Research graph
References from FaultUKAN: A Kolmogorov–Arnold network-enhanced U-Net for seismic fault detection. Local targets link to admitted publications; unresolved targets remain external evidence.
Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
10.1016/j.earscirev.2023.104509 · 2023 · External reference
Deep convolutional neural network for automatic fault recognition from 3D seismic datasets
10.1016/j.cageo.2021.104776 · 2021 · External reference
3-D seismic discontinuity for faults and stratigraphic features: The coherence cube
10.1190/1.1437077 · 1995 · External reference
Implicit u-kan2. 0: Dynamic, efficient and interpretable medical image segmentation
2025 · External reference
Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data
10.1016/j.cageo.2019.104344 · 2020 · External reference
Improving seismic fault detection by super-attribute-based classification
10.1190/int-2018-0188.1 · 2019 · External reference
FaultSSL: Seismic fault detection via semisupervised learning
10.1190/geo2023-0550.1 · 2024 · External reference
Coherence based on spectral variance analysis
10.1190/geo2017-0158.1 · 2018 · External reference
Integrating multiple seismic attributes for fault detection using a new hybrid machine learning
10.1038/s41598-025-26889-y · 2025 · External reference
ConSeisDiff: A conditional diffusion approach to mitigate synthetic-real disparities in seismic fault detection
10.1016/j.jappgeo.2025.105956 · 2025 · External reference
Fault detection on seismic structural images using a nested residual U-Net
2021 · External reference
Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping
10.1190/1.1444651 · 1999 · External reference
Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm
10.1190/int-2023-0025.1 · 2024 · External reference
Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand
10.1007/s40948-023-00646-9 · 2023 · External reference
Price predictions of scrap steel for north China via machine learning
10.1080/14765284.2025.2538934 · 2025 · External reference
Contemporaneous causal analysis of housing prices across Guangdong’s major cities: Employing vector error-correction modeling and directed acyclic graphs
10.1142/s1752890926500042 · 2026 · External reference
Overview and research progress of fault identification method
2018 · External reference
Fault-Seg-LNet: A method for seismic fault identification based on lightweight and dynamic scalable network
10.1016/j.engappai.2023.107316 · 2024 · External reference
Fault-Seg-Net: A method for seismic fault segmentation based on multi-scale feature fusion with imbalanced classification
10.1016/j.compgeo.2023.105412 · 2023 · External reference
10.1609/aaai.v39i5.32491
10.1609/aaai.v39i5.32491 · External reference
Automatic geologic fault identification from seismic data using 2.5 D channel attention U-net
10.1190/geo2021-0805.1 · 2022 · External reference
Unresolved reference
External reference
Research of complex fault recognition method based on UNet++ network and transfer learning technique
2022 · External reference
Improved fault automatic identification using Signal-to-noise ratio cubes
2014 · External reference
3-D seismic attributes using a semblance-based coherency algorithm
10.1190/1.1444415 · 1998 · External reference
Combining higher-order statistics and array techniques to pick low-energy P-seismic arrivals
10.3390/app15031172 · 2025 · External reference
Literature review on deep learning for the segmentation of seismic images
10.1016/j.earscirev.2024.104955 · 2024 · External reference
Unresolved reference
2018 · External reference
Fault detection in seismic data using graph convolutional network: P. Palo et al.
10.1007/s11227-023-05173-8 · 2023 · External reference
Seismic fault analysis using graph signal regularization
2021 · External reference
Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps
10.1109/lgrs.2018.2875836 · 2018 · External reference
U-net: Convolutional networks for biomedical image segmentation
2015 · External reference
10.1109/cvpr46437.2021.01629
10.1109/cvpr46437.2021.01629 · External reference
A survey on kolmogorov-arnold network
10.1145/3743128 · 2025 · External reference
A review of some amplitude-based seismic geometric attributes and their applications
10.1190/int-2021-0136.1 · 2022 · External reference
A new graph-based semi-supervised method for surface defect classification
10.1016/j.rcim.2020.102083 · 2021 · External reference
AttentionFaultFormer: An attention-enhanced 3D CNN & transformer model for seismic fault detection
10.1016/j.jappgeo.2025.105707 · 2025 · External reference
Transformer assisted dual U-net for seismic fault detection
2023 · External reference
Enhancing seismic fault segmentation for geological and engineering applications using the Boundary Deformable Convolutional Network
10.1016/j.jappgeo.2025.106014 · 2026 · External reference
Seismic fault detection using convolutional neural networks with focal loss
10.1016/j.cageo.2021.104968 · 2022 · External reference
FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation
10.1190/geo2018-0646.1 · 2019 · External reference
Automatic seismic fault identification based on an improved U-Net network
10.1007/s11600-023-01200-7 · 2024 · External reference
Seismic fault detection with convolutional neural network
10.1190/geo2017-0666.1 · 2018 · External reference
Individual time series and composite forecasting of the Chinese stock index
2021 · External reference
Steel price index forecasting through neural networks: The composite index, long products, flat products, and rolled products
10.1007/s13563-022-00357-9 · 2023 · External reference
The calculation method of curvature attributes and its effect analysis
2015 · External reference
Seismic data fault detection based on U-Net deep learning network
2021 · External reference
A fault detection workflow using deep learning and image processing
2018 · External reference
Seismic horizon tracking based on the TransUnet model
10.1190/geo2023-0626.1 · 2025 · External reference
Oil production forecasting using temporal Kolmogorov–Arnold networks
2025 · External reference
Fault detection in seismic data using graph convolutional network: P. Palo et al.
10.1007/s11227-023-05173-8 · ExternalCitation · doi-reference
Automatic seismic fault identification based on an improved U-Net network
10.1007/s11600-023-01200-7 · ExternalCitation · doi-reference
Steel price index forecasting through neural networks: The composite index, long products, flat products, and rolled products
10.1007/s13563-022-00357-9 · ExternalCitation · doi-reference
Unsupervised machine learning and multi-seismic attributes for fault and fracture network interpretation in the Kerry Field, Taranaki Basin, New Zealand
10.1007/s40948-023-00646-9 · ExternalCitation · doi-reference
Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data
10.1016/j.cageo.2019.104344 · ExternalCitation · doi-reference
Deep convolutional neural network for automatic fault recognition from 3D seismic datasets
10.1016/j.cageo.2021.104776 · ExternalCitation · doi-reference
Seismic fault detection using convolutional neural networks with focal loss
10.1016/j.cageo.2021.104968 · ExternalCitation · doi-reference
Fault-Seg-Net: A method for seismic fault segmentation based on multi-scale feature fusion with imbalanced classification
10.1016/j.compgeo.2023.105412 · ExternalCitation · doi-reference
Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
10.1016/j.earscirev.2023.104509 · ExternalCitation · doi-reference
Literature review on deep learning for the segmentation of seismic images
10.1016/j.earscirev.2024.104955 · ExternalCitation · doi-reference
Fault-Seg-LNet: A method for seismic fault identification based on lightweight and dynamic scalable network
10.1016/j.engappai.2023.107316 · ExternalCitation · doi-reference
AttentionFaultFormer: An attention-enhanced 3D CNN & transformer model for seismic fault detection
10.1016/j.jappgeo.2025.105707 · ExternalCitation · doi-reference
ConSeisDiff: A conditional diffusion approach to mitigate synthetic-real disparities in seismic fault detection
10.1016/j.jappgeo.2025.105956 · ExternalCitation · doi-reference
Enhancing seismic fault segmentation for geological and engineering applications using the Boundary Deformable Convolutional Network
10.1016/j.jappgeo.2025.106014 · ExternalCitation · doi-reference
A new graph-based semi-supervised method for surface defect classification
10.1016/j.rcim.2020.102083 · ExternalCitation · doi-reference
Integrating multiple seismic attributes for fault detection using a new hybrid machine learning
10.1038/s41598-025-26889-y · ExternalCitation · doi-reference
Price predictions of scrap steel for north China via machine learning
10.1080/14765284.2025.2538934 · ExternalCitation · doi-reference
10.1109/cvpr46437.2021.01629
10.1109/cvpr46437.2021.01629 · ExternalCitation · doi-reference
Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps
10.1109/lgrs.2018.2875836 · ExternalCitation · doi-reference
Contemporaneous causal analysis of housing prices across Guangdong’s major cities: Employing vector error-correction modeling and directed acyclic graphs
10.1142/s1752890926500042 · ExternalCitation · doi-reference
A survey on kolmogorov-arnold network
10.1145/3743128 · ExternalCitation · doi-reference
3-D seismic discontinuity for faults and stratigraphic features: The coherence cube
10.1190/1.1437077 · ExternalCitation · doi-reference
3-D seismic attributes using a semblance-based coherency algorithm
10.1190/1.1444415 · ExternalCitation · doi-reference
Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping
10.1190/1.1444651 · ExternalCitation · doi-reference
Coherence based on spectral variance analysis
10.1190/geo2017-0158.1 · ExternalCitation · doi-reference
Seismic fault detection with convolutional neural network
10.1190/geo2017-0666.1 · ExternalCitation · doi-reference
FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation
10.1190/geo2018-0646.1 · ExternalCitation · doi-reference
Automatic geologic fault identification from seismic data using 2.5 D channel attention U-net
10.1190/geo2021-0805.1 · ExternalCitation · doi-reference
FaultSSL: Seismic fault detection via semisupervised learning
10.1190/geo2023-0550.1 · ExternalCitation · doi-reference
Seismic horizon tracking based on the TransUnet model
10.1190/geo2023-0626.1 · ExternalCitation · doi-reference
Improving seismic fault detection by super-attribute-based classification
10.1190/int-2018-0188.1 · ExternalCitation · doi-reference
A review of some amplitude-based seismic geometric attributes and their applications
10.1190/int-2021-0136.1 · ExternalCitation · doi-reference
Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm
10.1190/int-2023-0025.1 · ExternalCitation · doi-reference
10.1609/aaai.v39i5.32491
10.1609/aaai.v39i5.32491 · ExternalCitation · doi-reference
Combining higher-order statistics and array techniques to pick low-energy P-seismic arrivals
10.3390/app15031172 · ExternalCitation · doi-reference
