Abstract
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Wenyan Zhang, Jian Xin Zou, Xiaoling Cao
Abstract
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crossref
Confidence 100%
ror
Confidence 99%
openalex
Confidence 95%
datacite
No local reference links have been materialized yet.
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Deep convolutional neural network for automatic fault recognition from 3D seismic datasets
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10.1190/geo2018-0646.1 · doi-reference
Seismic fault detection using convolutional neural networks with focal loss
10.1016/j.cageo.2021.104968 · doi-reference
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10.1016/j.jappgeo.2025.106014 · doi-reference
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10.1016/j.jappgeo.2025.105707 · doi-reference
A new graph-based semi-supervised method for surface defect classification
10.1016/j.rcim.2020.102083 · doi-reference
A review of some amplitude-based seismic geometric attributes and their applications
10.1190/int-2021-0136.1 · doi-reference
A survey on kolmogorov-arnold network
10.1145/3743128 · doi-reference
10.1109/cvpr46437.2021.01629
10.1109/cvpr46437.2021.01629 · doi-reference
Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps
10.1109/lgrs.2018.2875836 · doi-reference
Fault detection in seismic data using graph convolutional network: P. Palo et al.
10.1007/s11227-023-05173-8 · doi-reference
Literature review on deep learning for the segmentation of seismic images
10.1016/j.earscirev.2024.104955 · doi-reference
Combining higher-order statistics and array techniques to pick low-energy P-seismic arrivals
10.3390/app15031172 · doi-reference
3-D seismic attributes using a semblance-based coherency algorithm
10.1190/1.1444415 · doi-reference
Automatic geologic fault identification from seismic data using 2.5 D channel attention U-net
10.1190/geo2021-0805.1 · doi-reference
10.1609/aaai.v39i5.32491
10.1609/aaai.v39i5.32491 · 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 · doi-reference
Fault-Seg-LNet: A method for seismic fault identification based on lightweight and dynamic scalable network
10.1016/j.engappai.2023.107316 · 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 · doi-reference
Price predictions of scrap steel for north China via machine learning
10.1080/14765284.2025.2538934 · 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 · 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 · doi-reference
Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping
10.1190/1.1444651 · doi-reference
ConSeisDiff: A conditional diffusion approach to mitigate synthetic-real disparities in seismic fault detection
10.1016/j.jappgeo.2025.105956 · doi-reference
Integrating multiple seismic attributes for fault detection using a new hybrid machine learning
10.1038/s41598-025-26889-y · doi-reference
Coherence based on spectral variance analysis
10.1190/geo2017-0158.1 · doi-reference
FaultSSL: Seismic fault detection via semisupervised learning
10.1190/geo2023-0550.1 · doi-reference
Improving seismic fault detection by super-attribute-based classification
10.1190/int-2018-0188.1 · 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 · doi-reference
3-D seismic discontinuity for faults and stratigraphic features: The coherence cube
10.1190/1.1437077 · doi-reference
Deep convolutional neural network for automatic fault recognition from 3D seismic datasets
10.1016/j.cageo.2021.104776 · doi-reference
Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
10.1016/j.earscirev.2023.104509 · doi-reference