Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study
Abstract
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models inmore »
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Fossil Energy (FE)
- OSTI Identifier:
- 1827579
- Report Number(s):
- LA-UR-21-20004
Journal ID: ISSN 0196-2892
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Transactions on Geoscience and Remote Sensing
- Additional Journal Information:
- Journal Volume: 60; Journal ID: ISSN 0196-2892
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; earth sciences; data augmentation; multiscale analysis; scientific deep learning; seismic full-waveform inversion (FWI); style transfer
Citation Formats
Lin, Youzuo, Feng, Shihang, and Wohlberg, Brendt Egon. Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study. United States: N. p., 2021.
Web. doi:10.1109/tgrs.2021.3114101.
Lin, Youzuo, Feng, Shihang, & Wohlberg, Brendt Egon. Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study. United States. https://doi.org/10.1109/tgrs.2021.3114101
Lin, Youzuo, Feng, Shihang, and Wohlberg, Brendt Egon. Fri .
"Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study". United States. https://doi.org/10.1109/tgrs.2021.3114101. https://www.osti.gov/servlets/purl/1827579.
@article{osti_1827579,
title = {Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study},
author = {Lin, Youzuo and Feng, Shihang and Wohlberg, Brendt Egon},
abstractNote = {Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.},
doi = {10.1109/tgrs.2021.3114101},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
number = ,
volume = 60,
place = {United States},
year = {Fri Oct 01 00:00:00 EDT 2021},
month = {Fri Oct 01 00:00:00 EDT 2021}
}
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