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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Cited
27 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
- What lies beyond seismic reflection data? Interpretation of a salt-related fault based on combined seismic datasets (Santos Basin, Brazil) P. Bauli et al. https://doi.org/10.1016/j.jsg.2026.105827
- 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
- How do differences in interpreting seismic images affect estimates of geological slip rates? W. Hu https://doi.org/10.5194/se-13-1281-2022
- 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
- A systematic review of deep learning for structural geological interpretation G. Fernandes et al. https://doi.org/10.1007/s10618-024-01079-y
- 生成式人工智能在地震信号处理和成像中的现状及展望 琦. 张 et al. https://doi.org/10.1360/SSTe-2025-0265
- 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
- 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
- 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
- Enhancing fault delineation in highly structured carbonate reservoirs using PSF-convolved synthetic data, deep learning, and seismic preconditioning M. Nilo et al. https://doi.org/10.1190/tle-2026-1083
- 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
- Generative artificial intelligence for seismic signal processing and imaging: Current status and future perspectives Q. Zhang et al. https://doi.org/10.1007/s11430-025-2038-7
- 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
- Characterisation of Subsurface Dynamic Parameters through Spatial Analysis Utilising Three-Dimensional Modelling in North Bengkulu, Indonesia L. Mase et al. https://doi.org/10.1007/s40515-026-01028-w
- 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
- 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
- Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication L. Pérez-Díaz et al. https://doi.org/10.5194/se-11-889-2020
- 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
- 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
- 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
27 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
- What lies beyond seismic reflection data? Interpretation of a salt-related fault based on combined seismic datasets (Santos Basin, Brazil) P. Bauli et al. https://doi.org/10.1016/j.jsg.2026.105827
- 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
- How do differences in interpreting seismic images affect estimates of geological slip rates? W. Hu https://doi.org/10.5194/se-13-1281-2022
- 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
- A systematic review of deep learning for structural geological interpretation G. Fernandes et al. https://doi.org/10.1007/s10618-024-01079-y
- 生成式人工智能在地震信号处理和成像中的现状及展望 琦. 张 et al. https://doi.org/10.1360/SSTe-2025-0265
- 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
- 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
- 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
- Enhancing fault delineation in highly structured carbonate reservoirs using PSF-convolved synthetic data, deep learning, and seismic preconditioning M. Nilo et al. https://doi.org/10.1190/tle-2026-1083
- 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
- Generative artificial intelligence for seismic signal processing and imaging: Current status and future perspectives Q. Zhang et al. https://doi.org/10.1007/s11430-025-2038-7
- 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
- Characterisation of Subsurface Dynamic Parameters through Spatial Analysis Utilising Three-Dimensional Modelling in North Bengkulu, Indonesia L. Mase et al. https://doi.org/10.1007/s40515-026-01028-w
- 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
- 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
- Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication L. Pérez-Díaz et al. https://doi.org/10.5194/se-11-889-2020
- 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
- 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
- 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
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