Abraham N, Khan N M. 2019. A novel focal tversky loss function with improved attention U-Net for lesion segmentation. //2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy: IEEE, 683-687. Cited in this article [1]

AlBinHassan N M, Marfurt K. 2003. Fault detection using Hough transforms. //SEG Technical Program Expanded Abstracts 2003. SEG, 1719-1721. Cited in this article [2]

Al-Dossary S, Marfurt K J. 2003. Improved 3-D seismic edge-detection filter applied to Vinton Dome Louisiana. //SEG Technical Program Expanded Abstracts. SEG, 2370-2372. Cited in this article [2]

Al-Dossary S , Marfurt K J . 3D volumetric multispectral estimates of reflector curvature and rotation. Geophysics, 2006, 71 (5): P41- P51. https://doi.org/10.1190/1.2242449 Cited in this article [1]

An Y , Guo J L , Ye Q , et al. Deep convolutional neural network for automatic fault recognition from 3D seismic datasets. Computers & Geosciences, 2021a, 153: 104776 https://doi.org/10.1016/j.cageo.2021.104776 Cited in this article [7]

An Y , Guo J L , Ye Q , et al. A gigabyte interpreted seismic dataset for automatic fault recognition. Data in Brief, 2021b, 37: 107219 https://doi.org/10.1016/j.dib.2021.107219 Cited in this article [2]

An Y , Du H W , Ma S T , et al. Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review. Earth-Science Reviews, 2023, 243: 104509 https://doi.org/10.1016/j.earscirev.2023.104509 Cited in this article [1]

Bahorich M , Farmer S . 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube. The Leading Edge, 1995, 14 (10): 1053- 1058. https://doi.org/10.1190/1.1437077 Cited in this article [1]

Bi Z F , Wu X M , Geng Z C , et al. Deep relative geologic time: A deep learning method for simultaneously interpreting 3-D seismic horizons and faults. Journal of Geophysical Research: Solid Earth, 2021, 126 (9): e2021JB021882 https://doi.org/10.1029/2021JB021882 Cited in this article [3]

Chang D K , Yong X S , Wang Y H , et al. Seismic fault interpretation based on deep convolutional neural networks. Oil Geophysical Prospecting, 2021, 56 (1): 1- 8. https://doi.org/10.13810/j.cnki.issn.1000-7210.2021.01.001

Chang D K , Zhang G Z , Yong X S , et al. Deep learning using synthetic seismic data by Fourier domain adaptation in seismic structure interpretation. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 8030705 https://doi.org/10.1109/LGRS.2022.3218911 Cited in this article [2]

Chen G , Liu Y . Research progress of automatic fault recognition based on artificial intelligence. Progress in Geophysics, 2021, 36 (1): 119- 131. https://doi.org/10.6038/pg2021EE0252

Chopra S , Marfurt K J . Emerging and future trends in seismic attributes. The Leading Edge, 2008, 27 (3): 298- 318. https://doi.org/10.1190/1.2896620 Cited in this article [2]

Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 2016. 3D U-Net: Learning dense volumetric segmentation from sparse annotation. //19th International Conference on Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016. Athens, Greece: Springer International Publishing, 424-432.

Cunha A , Pochet A , Lopes H , et al. Seismic fault detection in real data using transfer learning from a convolutional neural network pre-trained with synthetic seismic data. Computers & Geosciences, 2020, 135: 104344 https://doi.org/10.1016/j.cageo.2019.104344 Cited in this article [6]

Di H B , Gao D L . Gray-level transformation and Canny edge detection for 3D seismic discontinuity enhancement. Computers & Geosciences, 2014, 72: 192- 200. Cited in this article [2]

Di H B, Shafiq M A, AlRegib G. 2017. Seismic-fault detection based on multiattribute support vector machine analysis. //SEG Technical Program Expanded Abstracts. SEG, 2039-2044. Cited in this article [1]

Di H B, Shafiq M, AlRegib G. 2018a. Patch-level MLP classification for improved fault detection. //SEG Technical Program Expanded Abstracts 2018. SEG, 2211-2215. Cited in this article [1]

Di H B, Wang Z, AlRegib G. 2018b. Why using CNN for seismic interpretation? An investigation. //SEG Technical Program Expanded Abstracts. SEG, 2216-2220. Cited in this article [2]

Di H B, Wang Z, AlRegib G. 2018c. Seismic fault detection from post-stack amplitude by convolutional neural networks. //80th EAGE Conference and Exhibition 2018. European Association of Geoscientists & Engineers, 2018: 1-5. Cited in this article [5]

Di H B , Gao D L , AlRegib G . Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification. Geophysical Journal International, 2019a, 218 (2): 1262- 1275. https://doi.org/10.1093/gji/ggz226 Cited in this article [1]

Di H B , Shafiq M A , Wang Z , et al. Improving seismic fault detection by super-attribute-based classification. Interpretation, 2019b, 7 (3): SE251- SE267. https://doi.org/10.1190/INT-2018-0188.1 Cited in this article [1]

Dou Y M , Li K W , Zhu J B , et al. Attention-based 3-D seismic fault segmentation training by a few 2-D slice labels. IEEE Transactions on Geoscience and Remote Sensing, 2022a, 60: 5906715 https://doi.org/10.1109/TGRS.2021.3113676 Cited in this article [2]

Dou Y M , Li K W , Zhu J B , et al. MD loss: Efficient training of 3-D seismic fault segmentation network under sparse labels by weakening anomaly annotation. IEEE Transactions on Geoscience and Remote Sensing, 2022b, 60: 5919014 https://doi.org/10.1109/TGRS.2022.3196810 Cited in this article [6]

Dou Y M , Li K W , Dong M H , et al. FaultSSL: Seismic fault detection via semisupervised learning. Geophysics, 2024, 89 (3): M79- M91. https://doi.org/10.1190/geo2023-0550.1 Cited in this article [3]

Dou Y M , Li K W . 3D seismic fault detection via contrastive-reconstruction representation learning. Expert Systems with Applications, 2024, 249: 123617 https://doi.org/10.1016/j.eswa.2024.123617

Du H W , An Y , Ye Q , et al. Disentangling noise patterns from seismic images: Noise reduction and style transfer. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4513214 https://doi.org/10.1109/TGRS.2022.3219117 Cited in this article [2]

Faleide T S , Braathen A , Lecomte I , et al. Impacts of seismic resolution on fault interpretation: Insights from seismic modelling. Tectonophysics, 2021, 816: 229008 https://doi.org/10.1016/j.tecto.2021.229008 Cited in this article [1]

Gao K , Huang L J , Zheng Y C . Fault detection on seismic structural images using a nested residual U-Net. IEEE Transactions on Geoscience and Remote Sensing, 2022a, 60: 4502215 https://doi.org/10.1109/TGRS.2021.3073840 Cited in this article [3]

Gao K , Huang L J , Zheng Y C , et al. Automatic fault detection on seismic images using a multiscale attention convolutional neural network. Geophysics, 2022b, 87 (1): N13- N29. https://doi.org/10.1190/geo2020-0945.1 Cited in this article [3]

Gersztenkorn A , Marfurt K J . Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping. Geophysics, 1999, 64 (5): 1468- 1479. https://doi.org/10.1190/1.1444651 Cited in this article [1]

Guillon S, Joncour F, Goutorbe P, et al. 2019. Reducing training dataset bias for automatic fault detection. //SEG Technical Program Expanded Abstracts. SEG, 2423-2427. Cited in this article [1]

Guitton A, Wang H, Trainor-Guitton W. 2017. Statistical imaging of faults in 3D seismic volumes using a machine learning approach. //SEG Technical Program Expanded Abstracts 2017. SEG, 2045-2049. Cited in this article [1]

Guitton A. 2018. 3D convolutional neural networks for fault interpretation. //80th EAGE Conference and Exhibition 2018. European Association of Geoscientists & Engineers, 2018: 1-5. Cited in this article [2]

Guo B W, Li L, Luo Y. 2018. A new method for automatic seismic fault detection using convolutional neural network. //SEG Technical Program Expanded Abstracts. SEG, 1951-1955. Cited in this article [1]

Guo L , Xiong R , Zhao J L , et al. Seismic fault identification based on multi-scale dense convolution and improved long short-term memory network. IEEE Access, 2023, 11: 124114- 124128. https://doi.org/10.1109/ACCESS.2023.3328630 Cited in this article [2]

Hale D . Methods to compute fault images, extract fault surfaces, and estimate fault throws from 3D seismic images. Geophysics, 2013, 78 (2): O33- O43. https://doi.org/10.1190/geo2012-0331.1 Cited in this article [1]

Han T J , Ding R W , Zhao S , et al. Algorithm for intelligent recognition low-grade seismic faults using codec target edges. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5907912 https://doi.org/10.1109/TGRS.2023.3267616 Cited in this article [2]

He Y L , Wen X T , Wang J T , et al. Fault recognition based on 3D U-Net ++ L3 convolutional neural network. Progress in Geophysics, 2022, 37 (2): 607- 616. https://doi.org/10.6038/pg2022FF0284

Hu H T , Lian W X , Su R , et al. Stylization of a seismic image profile based on a convolutional neural network. Energies, 2022, 15 (16): 6039 https://doi.org/10.3390/en15166039 Cited in this article [1]

Jing J K , Yan Z , Zhang Z , et al. Fault detection using a convolutional neural network trained with point-spread function-convolution-based samples. Geophysics, 2023, 88 (1): IM1- IM14. https://doi.org/10.1190/geo2021-0824.1 Cited in this article [3]

Jun Park M, Jennings J, Clapp B, et al. 2022. Realistic synthetic data generation using neural style transfer: Application to automatic fault interpretation. //SEG Technical Program Expanded Abstracts. SEG, 1714-1718. Cited in this article [1]

Kaur H , Zhang Q , Witte P , et al. Deep-learning-based 3D fault detection for carbon capture and storage. Geophysics, 2023, 88 (4): IM101- IM112. https://doi.org/10.1190/geo2022-0755.1 Cited in this article [1]

Li B , Li Y E . Neural network-based CO2 Interpretation from 4D sleipner seismic images. Journal of Geophysical Research: Solid Earth, 2021, 126 (12): e2021JB022524 https://doi.org/10.1029/2021JB022524 Cited in this article [1]

Li J T , Wu X M , Hu Z X . Deep learning for simultaneous seismic image super-resolution and denoising. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5901611 https://doi.org/10.1109/TGRS.2021.3057857 Cited in this article [3]

Li S R , Yang C C , Sun H , et al. Seismic fault detection using an encoder-decoder convolutional neural network with a small training set. Journal of Geophysics and Engineering, 2019, 16 (1): 175- 189. https://doi.org/10.1093/jge/gxy015 Cited in this article [1]

Li T T , Hou S Y , Ma S Z , et al. Overview and research progress of fault identification method. Progress in Geophysics, 2018, 33 (4): 1507- 1514. https://doi.org/10.6038/pg2018BB0311

Li X , Li K W , Xu Z F , et al. Fault-Seg-Net: A method for seismic fault segmentation based on multi-scale feature fusion with imbalanced classification. Computers and Geotechnics, 2023, 158: 105412 https://doi.org/10.1016/j.compgeo.2023.105412 Cited in this article [1]

Li X , Li K W . Fault-Attri-Attention: A method for fault identification based on seismic attributes attention. Neural Computing and Applications, 2024, 36 (7): 3645- 3661. https://doi.org/10.1007/s00521-023-09265-7 Cited in this article [1]

Lin L , Zhong Z , Cai Z X , et al. Automatic geologic fault identification from seismic data using 2.5D channel attention U-net. Geophysics, 2022, 87 (4): IM111- IM124. https://doi.org/10.1190/geo2021-0805.1 Cited in this article [2]

Lin T Y, Goyal P, Girshick R, et al. 2017. Focal loss for dense object detection. //Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2999-3007. Cited in this article [1]

Liu N H , Li S Z , Huang T , et al. Seismic fault Interpretation based on improved holistically-nested edge detection. Oil Geophysical Prospecting, 2022, 57 (3): 499- 509. https://doi.org/10.13810/j.cnki.issn.1000-7210.2022.03.001

Lu F M , Meng R G , Zhang J H , et al. Research of complex fault recognition method based on UNet ++ network and transfer learning technique. Progress in Geophysics, 2022, 37 (3): 1100- 1111. https://doi.org/10.6038/pg2022FF0122 Cited in this article [1]

Ma X , Yao G , Zhang F , et al. 3-D seismic fault detection using recurrent convolutional neural networks with compound loss. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5909815 https://doi.org/10.1109/TGRS.2023.3275951 Cited in this article [9]

Ma X , Yao G , Zhang F , et al. Fault detection method based on pixel difference network. Chinese Journal of Geophysics, 2023, 66 (4): 1649- 1663. https://doi.org/10.6038/cjg2022Q0452

Marfurt K J , Kirlin R L , Farmer S L , et al. 3-D seismic attributes using a semblance-based coherency algorithm. Geophysics, 1998, 63 (4): 1150- 1165. https://doi.org/10.1190/1.1444415 Cited in this article [1]

Marfurt K J . Robust estimates of 3D reflector dip and azimuth. Geophysics, 2006, 71 (4): P29- P40. https://doi.org/10.1190/1.2213049 Cited in this article [1]

Milletari F, Navab N, Ahmadi S A. 2016. V-net: Fully convolutional neural networks for volumetric medical image segmentation. //2016 Fourth International Conference on 3D Vision (3DV). Stanford, CA, USA: IEEE, 565-571. Cited in this article [1]

Mustafa A , Rastegar R , Brown T , et al. Visual attention-guided learning with incomplete labels for seismic fault interpretation. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5908012 https://doi.org/10.1109/TGRS.2024.3370037 Cited in this article [2]

Pedersen S I, Randen T, Sonneland L, et al. 2002. Automatic fault extraction using artificial ants. //SEG Technical Program Expanded Abstracts 2002. SEG, 512-515. Cited in this article [1]

Pham N, Dunlap D, Fomel S. 2021. Channel facies and faults multisegmentation in seismic volumes. //SEG Technical Program Expanded Abstracts. SEG, 1430-1434. Cited in this article [1]

Pham N , Fomel S . Seismic data augmentation for automatic fault picking using deep learning. Geophysical Prospecting, 2024, 72 (1): 125- 141. https://doi.org/10.1111/1365-2478.13245 Cited in this article [1]

Pochet A , Diniz P H B , Lopes H , et al. Seismic fault detection using convolutional neural networks trained on synthetic poststacked amplitude maps. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (3): 352- 356. https://doi.org/10.1109/LGRS.2018.2875836 Cited in this article [1]

Qi J , Machado G , Marfurt K . A workflow to skeletonize faults and stratigraphic features. Geophysics, 2017, 82 (4): O57- O70. https://doi.org/10.1190/geo2016-0641.1 Cited in this article [1]

Qi J, Lyu B, Wu X M, et al. 2020. Comparing convolutional neural networking and image processing seismic fault detection methods. //SEG Technical Program Expanded Abstracts. SEG, 1111-1115. Cited in this article [1]

Randen T, Monsen E, Signer C, et al. 2000. Three-dimensional texture attributes for seismic data analysis. //SEG Technical Program Expanded Abstracts 2000. SEG, 668-671. Cited in this article [1]

Randen T, Pedersen S I, Sønneland L. 2001. Automatic extraction of fault surfaces from three-dimensional seismic data. //SEG Technical Program Expanded Abstracts 2001. SEG, 551-554. Cited in this article [1]

Roberts A . Curvature attributes and their application to 3D interpreted horizons. First Break, 2001, 19 (2): 85- 100. https://doi.org/10.1046/j.0263-5046.2001.00142.x Cited in this article [1]

Salehi S S M, Erdogmus D, Gholipour A. 2017. Tversky loss function for image segmentation using 3D fully convolutional deep networks. //8th International Workshop on Machine Learning in Medical Imaging. Quebec City, QC, Canada: Springer International Publishing, 379-387. Cited in this article [1]

Silva C C, Marcolino C S, Lima F D. 2005. Automatic fault extraction using ant tracking algorithm in the Marlim South Field, Campos Basin. //SEG Technical Program Expanded Abstracts 2005. SEG, 857-860. Cited in this article [1]

Tang Z X , Wu B Y , Wu W H , et al. Fault detection via 2.5D Transformer U-Net with seismic data pre-processing. Remote Sensing, 2023, 15 (4): 1039 https://doi.org/10.3390/rs15041039 Cited in this article [6]

Van Bemmel P P, Pepper R E F. 2000. Seismic signal processing method and apparatus for generating a cube of variance values. U.S., 6151555. Cited in this article [1]

Wang J , Zhang J H , Lu F M , et al. Research on fault detection method based on 3D deeply supervised network. Oil Geophysical Prospecting, 2021, 56 (5): 947- 957. https://doi.org/10.13810/j.cnki.issn.1000-7210.2021.05.002

Wang S H , Si X , Cai Z X , et al. Structural augmentation in seismic data for fault prediction. Applied Sciences, 2022a, 12 (19): 9796 https://doi.org/10.3390/app12199796 Cited in this article [1]

Wang Z R , Li B , Liu N H , et al. Distilling knowledge from an ensemble of convolutional neural networks for seismic fault detection. IEEE Geoscience and Remote Sensing Letters, 2022b, 19: 7500805 https://doi.org/10.1109/LGRS.2020.3034960 Cited in this article [1]

Wang Z W , You J C , Liu W , et al. Transformer assisted dual U-net for seismic fault detection. Frontiers in Earth Science, 2023, 11: 1047626 https://doi.org/10.3389/feart.2023.1047626 Cited in this article [1]

Wei X L , Zhang C X , Kim S W , et al. Seismic fault detection using convolutional neural networks with focal loss. Computers & Geosciences, 2022, 158: 104968 https://doi.org/10.1016/j.cageo.2021.104968 Cited in this article [3]

Wu W H , Yang Y , Wu B Y , et al. MTL-FaultNet: Seismic data reconstruction assisted multitask deep learning 3-D fault Interpretation. IEEE Transactions on Geoscience and Remote Sensing, 2023a, 61: 5914815 https://doi.org/10.1109/TGRS.2023.3299378 Cited in this article [1]

Wu X M, Shi Y Z, Fomel S, et al. 2018. Convolutional neural networks for fault Interpretation in seismic images. //SEG Technical Program Expanded Abstracts. SEG, 1946-1950. Cited in this article [4]

Wu X M , Liang L M , Shi Y Z , et al. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, 2019a, 84 (3): IM35- IM45. https://doi.org/10.1190/geo2018-0646.1 Cited in this article [19]

Wu X M , Shi Y Z , Fomel S , et al. FaultNet3D: Predicting fault probabilities, strikes, and dips with a single convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 2019b, 57 (11): 9138- 9155. https://doi.org/10.1109/TGRS.2019.2925003 Cited in this article [9]

Wu X M , Geng Z C , Shi Y Z , et al. Building realistic structure models to train convolutional neural networks for seismic structural Interpretation. Geophysics, 2020, 85 (4): WA27- WA39. https://doi.org/10.1190/geo2019-0375.1 Cited in this article [6]

Wu X M , Ma J W , Si X , et al. Sensing prior constraints in deep neural networks for solving exploration geophysical problems. Proceedings of the National Academy of Sciences of the United States of America, 2023b, 120 (23): e2219573120 https://doi.org/10.1073/PNAS.2219573120 Cited in this article [4]

Xie S N, Tu Z W. 2015. Holistically-nested edge detection. //Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 1395-1403. Cited in this article [1]

Xiong W , Ji X , Ma Y , et al. Seismic fault detection with convolutional neural network. Geophysics, 2018, 83 (5): O97- O103. https://doi.org/10.1190/geo2017-0666.1 Cited in this article [5]

Yan Z , Zhang Z , Liu S Y . Improving performance of seismic fault detection by fine-tuning the convolutional neural network pre-trained with synthetic samples. Energies, 2021, 14 (12): 3650 https://doi.org/10.3390/en14123650 Cited in this article [2]

Yang D, Cai Y F, Hu G M, et al. 2020. Seismic fault detection based on 3D Unet+ + model. //SEG International Exposition and Annual Meeting. Virtual: SEG, 1631-1635. Cited in this article [5]

Yang J , Ding R W , Lin N T , et al. Research progress of intelligent identification of seismic faults based on deep learning. Progress in Geophysics, 2022, 37 (1): 298- 311. https://doi.org/10.6038/pg2022FF0233

Yang J R , Wu X M , Bi Z F , et al. A multi-task learning method for relative geologic time, horizons, and faults with prior information and transformer. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5907720 https://doi.org/10.1109/TGRS.2023.3264593 Cited in this article [2]

Yang W Y , Yang J R , Chen S Q , et al. Seismic data fault detection based on U-Net deep learning network. Oil Geophysical Prospecting, 2021, 56 (4): 688- 697. https://doi.org/10.13810/j.cnki.issn.1000-7210.2021.04.002

Yang Y C, Soatto S. 2020. FDA: Fourier domain adaptation for semantic segmentation. //Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE, 4084-4094.

Zhang B , Pu Y T , Xu Z H , et al. Exploring factors affecting the performance of deep learning in seismic fault attribute computation. Interpretation, 2022, 10 (4): T619- T636. https://doi.org/10.1190/INT-2022-0007.1 Cited in this article [2]

Zhang M M , Wu B Y , Ma D B , et al. Loss function comparison for fault interpretation of three-dimensional seismic data based on deep neural network. Oil Geophysical Prospecting, 2023, 58 (6): 1299- 1312. https://doi.org/10.13810/j.cnki.issn.1000-7210.2023.06.001 Cited in this article [4]

Zhang Q, Yusifov A, Joy C, et al. 2019. FaultNet: A deep CNN model for 3D automated fault picking. //SEG Technical Program Expanded Abstracts. SEG, 2413-2417. Cited in this article [2]

Zhang Z , Yan Z , Gu H M . Automatic fault recognition with residual network and transfer learning. Oil Geophysical Prospecting, 2020, 55 (5): 950- 956. https://doi.org/10.13810/j.cnki.issn.1000-7210.2020.05.002

Zhang Z , Yan Z , Jing J K , et al. Generating paired seismic training data with cycle-consistent adversarial networks. Remote Sensing, 2023, 15 (1): 265 https://doi.org/10.3390/rs15010265

Zhang Z R , Chen R , Ma J W . Improving seismic fault recognition with self-supervised pre-training: A study of 3D transformer-based with multi-scale decoding and fusion. Remote Sensing, 2024, 16 (5): 922 https://doi.org/10.3390/rs16050922 Cited in this article [3]

Zhao S , Ding R W , Han T J , et al. Fault2SeisGAN: A method for the expansion of fault datasets based on generative adversarial networks. Frontiers in Earth Science, 2023, 11: 1091803 https://doi.org/10.3389/feart.2023.1091803 Cited in this article [1]

Zhao T, Mukhopadhyay P. 2018. A fault-detection workflow using deep learning and image processing. //SEG Technical Program Expanded Abstracts. SEG, 1966-1970. Cited in this article [2]

Zhao T. 2019. 3D convolutional neural networks for efficient fault detection and orientation estimation. //SEG Technical Program Expanded Abstracts. SEG, 2418-2422. Cited in this article [1]

Zhao Y, Yue Y X, Huang J L, et al. 2015. Study and application of three parameters wavelet multi-scale ant tracking technology. //SEG Technical Program Expanded Abstracts 2015. SEG, 1866-1870. Cited in this article [1]

Zhou R S , Yao X M , Hu G M , et al. Learning from unlabelled real seismic data: Fault detection based on transfer learning. Geophysical Prospecting, 2021, 69 (6): 1218- 1234. https://doi.org/10.1111/1365-2478.13097 Cited in this article [2]

Zhou R S , Zhou C , Wang Y J , et al. Frequency adaptive fault detection by feature pyramid network with wavelet transform. Geophysics, 2023, 88 (6): IM113- IM130. https://doi.org/10.1190/geo2022-0549.1 Cited in this article [1]

Zhu D L , Li L , Guo R , et al. 3D fault detection: Using human reasoning to improve performance of convolutional neural networks. Geophysics, 2022, 87 (4): IM143- IM156. https://doi.org/10.1190/geo2020-0905.1 Cited in this article [2]

常 德宽 , 雍 学善 , 王 一惠 , 等. 基于深度卷积神经网络的地震数据断层识别方法. 石油地球物理勘探, 2021, 56 (1): 1- 8. https://doi.org/10.13810/j.cnki.issn.1000-7210.2021.01.001 Cited in this article [3]

陈 桂 , 刘 洋 . 基于人工智能的断层自动识别研究进展. 地球物理学进展, 2021, 36 (1): 119- 131. https://doi.org/10.6038/pg2021EE0252 Cited in this article [1]

何 易龙 , 文 晓涛 , 王 锦涛 , 等. 基于3D U-Net ++ L3卷积神经网络的断层识别. 地球物理学进展, 2022, 37 (2): 607- 616. https://doi.org/10.6038/pg2022FF0284 Cited in this article [1]

李 婷婷 , 侯 思宇 , 马 世忠 , 等. 断层识别方法综述及研究进展. 地球物理学进展, 2018, 33 (4): 1507- 1514. https://doi.org/10.6038/pg2018BB0311 Cited in this article [1]

刘 乃豪 , 李 时桢 , 黄 腾 , 等. 改进的整体嵌套边缘检测地震断层识别技术. 石油地球物理勘探, 2022, 57 (3): 499- 509. https://doi.org/10.13810/j.cnki.issn.1000-7210.2022.03.001 Cited in this article [2]

芦 凤明 , 孟 瑞刚 , 张 军华 , 等. UNet ++和迁移学习相结合的复杂断裂识别方法研究. 地球物理学进展, 2022, 37 (3): 1100- 1111. https://doi.org/10.6038/pg2022FF0122 Cited in this article [4]

马 啸 , 姚 刚 , 张 峰 , 等. 基于像素差分神经网络的断层识别方法. 地球物理学报, 2023, 66 (4): 1649- 1663. https://doi.org/10.6038/cjg2022Q0452 Cited in this article [6]

王 静 , 张 军华 , 芦 凤明 , 等. 构建三维深度监督网络的断层检测方法. 石油地球物理勘探, 2021, 56 (5): 947- 957. https://doi.org/10.13810/j.cnki.issn.1000-7210.2021.05.002 Cited in this article [3]

杨 晶 , 丁 仁伟 , 林 年添 , 等. 基于深度学习的地震断层智能识别研究进展. 地球物理学进展, 2022, 37 (1): 298- 311. https://doi.org/10.6038/pg2022FF0233 Cited in this article [1]

杨 午阳 , 杨 佳润 , 陈 双全 , 等. 基于U-Net深度学习网络的地震数据断层检测. 石油地球物理勘探, 2021, 56 (4): 688- 697. https://doi.org/10.13810/j.cnki.issn.1000-7210.2021.04.002 Cited in this article [3]

张 苗苗 , 吴 帮玉 , 马 德波 , 等. 深度神经网络三维地震资料断层解释损失函数对比. 石油地球物理勘探, 2023, 58 (6): 1299- 1312. https://doi.org/10.13810/j.cnki.issn.1000-7210.2023.06.001 Cited in this article [1]

张 政 , 严 哲 , 顾 汉明 . 基于残差网络与迁移学习的断层自动识别. 石油地球物理勘探, 2020, 55 (5): 950- 956. https://doi.org/10.13810/j.cnki.issn.1000-7210.2020.05.002 Cited in this article [4]