Seismic model low wavenumber extrapolation by a deep convolutional neural network
KAUST DepartmentEarth Science and Engineering
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Permanent link to this recordhttp://hdl.handle.net/10754/668758
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AbstractConventional seismic data are naturally mainly sensitive to the very smooth velocity variations that alter transmission traveltimes (low-model wavenumbers) and very abrupt discontinuities that cause reflections (high-model wavenumbers). Full-waveform inversion (FWI) of seismic data inherits this lack of middle model wavenumber illumination, which results into ringy artifacts in the gradients. Multiple methods have been suggested to overcome this issue. Here we tackle the problem of missing wavenumbers with a deep-learning approach. Namely, we filter out the wavenumbers that are expected to be missing from the acquisition design and then train a deep convolutional neural network to provide the missing wavenumbers trace-by-trace. We test several network configurations and several training sets derived from the Marmousi II model. The neural network shows limited capabilities in generalizing from the input data sets. We also report a tradeoff between the generalization abilities and accuracy on the training data set.
CitationPlotnitskii, P., Alkhalifah, T., Ovcharenko, O., & Kazei, V. (2019). Seismic model low wavenumber extrapolation by a deep convolutional neural network. ASEG Extended Abstracts, 2019(1), 1–5. doi:10.1080/22020586.2019.12073206
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
PublisherInforma UK Limited
JournalASEG Extended Abstracts