Learning to Unflood the Salt in Full-Waveform Inversion, Application on Vintage GOM Data
KAUST DepartmentEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/678315
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AbstractBuilding 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.
CitationAlali, 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
SponsorsWe 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 name83rd EAGE Annual Conference & Exhibition