Deep-learning tomography
Abstract
Velocity-model building is a key step in hydrocarbon exploration. The main product of velocity-model building is an initial model of the subsurface that is subsequently used in seismic imaging and interpretation workflows. Reflection or refraction tomography and full-waveform inversion (FWI) are the most commonly used techniques in velocity-model building. On one hand, tomography is a time-consuming activity that relies on successive updates of highly human-curated analysis of gathers. On the other hand, FWI is very computationally demanding with no guarantees of global convergence. We propose and implement a novel concept that bypasses these demanding steps, directly producing an accurate gridding or layered velocity model from shot gathers. Our approach relies on training deep neural networks. The resulting predictive model maps relationships between the data space and the final output (particularly the presence of high-velocity segments that might indicate salt formations). The training task takes a few hours for 2D data, but the inference step (predicting a model from previously unseen data) takes only seconds. The promising results shown here for synthetic 2D data demonstrate a new way of using seismic data and suggest fast turnaround of workflows that now make use of machine-learning approaches to identify key structures in the subsurface.
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Oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces.
“Oil and gas companies have had promising results using ML for subsurface imaging based on raw seismograph traces [31].”
From Tackling climate change with machine learning (2023) · cites this paper as [31]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Araya-Polo, M., Jennings, J., Adler, A., & Dahlke, T.
- Affiliations
- 1 Shell International Exploration & Production Inc.
- Stanford University (US)
- Massachusetts Institute of Technology (US)
- Venue
- The Leading Edge
- Publisher
- Society of Exploration Geophysicists
- Published
- 2018-01-01
- Volume
- 37
- Issue
- 1
- Pages
- 58-66
- ISSN
- 1070-485X (ISSN-L)
- OpenAlex
- W2776585113
- Cited by
- 558
- References
- 29
- Citations by year
- 2026: 55 · 2025: 47 · 2024: 62 · 2023: 74 · 2022: 80 · 2021: 106 · 2020: 61 · 2019: 58 · 2018: 15
- FWCI
- 26.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Leading Edge (publishedVersion)
- 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 › Earth and Planetary Sciences › Geophysics › Geophysical and Geoelectrical Methods
- Keywords
- velocity model building, seismic velocity model, shot gathers, deep neural network, salt structures, petroleum exploration, seismic imaging, layered velocity model, full waveform inversion
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