Improving imaging quality using least-squares reverse time migration: application to data from Bohai basin

Handle URI:
http://hdl.handle.net/10754/625286
Title:
Improving imaging quality using least-squares reverse time migration: application to data from Bohai basin
Authors:
Zhang, Hao; Liu, Qiancheng; Wu, Jizhong
Abstract:
Least-squares reverse time migration (LSRTM) is a seismic imaging technique based on linear inversion, which usually aims to improve the quality of seismic image through removing the acquisition footprint, suppressing migration artifacts, and enhancing resolution. LSRTM has been shown to produce migration images with better quality than those computed by conventional migration. In this paper, our derivation of LSRTM approximates the near-incident reflection coefficient with the normal-incident reflection coefficient, which shows that the reflectivity term defined is related to the normal-incident reflection coefficient and the background velocity. With reflected data, LSRTM is mainly sensitive to impedance perturbations. According to an approximate relationship between them, we reformulate the perturbation related system into a reflection-coefficient related one. Then, we seek the inverted image through linearized iteration. In the proposed algorithm, we only need the migration velocity for LSRTM considering that the density changes gently when compared with migration velocity. To validate our algorithms, we first apply it to a synthetic case and then a field data set. Both applications illustrate that our imaging results are of good quality.
KAUST Department:
King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Citation:
Zhang H, Liu Q, Wu J (2017) Improving imaging quality using least-squares reverse time migration: application to data from Bohai basin. Journal of Geophysics and Engineering. Available: http://dx.doi.org/10.1088/1742-2140/aa7e49.
Publisher:
IOP Publishing
Journal:
Journal of Geophysics and Engineering
Issue Date:
7-Jul-2017
DOI:
10.1088/1742-2140/aa7e49
Type:
Article
ISSN:
1742-2132; 1742-2140
Sponsors:
The authors are grateful to the National Major Project of China (under grant 2017ZX05008-007) and China Postdoctoral Science Foundation (under grant 2017M610982) for supporting this work. We also thank China National Petroleum Corporation for providing the field dataset.
Additional Links:
http://iopscience.iop.org/article/10.1088/1742-2140/aa7e49
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Haoen
dc.contributor.authorLiu, Qianchengen
dc.contributor.authorWu, Jizhongen
dc.date.accessioned2017-08-03T11:56:25Z-
dc.date.available2017-08-03T11:56:25Z-
dc.date.issued2017-07-07en
dc.identifier.citationZhang H, Liu Q, Wu J (2017) Improving imaging quality using least-squares reverse time migration: application to data from Bohai basin. Journal of Geophysics and Engineering. Available: http://dx.doi.org/10.1088/1742-2140/aa7e49.en
dc.identifier.issn1742-2132en
dc.identifier.issn1742-2140en
dc.identifier.doi10.1088/1742-2140/aa7e49en
dc.identifier.urihttp://hdl.handle.net/10754/625286-
dc.description.abstractLeast-squares reverse time migration (LSRTM) is a seismic imaging technique based on linear inversion, which usually aims to improve the quality of seismic image through removing the acquisition footprint, suppressing migration artifacts, and enhancing resolution. LSRTM has been shown to produce migration images with better quality than those computed by conventional migration. In this paper, our derivation of LSRTM approximates the near-incident reflection coefficient with the normal-incident reflection coefficient, which shows that the reflectivity term defined is related to the normal-incident reflection coefficient and the background velocity. With reflected data, LSRTM is mainly sensitive to impedance perturbations. According to an approximate relationship between them, we reformulate the perturbation related system into a reflection-coefficient related one. Then, we seek the inverted image through linearized iteration. In the proposed algorithm, we only need the migration velocity for LSRTM considering that the density changes gently when compared with migration velocity. To validate our algorithms, we first apply it to a synthetic case and then a field data set. Both applications illustrate that our imaging results are of good quality.en
dc.description.sponsorshipThe authors are grateful to the National Major Project of China (under grant 2017ZX05008-007) and China Postdoctoral Science Foundation (under grant 2017M610982) for supporting this work. We also thank China National Petroleum Corporation for providing the field dataset.en
dc.publisherIOP Publishingen
dc.relation.urlhttp://iopscience.iop.org/article/10.1088/1742-2140/aa7e49en
dc.rightsThis is an author-created, un-copyedited version of an article accepted for publication/published in Journal of Geophysics and Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://doi.org/10.1088/1742-2140/aa7e49. As the Version of Record of this article is going to be/has been published on a subscription basis, this Accepted Manuscript will be available for reuse under a CC BY-NC-ND 3.0 licence after a 12 month embargo period.en
dc.subjectleast-squares migrationen
dc.subjectimaging qualityen
dc.subjectreflectivityen
dc.subjectlinear inversionen
dc.titleImproving imaging quality using least-squares reverse time migration: application to data from Bohai basinen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalJournal of Geophysics and Engineeringen
dc.eprint.versionPost-printen
dc.contributor.institutionKey Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics Chinese Academy of Sciences, Beituchengxilu 19, Beijing, 100029, CHINAen
dc.contributor.institutionInstitute of Geology and Geophysics, Chinese Academy of Sciences, No. 19, Beitucheng Western Road, Chaoyang District, Beijing, P.R.China, Beijing, Beijing, 100029, CHINAen
dc.contributor.institutionExploration and Development Institute of Jidong Oilfield, Tangshan, CHINAen
kaust.authorLiu, Qianchengen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.