Improving imaging quality using least-squares reverse time migration: application to data from Bohai basin
KAUST DepartmentPhysical Science and Engineering (PSE) Division
Online Publication Date2017-09-20
Print Publication Date2017-10-01
Permanent link to this recordhttp://hdl.handle.net/10754/625286
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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.
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.
SponsorsThe 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.