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dc.contributor.authorZhang, Zhendong
dc.contributor.authorAlkhalifah, Tariq Ali
dc.contributor.authorNaeini, Ehsan Zabihi
dc.contributor.authorSun, Bingbing
dc.date.accessioned2018-04-01T08:20:28Z
dc.date.available2018-04-01T08:20:28Z
dc.date.issued2018-03-21
dc.identifier.citationZhang Z, Alkhalifah T, Naeini EZ, Sun B (2018) Multiparameter Elastic Full Waveform Inversion with Facies-based Constraints. Geophysical Journal International. Available: http://dx.doi.org/10.1093/gji/ggy113.
dc.identifier.issn0956-540X
dc.identifier.issn1365-246X
dc.identifier.doi10.1093/gji/ggy113
dc.identifier.urihttp://hdl.handle.net/10754/627390
dc.description.abstractFull waveform inversion (FWI) incorporates all the data characteristics to estimate the parameters described by the assumed physics of the subsurface. However, current efforts to utilize full waveform inversion beyond improved acoustic imaging, like in reservoir delineation, faces inherent challenges related to the limited resolution and the potential trade-off between the elastic model parameters. Some anisotropic parameters are insufficiently updated because of their minor contributions to the surface collected data. Adding rock physics constraints to the inversion helps mitigate such limited sensitivity, but current approaches to add such constraints are based on including them as a priori knowledge mostly valid around the well or as a global constraint for the whole area. Since similar rock formations inside the Earth admit consistent elastic properties and relative values of elasticity and anisotropy parameters (this enables us to define them as a seismic facies), utilizing such localized facies information in FWI can improve the resolution of inverted parameters. We propose a novel approach to use facies-based constraints in both isotropic and anisotropic elastic FWI. We invert for such facies using Bayesian theory and update them at each iteration of the inversion using both the inverted models and a prior information. We take the uncertainties of the estimated parameters (approximated by radiation patterns) into consideration and improve the quality of estimated facies maps. Four numerical examples corresponding to different acquisition, physical assumptions and model circumstances are used to verify the effectiveness of the proposed method.
dc.description.sponsorshipWe thank Juwon Oh, Vladimir Kazei and Yike Liu (IGG, CAS) for their helpful discussions. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. The research was partly funded by the National Natural Science Foundation of China (Grant Nos. 41730425).
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggy113/4950497
dc.rightsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Geophysical Journal International following peer review. The version of record is available online at: https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggy113/4950497.
dc.subjectElasticity
dc.subjectSeismic anisotropy
dc.subjectFull waveform inversion
dc.subjectFacies
dc.titleMultiparameter Elastic Full Waveform Inversion with Facies-based Constraints
dc.typeArticle
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSeismic Wave Analysis Group
dc.identifier.journalGeophysical Journal International
dc.eprint.versionPost-print
dc.contributor.institutionIkon Science- R&D, 1 The Crescent, Surbiton, London, KT6 4BN, UK
kaust.personZhang, Zhendong
kaust.personAlkhalifah, Tariq Ali
kaust.personSun, Bingbing
refterms.dateFOA2018-06-14T05:32:38Z
dc.date.published-online2018-03-21
dc.date.published-print2018-06-01


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