Extrapolating low-frequency prestack land data with deep learning
Alkhalifah, Tariq Ali
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Earth Science and Engineering Program
Extreme Computing Research Center
King Abdullah University of Science and Technology
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Online Publication Date2020-09-30
Print Publication Date2020-09-30
Permanent link to this recordhttp://hdl.handle.net/10754/665480
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AbstractMissing low-frequency content in seismic data is a common challenge for seismic inversion. Long wavelengths are necessary to reveal large structures in the subsurface and to build an acceptable starting point for later iterations of full-waveform inversion (FWI). High-frequency land seismic data are particularly challenging due to the elastic nature of the Earth contrasting with acoustic air at the typically rugged free surface, which makes the use of low frequencies even more vital to the inversion. We propose a supervised deep learning framework for bandwidth extrapolation of prestack elastic data in the time domain. We utilize a Convolutional Neural Network (CNN) with a UNet-inspired architecture to convert portions of band-limited shot gathers from 5-15 Hz to 0-5 Hz band. In the synthetic experiment, we train the network on 192x192 patches of wavefields simulated for different cross-sections of the elastic SEAM Arid model with free-surface. Then, we test the network on unseen shot gathers from the same model to demonstrate the viability of the approach. The results show promise for future field data applications.
CitationOvcharenko, O., Kazei, V., Plotnitskiy, P., Peter, D., Silvestrov, I., Bakulin, A., & Alkhalifah, T. (2020). Extrapolating low-frequency prestack land data with deep learning. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3427522.1
SponsorsWe thank Anatoly Baumstein from ExxonMobil for interestingdiscussion on low-frequency extrapolation at the EAGE An-nual Meeting. The research reported in this publication wassupported by funding from Saudi Aramco and King AbdullahUniversity of Science and Technology (KAUST).
PublisherSociety of Exploration Geophysicists