Deep learning for low-frequency extrapolation from multioffset seismic data

Author:

Ovcharenko Oleg1ORCID,Kazei Vladimir1ORCID,Kalita Mahesh1ORCID,Peter Daniel1ORCID,Alkhalifah Tariq1ORCID

Affiliation:

1. King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.(corresponding author); .

Abstract

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.

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

Reference77 articles.

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