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dc.contributor.authorOvcharenko, Oleg
dc.contributor.authorKazei, Vladimir
dc.contributor.authorKalita, Mahesh
dc.contributor.authorPeter, Daniel
dc.contributor.authorAlkhalifah, Tariq Ali
dc.date.accessioned2019-06-24T06:21:04Z
dc.date.available2019-06-24T06:21:04Z
dc.date.issued2019-09-06
dc.identifier.citationOvcharenko, O., Kazei, V., Kalita, M., Peter, D., & Alkhalifah, T. (2019). Deep learning for low-frequency extrapolation from multioffset seismic data. GEOPHYSICS, 84(6), R1001–R1013. doi:10.1190/geo2018-0884.1
dc.identifier.doi10.1190/geo2018-0884.1
dc.identifier.urihttp://hdl.handle.net/10754/655680
dc.description.abstractLow-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.
dc.description.sponsorshipWe are grateful to G. Pratt and W. Mulder for their comments, X. Zhang who advised us on ML, and F. J Simons who shared his expertise on stochastic processes. We also give credit to T. V. Leuween, whose open-source FWI code was used as a building block in our inversion scheme (https://github.com/tleeuwen/SimpleFWI). We thank the members of the Seismic Modeling and Inversion group (SMI) and the Seismic Wave Analysis Group (SWAG) at King Abdullah University of Science and Technology (KAUST) for the constructive discussions. The research reported in this publication was supported by funding from KAUST.
dc.language.isoen
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttps://library.seg.org/doi/10.1190/geo2018-0884.1
dc.rightsArchived with thanks to GEOPHYSICS
dc.titleDeep learning for low-frequency extrapolation from multioffset seismic data
dc.typeArticle
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.identifier.journalGEOPHYSICS
dc.eprint.versionPre-print
pubs.publication-statusSubmitted
kaust.personOvcharenko, Oleg
kaust.personKazei, Vladimir
kaust.personKalita, Mahesh
kaust.personPeter, Daniel
kaust.personAlkhalifah, Tariq Ali
refterms.dateFOA2019-06-24T06:21:05Z
kaust.acknowledged.supportUnitSeismic Wave Analysis Group (SWAG)
kaust.acknowledged.supportUnitSeismic Modeling and Inversion group (SMI)


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