Show simple item record

dc.contributor.authorOvcharenko, Oleg
dc.contributor.authorHou, S.
dc.date.accessioned2021-03-24T07:06:09Z
dc.date.available2021-03-24T07:06:09Z
dc.date.issued2020
dc.identifier.citationOvcharenko, O., & Hou, S. (2020). Deep Learning for Seismic Data Reconstruction: Opportunities and Challenges. First EAGE Digitalization Conference and Exhibition. doi:10.3997/2214-4609.202032054
dc.identifier.doi10.3997/2214-4609.202032054
dc.identifier.urihttp://hdl.handle.net/10754/668227
dc.description.abstractNatural 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.
dc.description.sponsorshipThe 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.
dc.publisherEAGE Publications
dc.relation.urlhttps://www.earthdoc.org/content/papers/10.3997/2214-4609.202032054
dc.rightsArchived with thanks to European Association of Geoscientists & Engineers
dc.titleDeep Learning for Seismic Data Reconstruction: Opportunities and Challenges
dc.typeConference Paper
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.rights.embargodate2021-12-03
dc.conference.date2020-11-30 to 2020-12-03
dc.conference.name1st EAGE Digitalization Conference and Exhibition
dc.conference.locationVienna, AUT
dc.eprint.versionPost-print
dc.contributor.institutionCGG
kaust.personOvcharenko, Oleg
dc.identifier.eid2-s2.0-85092664337


Files in this item

Thumbnail
Name:
dl_for_interpolation_ovcharenko_song_2020_compressed.pdf
Size:
593.0Kb
Format:
PDF
Description:
Accepted Manuscript
Embargo End Date:
2021-12-03

This item appears in the following Collection(s)

Show simple item record