Learning to Unflood the Salt in Full-Waveform Inversion, Application on Vintage GOM Data
Type
Conference PaperAuthors
Alali, A.Alkhalifah, Tariq Ali

KAUST Department
Earth Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Date
2022Permanent link to this record
http://hdl.handle.net/10754/678315
Metadata
Show full item recordAbstract
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/10.3997/2214-4609.202210812Sponsors
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.Conference/Event name
83rd EAGE Annual Conference & ExhibitionAdditional Links
https://www.earthdoc.org/content/papers/10.3997/2214-4609.202210812ae974a485f413a2113503eed53cd6c53
10.3997/2214-4609.202210812