Articles | Volume 10, issue 5
https://doi.org/10.5194/se-10-1651-2019
© Author(s) 2019. 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-10-1651-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Oct 2019
Research article |
| 09 Oct 2019
| 09 Oct 2019
Fault interpretation in seismic reflection data: an experiment analysing the impact of conceptual model anchoring and vertical exaggeration
Department of Structure and Dynamics of the Earth, Institute of
Earth Sciences Jaume Almera, ICTJA-CSIC, Lluis Sole i Sabaris s/n, 08028
Barcelona, Spain
Department of Geology and Petroleum Geology, School of Geosciences,
University of Aberdeen, Aberdeen AB24 3UE, UK
Clare E. Bond
Department of Geology and Petroleum Geology, School of Geosciences,
University of Aberdeen, Aberdeen AB24 3UE, UK
Gareth Johnson
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow G1 1XZ, UK
Armelle Kloppenburg
4DGeo, Daal en Bergselaan 80, 2565 AH The Hague, the Netherlands
Oriol Ferrer
Institut de Recerca Geomodels, Departament de Dinàmica de la
Terra i de l'Oceà, Facultat de Ciències de la Terra, Universitat de
Barcelona, c/ Martí i Franquès s/n, 08028 Barcelona, Spain
Rebecca Bell
Basins Research Group (BRG), Department of Earth Science &
Engineering, Imperial College, Prince Consort Road, London SW7 2BP, UK
Puy Ayarza
Department of Geology, University of Salamanca, 37008 Salamanca,
Spain
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- Petroelastic modeling of well logging and seismic inversion data combining rock physics templates and conventional petrophysical analysis for 1D, 2D, and 3D supervised reservoir characterization of the Stybarrow Field in Exmouth sub-basin inner Carnarvon Basin, Australia M. Dominguez-Cruz et al. https://doi.org/10.1007/s40808-024-02245-3
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- Faulted karst reservoir spaces in Middle-Lower Triassic carbonates, Qingjiang Region, Yangtze block, China S. Liao et al. https://doi.org/10.1016/j.jsg.2024.105273
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- Detailed 3D characterization of fault geometric attributes: Insights from deep learning-based fault imaging in seismic data A. Basa et al. https://doi.org/10.1016/j.jsg.2025.105569
- Deep convolutional neural network for automatic fault recognition from 3D seismic datasets Y. An et al. https://doi.org/10.1016/j.cageo.2021.104776
- Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion Z. Zhang et al. https://doi.org/10.3390/rs16050922
- Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO2 storage site assessment E. Michie et al. https://doi.org/10.1190/INT-2021-0077.1
- Continent‐Ocean Transition or Boundary? Crowd‐Sourced Seismic Interpretations of the East‐India Passive Margin C. Bond et al. https://doi.org/10.1029/2022TC007624
- Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile M. Ercoli et al. https://doi.org/10.3389/feart.2023.1119554
- Mapping faults in 3D seismic data – why the method matters F. Robledo Carvajal et al. https://doi.org/10.1016/j.jsg.2023.104976
- SCAFaultNet: application of a hybrid attention network based on multi-scale feature fusion in seismic fault identification C. Xu et al. https://doi.org/10.1007/s12145-025-01971-1
- Petrophysical appraisal and 3-D structural interpretation of reservoirs in an Onshore Niger Delta Field, Southeastern Nigeria M. Akpan et al. https://doi.org/10.1007/s42108-022-00218-9
22 citations as recorded by crossref.
- Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review Y. An et al. https://doi.org/10.1016/j.earscirev.2023.104509
- Petroelastic modeling of well logging and seismic inversion data combining rock physics templates and conventional petrophysical analysis for 1D, 2D, and 3D supervised reservoir characterization of the Stybarrow Field in Exmouth sub-basin inner Carnarvon Basin, Australia M. Dominguez-Cruz et al. https://doi.org/10.1007/s40808-024-02245-3
- Fracking bad language – hydraulic fracturing and earthquake risks J. Roberts et al. https://doi.org/10.5194/gc-4-303-2021
- Seismic and outcrop-based 3D characterization of fault damage zones in sandstones, Rio do Peixe Basin, Brazil L. Oliveira et al. https://doi.org/10.1016/j.jsg.2024.105276
- Improving Accuracy in Studying the Interactions of Seismic Waves with Bottom Sediments G. Mitrofanov et al. https://doi.org/10.3390/jmse9020229
- How do differences in interpreting seismic images affect estimates of geological slip rates? W. Hu https://doi.org/10.5194/se-13-1281-2022
- Faulted karst reservoir spaces in Middle-Lower Triassic carbonates, Qingjiang Region, Yangtze block, China S. Liao et al. https://doi.org/10.1016/j.jsg.2024.105273
- 3-D Seismic fault identification using dual-decoder network with multi-feature fusion L. Zeng et al. https://doi.org/10.1007/s12145-026-02103-z
- Seismic Attribute-Based Characterization of Heterogeneous Sand Reservoirs in the Niger Delta Basin, Nigeria O. I. H. et al. https://doi.org/10.52589/AJENSR-UL2U3OM4
- Fault interpretation uncertainties using seismic data, and the effects on fault seal analysis: a case study from the Horda Platform, with implications for CO2 storage E. Michie et al. https://doi.org/10.5194/se-12-1259-2021
- Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication L. Pérez-Díaz et al. https://doi.org/10.5194/se-11-889-2020
- A systematic review of deep learning for structural geological interpretation G. Fernandes et al. https://doi.org/10.1007/s10618-024-01079-y
- Imaging along-strike variability in fault structure; insights from seismic modelling of the Maghlaq Fault, Malta V. Dimmen et al. https://doi.org/10.1016/j.marpetgeo.2024.106891
- Detailed 3D characterization of fault geometric attributes: Insights from deep learning-based fault imaging in seismic data A. Basa et al. https://doi.org/10.1016/j.jsg.2025.105569
- Deep convolutional neural network for automatic fault recognition from 3D seismic datasets Y. An et al. https://doi.org/10.1016/j.cageo.2021.104776
- Improving Seismic Fault Recognition with Self-Supervised Pre-Training: A Study of 3D Transformer-Based with Multi-Scale Decoding and Fusion Z. Zhang et al. https://doi.org/10.3390/rs16050922
- Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO2 storage site assessment E. Michie et al. https://doi.org/10.1190/INT-2021-0077.1
- Continent‐Ocean Transition or Boundary? Crowd‐Sourced Seismic Interpretations of the East‐India Passive Margin C. Bond et al. https://doi.org/10.1029/2022TC007624
- Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile M. Ercoli et al. https://doi.org/10.3389/feart.2023.1119554
- Mapping faults in 3D seismic data – why the method matters F. Robledo Carvajal et al. https://doi.org/10.1016/j.jsg.2023.104976
- SCAFaultNet: application of a hybrid attention network based on multi-scale feature fusion in seismic fault identification C. Xu et al. https://doi.org/10.1007/s12145-025-01971-1
- Petrophysical appraisal and 3-D structural interpretation of reservoirs in an Onshore Niger Delta Field, Southeastern Nigeria M. Akpan et al. https://doi.org/10.1007/s42108-022-00218-9
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