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    Learning to Unflood the Salt in Full-Waveform Inversion, Application on Vintage GOM Data

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    Type
    Conference Paper
    Authors
    Alali, A.
    Alkhalifah, Tariq Ali cc
    KAUST Department
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Seismic Wave Analysis Group
    Date
    2022
    Permanent link to this record
    http://hdl.handle.net/10754/678315
    
    Metadata
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    Abstract
    Building a velocity model in salt provinces is a challenging task. Traditionally the salt boundaries is manually interpreted by an iterative process of imaging, picking the top of the salt and flooding, re-imaging and picking the bottom of the salt for unflooding. This workflow is time-consuming and prone to errors. Full-waveform inversion (FWI) can be used to correct the erroneous picks of the salt boundaries. However, it requires low frequency and long offsets data to build an accurate salt body. We apply an FWI-based automatic unflooding process on vintage field data that do not meet these requirement by training a neural network using data with similar characteristics. The network is trained to unflood the salt and estimate the subsalt velocity from an inverted flooded model in a regression manner. The network shows good potential to unflood the vintage data and produce results comparable with the legacy model.
    Citation
    Alali, A., & Alkhalifah, T. (2022). Learning to Unflood the Salt in Full-Waveform Inversion, Application on Vintage GOM Data. 83rd EAGE Annual Conference & Exhibition. https://doi.org/10.3997/2214-4609.202210812
    Sponsors
    We thank Mahesh Kalita for his valuable insights and SWAG group for their support. We are also grateful for the Supercomputing Laboratory at KAUST for providing the computational resources.
    Publisher
    European Association of Geoscientists & Engineers
    Conference/Event name
    83rd EAGE Annual Conference & Exhibition
    DOI
    10.3997/2214-4609.202210812
    Additional Links
    https://www.earthdoc.org/content/papers/10.3997/2214-4609.202210812
    ae974a485f413a2113503eed53cd6c53
    10.3997/2214-4609.202210812
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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