Deep Learning for Seismic Data Reconstruction: Opportunities and Challenges

Oleg Ovcharenko, S. Hou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Natural and instrumental conditions during field seismic survey lead to noise and irregularities in acquired seismic data. In this work, we explore challenges and opportunities related to denoising and interpolation of seismic data by deep convolutional neural networks. In particular, we apply three network configurations to field data and match them with suitable applications. We show that U-Net is beneficial for denoising applications while adversarial generative networks (GAN) are superior in interpolation tasks. Enhanced interpolation capability of GANs, however, comes at cost of increased uncertainty in the results and we raise awareness about this observation. In the end, we consider the pitfalls of conventional metrics and outline the requirements for data-driven approaches to be suitable in production applications.
Original languageEnglish (US)
Title of host publicationFirst EAGE Digitalization Conference and Exhibition
PublisherEuropean Association of Geoscientists & Engineers
DOIs
StatePublished - 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-03-25
Acknowledgements: The authors thank CGG for permission to publish. Special thanks to Henning Hoeber, Alex Clowes, Igor Mikhalev, Ewa Kaszycka, Gordon Poole, Sharon Howe, Jeremie Messud and our colleagues in CGG processing and imaging for the discussions and suggestions.

Fingerprint

Dive into the research topics of 'Deep Learning for Seismic Data Reconstruction: Opportunities and Challenges'. Together they form a unique fingerprint.

Cite this