Linearized least-square imaging of internally scattered data

Ali Aldawood, Ibrahim Hoteit, George M. Turkiyyah, M. A H Zuberi, Tariq Ali Alkhalifah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Internal multiples deteriorate the quality of the migrated image obtained conventionally by imaging single scattering energy. However, imaging internal multiples properly has the potential to enhance the migrated image because they illuminate zones in the subsurface that are poorly illuminated by single-scattering energy such as nearly vertical faults. Standard migration of these multiples provide subsurface reflectivity distributions with low spatial resolution and migration artifacts due to the limited recording aperture, coarse sources and receivers sampling, and the band-limited nature of the source wavelet. Hence, we apply a linearized least-square inversion scheme to mitigate the effect of the migration artifacts, enhance the spatial resolution, and provide more accurate amplitude information when imaging internal multiples. Application to synthetic data demonstrated the effectiveness of the proposed inversion in imaging a reflector that is poorly illuminated by single-scattering energy. The least-square inversion of doublescattered data helped delineate that reflector with minimal acquisition fingerprint.
Original languageEnglish (US)
Title of host publicationProceedings 76th EAGE Conference and Exhibition 2014
PublisherEAGE Publications
ISBN (Print)9781632666949
DOIs
StatePublished - 2014

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