Extrapolated full-waveform inversion: An image-space approach
Name
ISEFWI-seg.pdf
Size
775.96 KB
Format
Adobe PDF
Checksum (MD5)
0bd86e1164d1480122d513e3d3d49cb4
Author(s)
Li, Yunyue
Demanet, Laurent
Date Issued
August 2017
Journal
SEG Technical Program Expanded Abstracts 2017
Publisher
Society of Exploration Geophysicists
Citation
Li, Yunyue and Laurent Demanet. “Extrapolated Full-Waveform Inversion: An Image-Space Approach.” SEG Technical Program Expanded Abstracts 2017 (August 2017): 1682-1686
Version
Author's final manuscript
Abstract
The primary factor that prevents full waveform inversion from universal success is the band-limited nature of seismic data, resulting in a gap between the low wavenumber background velocity model and the high wavenumber seismic images. In this paper, we propose to bridge the wavenumber gap in the extended image space, where full kinematic information in the data is preserved in spite of the inaccuracy of the background migration velocity model, and where the wavenumber range of the extended image is extrapolated using total-variation constrained deconvolution. This explicit wavenumber extrapolation is nested within least-squares reverse time migration iterations to ensure that the resulting extended images match the recorded band-limited data. We then synthesize reflection data using extended Born modeling with the extrapolated images. Numerical experiments show that although the total variation projection has limited the high frequencies that can be recreated by extended Born modeling, the low frequencies are reliably extrapolated at all offsets, given a reasonable starting velocity model. When the initial model is too crude, the proposed frequency extrapolation breaks down near the complex structures. Keywords: data reconstruction; deconvolution; full-waveform inversion; least-squares migration
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1190/SEGAM2017-17725126.1
