Deblending Using Focal Transformation with a Greedy Inversion Solver

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    Abstract

    In this work, we adopt a greedy inversion solver to design a fast version of the double focal transform that we can use to eliminate blending noise in simultaneous source acquisition. The greedy inversion introduces a coherence-oriented mechanism to enhance focusing of significant model space, leading to a sparse model space and fast convergence rate. Synthetics and numerically blended field data examples demonstrate the validity of its application for deblending. We also tested different inversion parameters (percentile value and weights) influencing the choice of the model subspace. The results indicate that by setting the percentile carefully and using weights it is possible to get better deblending results.
    Original languageEnglish
    Title of host publicationProceedings of 79th EAGE Conference and Exhibition 2017
    PublisherEAGE
    Number of pages5
    DOIs
    Publication statusPublished - 12 Jun 2017
    Event79th EAGE Conference and Exhibition 2017: Energy, Technology, Sustainability - Time to Open a New Chapter - Paris, France
    Duration: 12 Jun 201715 Jun 2017
    Conference number: 79

    Conference

    Conference79th EAGE Conference and Exhibition 2017
    CountryFrance
    CityParis
    Period12/06/1715/06/17

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