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dc.contributor.authorKazei, Vladimir
dc.contributor.authorOvcharenko, Oleg
dc.contributor.authorAlkhalifah, Tariq Ali
dc.date.accessioned2020-10-07T11:47:23Z
dc.date.available2020-10-07T11:47:23Z
dc.date.issued2020-09-30
dc.identifier.citationKazei, V., Ovcharenko, O., & Alkhalifah, T. (2020). Velocity model building by deep learning: From general synthetics to field data application. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3428324.1
dc.identifier.doi10.1190/segam2020-3428324.1
dc.identifier.urihttp://hdl.handle.net/10754/665479
dc.description.abstractVelocity model building is not straightforward in geologically complex environments. We train a convolutional neural network (CNN) to map full wavefields to smooth subsurface parameter distributions to address the problem. Specifically, cubes of neighboring CMP gathers are mapped into in 1D vertical profiles to simplify the training phase and to make it easier to utilize well logs in future applications. We train the CNN using a total of one hundred thousand random subsurface models generated on-the-fly and the corresponding synthetic data. The application of the trained CNN on synthetic and real data admitted reasonably accurate models representing mostly the low wavenumber features of the true models.
dc.description.sponsorshipWe thank Saudi Aramco for its support and CGG for sharingthe broadseis field data. The research reported in this publication was supported by funding from King Abdullah Universityof Science and Technology (KAUST), Thuwal, 23955-6900,Saudi Arabia
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttps://library.seg.org/doi/10.1190/segam2020-3428324.1
dc.rightsArchived with thanks to Society of Exploration Geophysicists
dc.titleVelocity model building by deep learning: From general synthetics to field data application
dc.typeConference Paper
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSeismic Wave Analysis Group
dc.eprint.versionPost-print
kaust.personKazei, Vladimir
kaust.personOvcharenko, Oleg
kaust.personAlkhalifah, Tariq Ali
refterms.dateFOA2020-10-22T13:29:17Z
dc.date.published-online2020-09-30
dc.date.published-print2020-09-30


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