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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Nov 2023
Research article |
| 21 Nov 2023
| 21 Nov 2023
Complex fault system revealed by 3-D seismic reflection data with deep learning and fault network analysis
Department of Earth Science, University of Bergen, Allégaten 41, 5007 Bergen, Norway
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Deutsche Erdwärme, Stephanienstraße 55, 76133 Karlsruhe, Germany
Indranil Pan
Centre for Process Systems Engineering and Centre for Environmental Policy, Imperial College London, SW7 1NE, London, UK
The Alan Turing Institute, British Library, NW1 2DB, London, UK
School of Mathematics, Statistics and Physics, Newcastle University, NE1 7RU, Newcastle, UK
Rebecca E. Bell
Basins Research Group (BRG), Department of Earth Science and Engineering, Imperial College, Prince Consort Road, London, SW7 2BP, UK
Christopher A.-L. Jackson
Department of Earth and Environmental Sciences, University of Manchester, M13 9PY, Manchester, UK
Robert L. Gawthorpe
Department of Earth Science, University of Bergen, Allégaten 41, 5007 Bergen, Norway
Haakon Fossen
Museum of Natural History, University of Bergen, Allégaten 41, 5007 Bergen, Norway
Edoseghe E. Osagiede
Department of Earth Science, University of Bergen, Allégaten 41, 5007 Bergen, Norway
Sascha Brune
GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Institute of Geosciences, University of Potsdam, 14476 Potsdam–Golm, Germany
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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.
We need to understand where faults are to do the following: (1) assess their seismic hazard, (2)...
