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dc.contributor.authorOvcharenko, Oleg
dc.contributor.authorKazei, Vladimir
dc.contributor.authorPeter, Daniel
dc.contributor.authorAlkhalifah, Tariq Ali
dc.date.accessioned2020-03-04T08:36:58Z
dc.date.available2020-03-04T08:36:58Z
dc.date.issued2019-08-26
dc.identifier.citationOvcharenko, O., Kazei, V., Peter, D., & Alkhalifah, T. (2019). Transfer Learning For Low Frequency Extrapolation From Shot Gathers For FWI Applications. 81st EAGE Conference and Exhibition 2019. doi:10.3997/2214-4609.201901617
dc.identifier.doi10.3997/2214-4609.201901617
dc.identifier.urihttp://hdl.handle.net/10754/661870
dc.description.abstractLow-frequency data proved to be crucial for robust full-waveform inversion (FWI) applications. However, acquiring those data in the field is a challenging and costly task. Deep neural networks can be trained to extrapolate missing low frequencies, but no optimal network configuration exists. Therefore, the search for an acceptable network architecture is a tedious empirical task whose outcome heavily affects the performance of the application. Here, we propose and utilize transfer learning to reduce the computational efforts otherwise spent on an optimal architecture search and an initial network training. We re-train the light-weight MobileNet convolutional network to infer low-frequency data from a frequency-domain representation of the individual shot-gathers, which leads to an efficient, yet accurate inference of low frequencies according to wavenumber theory. In particular, we show that the extrapolated 0.25 - 1 Hz from 2-4.5 Hz data are accurate enough for acoustic FWI on part of the original BP 2004 model and the Marmousi II model of double scale. We bridge the gap between the 1 Hz predicted and the 2 Hz modeled data by the application of a Sobolev space norm regularization.
dc.publisherEAGE Publications
dc.relation.urlhttp://www.earthdoc.org/publication/publicationdetails/?publication=97373
dc.rightsArchived with thanks to EAGE Publications BV
dc.titleTransfer learning for low frequency extrapolation from shot gathers for FWI applications
dc.typeConference Paper
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.conference.date2019-06-03 to 2019-06-06
dc.conference.name81st EAGE Conference and Exhibition 2019
dc.conference.locationLondon, GBR
dc.eprint.versionPre-print
kaust.personOvcharenko, Oleg
kaust.personKazei, Vladimir
kaust.personPeter, Daniel
kaust.personAlkhalifah, Tariq Ali
refterms.dateFOA2020-03-04T10:13:58Z


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