhttps://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.pdf
URL1

https://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2017...

Citation Keysiahkoohi2017SINBADFsdi