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dc.contributor.authorPlotnitskii, Pavel
dc.contributor.authorKazei, Vladimir
dc.contributor.authorOvcharenko, Oleg
dc.contributor.authorPeter, Daniel
dc.contributor.authorAlkhalifah, Tariq Ali
dc.date.accessioned2021-03-22T06:35:31Z
dc.date.available2021-03-22T06:35:31Z
dc.date.issued2020-12
dc.identifier.citationPlotnitskii, P., Kazei, V., Ovcharenko, O., Peter, D., & Alkhalifah, T. (2020). Extrapolation of Low Wavenumbers in FWI Gradients by a Deep Convolutional Neural Network. EAGE 2020 Annual Conference & Exhibition Online. doi:10.3997/2214-4609.202011988
dc.identifier.doi10.3997/2214-4609.202011988
dc.identifier.urihttp://hdl.handle.net/10754/668181
dc.description.abstractSeismic full-waveform inversion (FWI) as a non-linear, iterative optimization benefits from low-frequency data to constrain low-wavenumber model updates and to improve model convergence. However, low-frequency data is often limited in active seismic acquisitions. Using a model-domain approach, we attempt to generate low-wavenumber model updates from existing gradients at higher frequencies within a deep learning framework. Namely, we train a convolutional neural network (CNN) to provide missing FWI model updates associated with low-frequency data from higher frequency updates. We test this technique on the Marmousi II model and quantify the goodness of fit of the inversion result using an R2 score model misfit. We observe that predicted low-wavenumber updates differ significantly from model updates using actual low-frequency data. However, comparing the final models of the corresponding multi-scale strategy FWIs we find that resulting differences are negligible.
dc.publisherEAGE Publications
dc.relation.urlhttps://www.earthdoc.org/content/papers/10.3997/2214-4609.202011988
dc.rightsArchived with thanks to European Association of Geoscientists & Engineers
dc.titleExtrapolation of Low Wavenumbers in FWI Gradients by a Deep Convolutional Neural Network
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Science and Engineering
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSeismic Wave Analysis Group
dc.rights.embargodate2021-12-01
dc.conference.dateDecember 2020
dc.conference.nameEAGE2020: Annual Conference Online
dc.conference.locationOnline
dc.eprint.versionPost-print
kaust.personPlotnitskii, Pavel
kaust.personKazei, Vladimir
kaust.personOvcharenko, Oleg
kaust.personPeter, Daniel
kaust.personAlkhalifah, Tariq Ali


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