Deep learning for low-frequency extrapolation from multioffset seismic data

Oleg Ovcharenko, Vladimir Kazei, Mahesh Kalita, Daniel Peter, Tariq Ali Alkhalifah

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)R1001-R1013
Number of pages1
Issue number6
StatePublished - Sep 6 2019


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