Simultaneous joint migration inversion for semicontinuous time-lapse seismic data

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


    We demonstrate that Simultaneous Joint Migration Inversion (SJMI) is a very suitable tool to process time-lapse datasets acquired from semi-continuous time-lapse monitoring surveys, such as a so-called i4D survey technology. In the i4D technology, inexpensive sparse localized surveys, termed i4D surveys, are employed between conventional full-field surveys, which can be treated as a special case of changing geometries during monitoring. The simultaneous strategy of SJMI can help compensate the poor illumination of the sparse surveys with full-field survey information during processing. The imaging of multiples embedded in JMI also helps to improve the illumination. Furthermore, for (semi-)continuous monitoring, we propose to apply an extra L1 denoising sparse constraint on the image differences between the baseline and monitors along the calendar time axis, by taking advantage of the feature that time-lapse effects act like continuous events along the calendar time axis when the monitoring surveys are employed frequently enough. With a complex synthetic example based on the Marmousi model, we demonstrate that SJMI is a proper tool to process datasets from such i4D surveys. We also show that SJMI with the extra L1 constraint on reflectivity differences along the calendar time axis improves the quality of time-lapse effects.
    Original languageEnglish
    Title of host publicationSEG Technical Program Expanded Abstracts 2017
    Subtitle of host publication24-29 September, Houston, USA
    Publication statusPublished - 2017
    EventSEG Annual Meeting 2017 - Houston, United States
    Duration: 24 Sep 201729 Sep 2018
    Conference number: 87

    Publication series

    NameSEG Technical Program Expanded Abstracts 2017
    ISSN (Electronic)1949-4645


    ConferenceSEG Annual Meeting 2017
    Abbreviated titleSEG 2017
    CountryUnited States


    • 4D
    • depth migration
    • time-lapse

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