Low-frequency extrapolation with deep learning
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lfedl.pdf
Description
Accepted version
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672.96 KB
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8e7b12b4c705804417e250a84be08e55
Author(s)
Sun, Hongyu
Demanet, Laurent
Date Issued
August 2018
Journal
SEG Technical Program Expanded Abstracts 2018
Publisher
Society of Exploration Geophysicists
Citation
Sun, Hongyu and Demanet, Laurent. "Low-frequency extrapolation with deep learning." SEG Technical Program Expanded Abstracts (2018): 2011-2015 © 2018 Author(s)
Version
Author's final manuscript
Abstract
The lack of the low frequency information and good initial model can seriously affect the success of full waveform inversion (FWI) due to the inherent cycle skipping problem. Reasonable and reliable low frequency extrapolation is in principle the most direct way to solve this problem. In this paper, we propose a deep-learning-based bandwidth extension method by considering low frequency extrapolation as a regression problem. The Deep Neural Networks (DNNs) are trained to automatically extrapolate the low frequencies without prepro-cessing steps. The band-limited recordings are the inputs of the DNNs and, in our numerical experiments, the pretrained neural networks can predict the continuous-valued seismograms in the unobserved low frequency band. For the numerical experiments considered here, it is possible to find the amplitude and phase correlations among different frequency components by training the DNNs with enough data samples, and extrapolate the low frequencies from the band-limited seismic records trace by trace. The synthetic example shows that our approach is not subject to the structural limitations of other methods to bandwidth extension, and seems to offer a tantalizing solution to the problem of properly initializing FWI.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1190/segam2018-2997928.1
