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dc.contributor.authorYu, Han
dc.contributor.authorHuang, Yunsong
dc.contributor.authorGuo, Bowen
dc.date.accessioned2016-05-09T07:48:46Z
dc.date.available2016-05-09T07:48:46Z
dc.date.issued2016-04-28
dc.identifier.citationNear-surface fault detection by migrating back-scattered surface waves with and without velocity profiles 2016, 130:81 Journal of Applied Geophysics
dc.identifier.issn09269851
dc.identifier.doi10.1016/j.jappgeo.2016.04.013
dc.identifier.urihttp://hdl.handle.net/10754/608625
dc.description.abstractWe demonstrate that diffraction stack migration can be used to discover the distribution of near-surface faults. The methodology is based on the assumption that near-surface faults generate detectable back-scattered surface waves from impinging surface waves. We first isolate the back-scattered surface waves by muting or FK filtering, and then migrate them by diffraction migration using the surface wave velocity as the migration velocity. Instead of summing events along trial quasi-hyperbolas, surface wave migration sums events along trial quasi-linear trajectories that correspond to the moveout of back-scattered surface waves. We have also proposed a natural migration method that utilizes the intrinsic traveltime property of the direct and the back-scattered waves at faults. For the synthetic data sets and the land data collected in Aqaba, where surface wave velocity has unexpected perturbations, we migrate the back-scattered surface waves with both predicted velocity profiles and natural Green's function without velocity information. Because the latter approach avoids the need for an accurate velocity model in event summation, both the prestack and stacked migration images show competitive quality. Results with both synthetic data and field records validate the feasibility of this method. We believe applying this method to global or passive seismic data can open new opportunities in unveiling tectonic features.
dc.description.sponsorshipWe thank the sponsors of the CSIM Consortium (http://csim.kaust.edu.sa/web/) for their support. We also thank Prof. Gerard T. Schuster and anonymous CSIM members for their efforts and comments in the development of this work. This work is also sponsored by the National Natural Science Fund of China (Grant Nos.: 11501302, 61571238, 61571233, 61501250, 61502247), the Natural Science Foundation for Young Scientists of Jiangsu Province (Grant No.: BK20150856, BK20140879), and the Scientific Research Foundation of Nanjing University of Posts and Telecommunications (NUPTSF, Grant No.: NY214170).
dc.language.isoen
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0926985116301136
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Applied Geophysics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Applied Geophysics, 26 April 2016. DOI: 10.1016/j.jappgeo.2016.04.013
dc.subjectBack-scattered surface waves
dc.subjectMigration
dc.subjectFault detection
dc.subjectVelocity
dc.subjectNatural Green's function
dc.titleNear-surface fault detection by migrating back-scattered surface waves with and without velocity profiles
dc.typeArticle
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalJournal of Applied Geophysics
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Technology, Nanjing University of Posts & Telecommunications, Nanjing 210023, China
dc.contributor.institutionSubsurface Imaging, CGG, Houston, USA
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personGuo, Bowen
refterms.dateFOA2018-04-26T00:00:00Z
dc.date.published-online2016-04-28
dc.date.published-print2016-07


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