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dc.contributor.authorZhang, Zhendong
dc.contributor.authorAlkhalifah, Tariq Ali
dc.date.accessioned2020-02-13T07:18:18Z
dc.date.available2020-02-13T07:18:18Z
dc.date.issued2019-08-24
dc.date.submitted2018-09-30
dc.identifier.citationZhang, Z.-D., & Alkhalifah, T. (2019). Regularized elastic full-waveform inversion using deep learning. GEOPHYSICS, 84(5), R741–R751. doi:10.1190/geo2018-0685.1
dc.identifier.doi10.1190/geo2018-0685.1
dc.identifier.urihttp://hdl.handle.net/10754/661502
dc.description.abstractObtaining high-resolution models of the earth, especially around the reservoir, is crucial to properly image and interpret the subsurface. We have developed a regularized elastic full-waveform inversion (FWI) method that uses facies as the prior information. Deep neural networks (DNNs) are trained to estimate the distribution of facies in the subsurface. Here, we use facies extracted from wells as the prior information. Seismic data, well logs, and interpreted facies have different resolution and illumination to the subsurface. Besides, a physical process, such as anelasticity in the subsurface, is often too complicated to be fully considered. Therefore, there are often no explicit formulas to connect the data coming from different geophysical surveys. A deep-learning method can find the statistically correct connection without the need to know the complex physics. In our deep-learning scheme, we specifically use it to assist the inverse problem instead of the widely used labeling task. First, we conduct an adaptive data-selection elastic FWI using the observed seismic data and obtain estimates of the subsurface, which do not need to be perfect. Then, we use the extracted facies information from the wells and force the estimated model to fit the facies by training DNNs. In this way, a list of facies is mapped to a 2D or 3D inverted model guided mainly by the structure features of the model. The multidimensional distribution of facies is used either as a regularization term or as an initial model for the next waveform inversion. Our method has two main features: (1) It applies to any kind of distribution of data samples and (2) it interpolates facies between wells guided by the structure of the estimated models. Results with synthetic and field data illustrate the benefits and limitations of this method.
dc.description.sponsorshipWe thank Y. Liu, Z. Wu, and Y. Choi for their helpful discussions. We also thank E. Gasperikova, A. Guitton, and four anonymous reviewers for the effort put into the review of this paper. We thank the King Abdullah University of Science & Technology (KAUST) for its support and specifically the seismic wave analysis group members for their valuable insights. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST in Thuwal, Saudi Arabia. We thank Vecta Oil and Gas and especially B. Devault for the BigSky data and the helpful discussions.
dc.publisherSociety of Exploration Geophysicists
dc.relation.urlhttp://mr.crossref.org/iPage?doi=10.1190%2Fgeo2018-0685.1
dc.rightsArchived with thanks to GEOPHYSICS
dc.titleRegularized elastic full-waveform inversion using deep learning
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.journalGEOPHYSICS
dc.eprint.versionPost-print
kaust.personZhang, Zhendong
kaust.personAlkhalifah, Tariq Ali
dc.date.accepted2019-06-11
refterms.dateFOA2020-02-15T09:13:25Z
kaust.acknowledged.supportUnitSupercomputing Laboratory at KAUST


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