Complex fault system revealed by 3-D seismic reflection data with deep learning and fault network analysis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Understanding where normal faults are located is critical for an accurate assessment of seismic hazard; the successful exploration for, and production of, natural (including low-carbon) resources; and the safe subsurface storage of CO2. Our current knowledge of normal fault systems is largely derived from seismic reflection data imaging, intracontinental rifts and continental margins. However, exploitation of these data sets is limited by interpretation biases, data coverage and resolution, restricting our understanding of fault systems. Applying supervised deep learning to one of the largest offshore 3-D seismic reflection data sets from the northern North Sea allows us to image the complexity of the rift-related fault system. The derived fault score volume allows us to extract almost 8000 individual normal faults of different geometries, which together form an intricate network characterised by a multitude of splays, junctions and intersections. Combining tools from deep learning, computer vision and network analysis allows us to map and analyse the fault system in great detail and in a fraction of the time required by conventional seismic interpretation methods. As such, this study shows how we can efficiently identify and analyse fault systems in increasingly large 3-D seismic data sets.
Date Issued
2023-11-21
Date Acceptance
2023-10-03
Citation
Solid Earth, 2023, 14 (11), pp.1181-1195
ISSN
1869-9510
Publisher
Copernicus Publications
Start Page
1181
End Page
1195
Journal / Book Title
Solid Earth
Volume
14
Issue
11
Copyright Statement
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
License URL
Identifier
http://dx.doi.org/10.5194/se-14-1181-2023
Publication Status
Published
Date Publish Online
2023-11-21