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Articles | Volume 14, issue 11
Articles | Volume 14, issue 11
https://doi.org/10.5194/se-14-1181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-14-1181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 11
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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Latest update: 12 May 2024
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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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