The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Automated fault detection without seismic processing
Open accessAbstract
For hydrocarbon exploration, large volumes of data are acquired and used in physical modeling-based workflows to identify geologic features of interest such as fault networks, salt bodies, or, in general, elements of petroleum systems. The adjoint modeling step, which transforms the data into the model space, and subsequent interpretation can be very expensive, both in terms of computing resources and domain-expert time. We propose and implement a unique approach that bypasses these demanding steps, directly assisting interpretation. We do this by training a deep neural network to learn a mapping relationship between the data space and the final output (particularly, spatial points indicating fault presence). The key to obtaining accurate predictions is the use of the Wasserstein loss function, which properly handles the structured output — in our case, by exploiting fault surface continuity. The promising results shown here for synthetic data demonstrate a new way of using seismic data and suggest more direct methods to identify key elements in the subsurface.
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Cites
Multilayer feedforward networks are universal approximators
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Multilayer feedforward networks are universal approximators
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Why Does Deep and Cheap Learning Work So Well?Preprint
Lin, H. W., Tegmark, M., & Rolnick, D. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 526
Why Does Deep and Cheap Learning Work So Well?
Lin, H. W., Tegmark, M., & Rolnick, D. (2017) · Journal of Statistical Physics · doi:10.1007/s10955-017-1836-5 · Cited by 393
Hale, D. (2013) · Geophysics · doi:10.1190/geo2012-0331.1 · Cited by 378
Learning with a Wasserstein Loss
Frogner, C., Zhang, C., Mobahi, H., Araya‐Polo, M., & Poggio, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.05439 · Cited by 337
Self-citationNeuroAnimator: fast neural network emulation and control of physics-based models
Grzeszczuk, R., Terzopoulos, D., & Hinton, G. (1998) · Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH '98 · doi:10.1145/280814.280816 · Cited by 293
Structured Learning and Prediction in Computer Vision
Nowozin, S. (2010) · now publishers, Inc. eBooks · doi:10.1561/9781601984579 · Cited by 239
Structured Learning and Prediction in Computer Vision
Nowozin, S., & Lampert, C. H. (2011) · Foundations and Trends in Computer Graphics and Vision · doi:10.1561/0600000033 · Cited by 178
Machine-learning Based Automated Fault Detection in Seismic Traces
Zhang, C., Frogner, C., Araya‐Polo, M., et al. (2014) · Proceedings · doi:10.3997/2214-4609.20141500 · Cited by 83
Self-citation
Cited by
Araya-Polo, M., Jennings, J., Adler, A., & Dahlke, T. (2018) · The Leading Edge · doi:10.1190/tle37010058.1 · Cited by 558
Self-citationAn empirical study of large-scale data-driven full waveform inversion
Jin, P., Feng, Y., Feng, S., Wang, H., Chen, Y., Consolvo, B., Liu, Z., & Lin, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-68573-7 · Cited by 10
Metadata
- Authors
- Araya-Polo, M., Dahlke, T., Frogner, C., Zhang, C., Poggio, T., & Hohl, D.
- Venue
- The Leading Edge
- Publisher
- Society of Exploration Geophysicists
- Published
- 2017-03-01
- Volume
- 36
- Issue
- 3
- Pages
- 208-214
- ISSN
- 1070-485X (ISSN-L)
- OpenAlex
- W2592421213
- Cited by
- 257
- References
- 14
- FWCI
- 23.63
- Type
- journal article
- Language
- en
- Open access
- green
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