Surface multiple leakage extraction using local primary- and-multiple orthogonalization

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    Abstract

    Surface-related multiple elimination (SRME) is a solid and effective approach for primary estimation. However, due to the imperfections in data and method (e.g. coarsely-sampled dataset and balancing effect of adaptive subtraction) multiple energy leakage is commonly seen in the results of SRME-predicted primaries. Assuming that the primaries and multiples do not correlate locally in the time-space domain, we are able to extract the leaked multiples from the initially estimated primaries using local primary-and-multiple orthogonalization. The proposed framework consists of two steps: an initial primary/multiple estimation step and a multiple-leakage extraction step. The initial step corresponds to SRME, which produces the initial estimated primary and multiple models. The second step is based on local primary-and-multiple orthogonalization to retrieve the leaked multiples, which can be seen as a remedy for correcting the initial estimated primary and multiple models. Thus, we can obtain a better primary output which has much less leaked multiple energy. We demonstrate a good performance of our proposed framework on both synthetic and field data, where it repairs the leakage of standard adaptive subtraction.

    Original languageEnglish
    Title of host publication81st EAGE Conference and Exhibition 2019
    EditorsHoward Leach
    PublisherEAGE
    Number of pages5
    ISBN (Electronic)9789462822894
    DOIs
    Publication statusPublished - 2019
    Event81st EAGE Conference and Exhibition 2019 - ExCeL Centre, London, United Kingdom
    Duration: 3 Jun 20196 Jun 2019
    https://eage.eventsair.com/81st-eage-annual-conference-and-exhibtion/

    Conference

    Conference81st EAGE Conference and Exhibition 2019
    CountryUnited Kingdom
    CityLondon
    Period3/06/196/06/19
    Internet address

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