Deep learning for low-frequency extrapolation from multioffset seismic data
Type
ArticleKAUST Department
Earth Science and Engineering ProgramExtreme Computing Research Center
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Date
2019-09-06Permanent link to this record
http://hdl.handle.net/10754/655680
Metadata
Show full item recordAbstract
Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.Citation
Ovcharenko, O., Kazei, V., Kalita, M., Peter, D., & Alkhalifah, T. (2019). Deep learning for low-frequency extrapolation from multioffset seismic data. GEOPHYSICS, 84(6), R1001–R1013. doi:10.1190/geo2018-0884.1Sponsors
We are grateful to G. Pratt and W. Mulder for their comments, X. Zhang who advised us on ML, and F. J Simons who shared his expertise on stochastic processes. We also give credit to T. V. Leuween, whose open-source FWI code was used as a building block in our inversion scheme (https://github.com/tleeuwen/SimpleFWI). We thank the members of the Seismic Modeling and Inversion group (SMI) and the Seismic Wave Analysis Group (SWAG) at King Abdullah University of Science and Technology (KAUST) for the constructive discussions. The research reported in this publication was supported by funding from KAUST.Publisher
Society of Exploration GeophysicistsJournal
GEOPHYSICSAdditional Links
https://library.seg.org/doi/10.1190/geo2018-0884.1ae974a485f413a2113503eed53cd6c53
10.1190/geo2018-0884.1