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Does Full Waveform Inversion Benefit from Big Data?
Open accessPreprint — published as 10.1038/s41598-024-68573-7 · Paper page
Abstract
This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural, synthetic datasets published recently. In particular, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K pairs of seismic data and velocity maps in total. Our experiments demonstrate that larger datasets lead to better performance and generalization of deep learning models for FWI. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement.
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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Jin, P., Feng, Y., Feng, S., Wang, H., Chen, Y., Consolvo, B., Liu, Z., & Lin, Y.
- Affiliations
- Pennsylvania State University (US)
- Los Alamos National Laboratory (US)
- Google (United States) (US)
- Intel (United States) (US)
- Microsoft (United States) (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Jin, Peng
- Lin, Youzuo
- Venue
- Scientific Reports
- Published
- 2023-12-28
- Volume
- 14
- Issue
- 1
- Pages
- 20034-20034
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11358280
- OpenAlex
- W4401938289
- Cited by
- 10
- References
- 1
- Citations by year
- 2026: 5 · 2025: 5
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- U.S. Department of Energy
- Hill's Pet Nutrition
- Office of Fossil Energy
- University of North Carolina at Chapel Hill
- Topics
- Physical Sciences › Earth and Planetary Sciences › Geophysics › Seismic Imaging and Inversion Techniques
- Physical Sciences › Earth and Planetary Sciences › Geophysics › Seismic Waves and Analysis
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- full waveform inversion, big data, deep learning, synthetic seismic data, model capacity, dataset size, velocity model building, seismic imaging
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