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dc.contributor.authorBirnie, Claire Emma
dc.contributor.authorAlkhalifah, Tariq Ali
dc.date.accessioned2022-05-09T13:50:59Z
dc.date.available2022-05-09T13:50:59Z
dc.date.issued2022-05-03
dc.identifier.citationBirnie, C., & Alkhalifah, T. (2022). Leveraging domain adaptation for efficient seismic denoising. Energy in Data Conference, Austin, Texas, 20–23 February 2022. https://doi.org/10.7462/eid2022-04.1
dc.identifier.doi10.7462/eid2022-04.1
dc.identifier.urihttp://hdl.handle.net/10754/676710
dc.description.abstractThe selection of training data for deep learning procedures dictates both the neural network's performance and its applicability to further datasets. Particularly in seismic applications, the selection is non-trivial with the common approaches of manually labelling field data or generating synthetic data both exhibiting severe limitations. The former in its inability to outperform conventional approaches required for the label generation and the later in its inability to properly represent future application data. Domain adaptation, through input features and label transformations, offers the potential to leverage on the benefits of both these approaches while reducing their drawbacks. In this work we illustrate how vital information from field data can be incorporated into a training procedure on synthetic data with the trained network successfully applied on the field data afterwards, despite large differences between the training and inference, i.e., synthetic and field, datasets. Furthermore, we illustrate how an inverse correlation procedure can be incorporated into the training procedure in an attempt to maintain the original wavefield properties.
dc.description.sponsorshipThe authors thank Prof. M. Ravasi, Dr O. Ovcharenko and Dr H. Wang for insightful discussions, as well as the wider KAUST Seismic Wave Analysis Group. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia
dc.publisherEnergy in Data
dc.relation.urlhttps://library.seg.org/doi/10.7462/eid2022-04.1
dc.rightsArchived with thanks to Energy in Data
dc.titleLeveraging domain adaptation for efficient seismic denoising
dc.typeConference Paper
dc.contributor.departmentAli I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSeismic Wave Analysis Group
dc.conference.date2022-02-20 to 2022-02-23
dc.conference.nameEnergy in Data Conference
dc.conference.locationAustin, Texas
dc.eprint.versionPost-print
kaust.personBirnie, Claire Emma
kaust.personAlkhalifah, Tariq Ali
kaust.acknowledged.supportUnitwider KAUST Seismic Wave Analysis Group
kaust.acknowledged.supportUnitSupercomputing Laboratory


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