Articles | Volume 14, issue 11
https://doi.org/10.5194/se-14-1181-2023
https://doi.org/10.5194/se-14-1181-2023
Research article
 | 
21 Nov 2023
Research article |  | 21 Nov 2023

Complex fault system revealed by 3-D seismic reflection data with deep learning and fault network analysis

Thilo Wrona, Indranil Pan, Rebecca E. Bell, Christopher A.-L. Jackson, Robert L. Gawthorpe, Haakon Fossen, Edoseghe E. Osagiede, and Sascha Brune

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Cited articles

Bartholomew, I. D., Peters, J. M., and Powell, C. M.: Regional structural evolution of the North Sea: Oblique slip and the reactivation of basement lineaments, in: Petroleum Geology Conference Proceedings, London, 1109–1122, https://doi.org/10.1144/0041109, 1993. 
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Bissell, R. C., Vasco, D. W., Atbi, M., Hamdani, M., Okwelegbe, M., and Goldwater, M. H.: A full field simulation of the in Salah gas production and CO2 storage project using a coupled geo-mechanical and thermal fluid flow simulator, Energy Proced., 4, 3290–3297, https://doi.org/10.1016/j.egypro.2011.02.249, 2011. 
Bond, C. E.: Uncertainty in structural interpretation: Lessons to be learnt, J. Struct. Geol., 74, 185–200, https://doi.org/10.1016/j.jsg.2015.03.003, 2015. 
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Short summary
We need to understand where faults are to do the following: (1) assess their seismic hazard, (2) explore for natural resources and (3) store CO2 safely in the subsurface. Currently, we still map subsurface faults primarily by hand using seismic reflection data, i.e. acoustic images of the Earth. Mapping faults this way is difficult and time-consuming. Here, we show how to use deep learning to accelerate fault mapping and how to use networks or graphs to simplify fault analyses.
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