Bhaktishree Nayak; Pallavi Nayak

doi:10.26599/JGSE.2025.9280049

doi: 10.26599/JGSE.2025.9280049

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出版历程
- 收稿日期: 2024-08-19
- 录用日期: 2025-03-21
- 网络出版日期: 2025-05-10
- 刊出日期: 2025-06-30

Optimal fault detection from seismic data using intelligent techniques: A comprehensive review of methods

Bhaktishree Nayak^{1
, ,},
Pallavi Nayak²

1.
Government college of Engineering, Keonjhar At-Jamunalia Po-Old Towm Dist-Keonjhar Odisha 758001, India
2.
Ravenshaw University, Cuttack Department of Geology, Ravenshaw University Cuttack Pin-753003, India

More Information

Corresponding author: scholar.bhaktishreenayak@gmail.com

摘要

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Abstract: Seismic data plays a pivotal role in fault detection, offering critical insights into subsurface structures and seismic hazards. Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans. This paper presents a comprehensive review of existing methodologies for fault detection, focusing on the application of Machine Learning (ML) and Deep Learning (DL) techniques to enhance accuracy and efficiency. Various ML and DL approaches are analyzed with respect to fault segmentation, adaptive learning, and fault detection models. These techniques, benchmarked against established seismic datasets, reveal significant improvements over classical methods in terms of accuracy and computational efficiency. Additionally, this review highlights emerging trends, including hybrid model applications and the integration of real-time data processing for seismic fault detection. By providing a detailed comparative analysis of current methodologies, this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies. Ultimately, the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection.
- Seismic data /
- Fault detection /
- Fault Segmentation /
- Machine learning /
- Deep learning /
- Adaptive learning

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Figure 1. Flow diagram of the review process for fault detection from seismic data

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Figure 2. Comparative analysis of the accuracy of existing fault detection techniques

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Figure 3. Comparative analysis of loss of existing fault detection techniques

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Figure 4. Comparative analysis of precision of existing fault detection techniques

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Figure 5. Comparative analysis of F-measure of existing fault detection techniques

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Figure 6. Comparative analysis of Recall of existing fault detection techniques

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Figure 7. Comparative analysis of sensitivity of existing fault detection techniques

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Figure 8. Comparative analysis of specificity of existing fault detection techniques

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Figure 9. Comparative analysis of AUROC of existing fault detection techniques

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Table 1. Comparison of fault segmentation techniques from seismic data

Reference	Techniques used	Significance	Limitations
Wu et al. (2019)	Fully Convolutional Neural Network	Efficient fault segmentation, Automatic feature extraction	Requires expert knowledge for accurate labelling, Time-consuming label creation
Hu et al. (2020)	CNN	Limited training set usage, Reduced training duration, Improved segmentation	Balancing model complexity with resources is challenging
Dou et al. (2021)	3D-CNN	Effective training with limited data, Attention mechanism for noise reduction	Hyperparameter tuning is required, Limited data for attention module
Lefevre et al. (2020)	Analog models	Understanding fault geometry determinants	Difficulty in achieving true scale similarity
Dou et al. (2022)	Fault-Net architecture	Reduction of false negatives, Preserving edge information	Incomplete labelling may lead to inaccurate training
Visini et al. (2020)	FRESH and SUNFISH	Improving PSHA methodologies	Lack of user-friendly interface, Uncertainty handling needs improvement
Li et al. (2023)	Fault-Seg-Net	High precision fault localization, Compound loss for uneven segmentation	Increased computational overhead, Training time prolongation
Lima et al. (2024)	DNFS	Enhanced accurate predictions	Sensitivity to geological transitions
Liu et al. (2020)	CNN	Improved interpretability, Better prediction accuracy	Need for deeper interpretability exploration, Additional domain knowledge integration
Li et al. (2024)	Fault-Seg-LNet	Achieve the tradeoff between model precision and efficiency	Continuous fine-tuning required, Adaptation to changing geological conditions
Khayer et al. (2023)	HOG	Enhances the accuracy of geological object delineation in seismic images	The quality of seismic data and the optimization of HOG parameters affects the system performance
Dou et al. (2024)	FaultSSL	Enhances fault detection by effectively integrating limited labelled data	Constrained by the reliance on sparse and potentially inaccurate 2D slice annotations

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Table 2. Review of fault detection from seismic data using ML algorithms

Reference	Techniques used	Significance	Limitations
Zeng et al. (2021)	SVM, VMD	Intelligent fault diagnosis, noise attenuation, strong relationship between seismic features and faults	Ineffective for non-stationary or complex noise patterns
Martín et al. (2023)	KNN	Interactive 3D fault identification, lithological classification	Struggles with complex fault geometries and heterogeneities
Ashraf et al. (2020)	NN, ACO	Advanced fracture network recognition, fault identification using seismic data	Requires careful parameter tuning for optimization, may not effectively handle all types of faults
Noori et al. (2019)	Gaussian process regression	Fault detection via abnormality identification, fault edge determination	Propagation of uncertainties from GPR into fault detection may lead to false positives or missed detections
Ren et al. (2023)	SVM, PSO	Provided insights into fault exposure conditions for roads and wells in the target coal seam	Lack of true fault existence probability assessment.
Wu et al. (2021)	FCN	Fault segmentation based on FCN, balanced loss function for model optimization	Incorporating physical and geological constraints in model architecture is challenging
Wu et al. (2019)	MTL-CNN	Fault detection, structure-oriented smoothing, seismic normal vector estimation	Designing CNN architectures for improved structural interpretation is challenging
Jang et al. (2023)	PCA, RF	Relationship between fault distribution and controlling factors, efficient RF classification	Challenge in interpreting feature importance due to RF's bias toward correlated features
Gong et al. (2024)	SOM-GWO-SVM	Intelligent data preprocessing, fault identification accuracy improvement	Struggles to capture temporal dynamics of fault patterns and seismic activity evolution
Wang et al. (2020)	CNN	Enhanced fault detection through knowledge amalgamation, student CNN trained on synthetic and field data	Investigation of appropriate training data sets and labels needed for effective fault interpretation
Feng et al. (2022)	LOC-FLOW	Enhances earthquake catalog accuracy and provides high-resolution velocity structures	Effectiveness is constrained by the availability and quality of seismic data from dense station networks
Waheed et al. (2021)	PINNTOMO	Enhances seismic tomography by leveraging physics-informed neural networks	Has extremely complex geological settings

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Table 3. Review of fault detection from seismic data using DL algorithms

Reference	Techniques used	Significance	Limitations
An et al.(2021)	DCNN	Efficient fault recognition methodology outperforms state-of-the-art methods, anticipates small errors	Mitigating label discrepancies, reliable model training
Palo et al.(2023)	Graph Convolutional Network (GCN)	Interpreting faults in seismic data, good accuracy	Lacks feature engineering strategies
Alfarhan et al. (2020)	Encoder-decoder deep neural network	Good detection accuracy, robustness to labelled data scarcity	Lack of uncertainty estimation methods
Bi et al.(2021)	Volume-to-volume neural network	High prediction accuracy, low computing costs	Ineffective for low dip-angle thrust faults
Li et al.(2019)	U-Net	Efficient fault detection with small training sets, increased interpretation efficiency	Class imbalance leads to less accurate fault detection
Xu et al. (2021)	3D convolutional autoencoder	Handling seismic data directly, with good accuracy	Needs optimization of architecture and hyperparameters
Li et al. (2021)	Deep CNN	Enhanced perceived quality, better fault detection	Artifacts, slight overfitting
Wu et al. (2022)	Modified U-Net with dilated convolutions	Improved capacity for multi-scale information, better fault identification	Requires further computational optimization
Lin et al. (2022)	2.5D CAU-net with channel attention mechanism	Efficient utilization of correlation between seismic slices, enhanced fault detection	Model overfitting with a larger cropping approach
Ma et al. (2023)	U-Net and CNN	Accurate multiparameter elastic wave inversion, strong generalizability	Physical limitations, noisy data sensitivity
Vu and Jardani (2022a)	SegNet	Accurately map fracture networks in heterogeneous aquifers using hydraulic tomography data	Not fully capture the complexities of real-world fracture geometries and hydrological conditions
Vu and Jardani(2022b)	HT-XNET	Simultaneously reconstruct transmissivity and storability with improved accuracy	Need in-depth considering limits under variance of the method on aquifer conditions and data

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Table 4. Review of fault detection from seismic data using adaptive learning algorithms

Reference	Techniques used	Significance	Limitations
Zini et al. (2019)	SeisNet	Achieved high F1 score on bright spot recognition, quantifying bright spots, and predicting volume	Further research is needed for processing seismic data, waveform prediction, and performance on larger datasets
Zhou et al. (2021)	Transfer learning with convolutional neural networks	Quick training, produced satisfactory results despite the class imbalance	Inaccuracy in detecting fault discontinuities in 3D space
Cunha et al. (2020)	U-net based on DANN (Domain Adversarial Neural Network)	Improved fault detection accuracy, addressed challenges of real geological situations, noise disturbance, and seismic signal frequency	challenge in finishing fault detection on seismic data with various frequencies
Ao et al. (2021)	Transfer learning	Improved seismic dip estimation accuracy, applicability in real-world scenarios	Difficulty in assessing the reliability of network predictions
Dou et al. (2024)	Tiny Self-Attention and HRNet, contrastive learning	Enhanced representation learning, improved fault detection tasks, addressed memory overflow issues	Challenges in sparse distance matching in 3D high-resolution data
Zhou et al. (2021)	Progressive transfer learning	Enhanced fault detection using real seismic data, improved fault continuity	Difficulty in updating training dataset without introducing biases
Wei et al. (2022)	CNN and transfer learning	Robust fault feature representation learning, effective fault detection	Challenges in tuning focal loss parameters and ensuring effectiveness across different datasets
Li et al. (2024)	Fault-Attri-Attention	Improved fault detection with enhanced accuracy	Reduced efficiency and increased computational overhead due to managing multiple attributes
Mustafa et al. (2024)	3D CNN and Attention-guided training Framework	Enhanced fault prediction with better performance	Lack of deep understanding in modelling and incorporating human visual attention
Zeng et al. (2024)	3D-UNet	Enhanced feature extraction and fault detection, improved accuracy and continuity	Struggles in characterizing low-order faults and fault continuity
Zhang et al. (2022)	Deep Transfer Learning	Significantly accelerates hydraulic fracture imaging through deep transfer learning	Reliance on simplified models that introduce approximation errors
Titos et al. (2023)	Transfer Learning	Enhances real-time volcano tectonic earthquake monitoring through transfer learning	The quality and completeness of the master dataset introduce biases

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Table 5. Review of enhanced fault detection models from seismic data

Reference	Techniques used	Significance	Limitations
Yan et al. (2019)	Forward and backward diffusion	Enhancing fault features while suppressing noise, improving fault-tracking accuracy	Struggles in differentiating actual faults and stratigraphic features in complex geological structures
Mousavi et al. (2022)	Erosion algorithm, Sobel and Laplacian of Gaussian	Potential alternative to conventional fault enhancement methods	Artificial enhancements or suppressions near boundaries affect overall image quality
Lyu et al. (2019)	Structure-oriented filtering	Improved fault identification through coherence enhancement	Introduction of spurious features or oversimplification of complex fault networks
Yan et al. (2021)	Transfer learning	Enhanced fault detection accuracy, particularly for complex fault types	Difficulty in accurately identifying complex fault types such as thrust and listric faults
Laudon et al. (2021)	CNN-SOM	Better outcomes compared to using single ML techniques	Lack of feature design invariant to variations such as noise, resolution, or acquisition parameters
Yuan et al. (2019)	Adaptive spectrum decomposition and super-resolution DL with CNN	Improved fault-detection system with adjustable scale highlighting and high-resolution	Bridging the gap between domains and fostering collaboration
Otchere et al. (2022)	Deep Residual U-net	Respectable fault prediction result, enhanced seismic imaging	Struggles to understand uncertainty inherent in predictions
Zhang et al. (2024)	FaultSeg Swin-UNet Transformer	Improved feature representations, increased recognition accuracy	Adaptability challenges with narrow, elongated, and unevenly distributed fault annotations
Mahadik et al. (2021)	Gradient structure tensor-based coherence	Clearer fault lines with less noise, future goal of creating automated defect detection system	Future integration of DL and ML is needed for complete automation
Isaac et al. (2023)	Dip-steered diffusion filter, DSMF and FEF	Revealing small-scale faults and stratigraphic heterogeneity	Need for improvement in noise suppression while preserving useful signal information
Sheng et al. (2022)	REST and hypoDD	Mechanisms of induced seismicity through fluid diffusion and fault reactivation	Limited by the lack of long-term observational data and potential variability
Feng et al. (2022)	Enhanced Geothermal System (EGS)	Offers a quantitative framework for assessing fault slip potential during geothermal operations	Limited by uncertainties in stress field parameters

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参考文献(72)

Alfarhan M, Deriche M, Maalej A, et al. 2020. Multiple events detection in seismic structures using a novel U-Net variant. In 2020 IEEE International Conference on Image Processing (ICIP), 2900-2904, IEEE. DOI: 10.1109/ICIP40778.2020.9190682.

Alfarhan M, Maalej A, Deriche M. 2020. Concurrent detection of salt domes and faults using resnet with u-net. In 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), 118−122, IEEE. DOI: 10.1109/CDMA47397.2020.00026.

Alfarhan M, Deriche M, Maalej A. 2020. Robust concurrent detection of salt domes and faults in seismic surveys using an improved UNet architecture. IEEE access, 10: 39424−39435. DOI: 10.1109/ACCESS.2020.3043973.

Aloisio A, Battista LD, Alaggio R, et al. 2021. Assessment of structural interventions using Bayesian updating and subspace-based fault detection methods: The case study of S. Maria di Collemaggio basilica, L'Aquila, Italy. Structure and Infrastructure Engineering, 17(2): 141−155. DOI: 10.1080/15732479.2020.1731559.

An Y, Guo J, Ye Q, et al. 2021. Deep convolutional neural network for automatic fault recognition from 3D seismic datasets. Computers & Geosciences, 153: 104776. DOI: 10.1016/j.cageo.2021.104776.

Ao Y, Lu W, Xu P, et al. 2021. Seismic dip estimation with a domain knowledge constrainedtransfer learning approach. IEEE Transactions on Geoscience and Remote Sensing, 60: 1−16. DOI: 10.1109/TGRS.2021.3061438.

Ashraf U, Zhang H, Anees A, et al. 2020. Application of unconventional seismic attributes and unsupervised machine learning for the identification of fault and fracture network. Applied Sciences, 10(11): 3864. DOI: 10.3390/app10113864.

Bi Z, Wu X, Geng Z, et al. 2021. Deep relative geologic time: A deep learning method for simultaneously interpreting 3‐D seismic horizons and faults. Journal of Geophysical Research: Solid Earth, 126(9): e2021JB021882. DOI: 10.1029/2021JB021882.

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

Di H, Li C, Smith S, et al. 2021. Imposing interpretational constraints on a seismic interpretation convolutional neural network. Geophysics, 86(3): IM63−IM71. DOI: 10.1190/geo2020-0449.1.

Dou Y, Li K, Zhu J, et al. 2021. Attention-based 3-D seismic fault segmentation training by afew 2-D slice labels. IEEE Transactions on Geoscience and Remote Sensing, 60: 1−15. DOI: 10.1109/TGRS.2021.3113676.

Dou Y, Li K, Zhu J, et al. 2022. 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, 1−13.

Dou YM, Li KW, Dong MH, et al. 2024. FaultSSL: Seismic fault detection via semisupervised learning. Geophysics, 89(3): M79−M91. DOI: 10.1190/geo2023-0550.1.

Dou YM, Li KW. 2024. 3D seismic fault detection via contrastive-reconstruction representation learning. Expert Systems with Applications, 123617. DOI: 10.1016/j.eswa.2024.123617.

Feng C, Gao G, Zhang S, et al. 2022. Fault slip potential induced by fluid injection in the Matouying enhanced geothermal system (EGS) field, Tangshan seismic region, North China. Natural Hazards and Earth System Sciences, 22(7): 2257−2287. DOI: 10.5194/nhess-22-2257-2022.

Feng T, Zhang M, Xu L, et al. 2022. Machine learning‐based earthquake catalog and tomography characterize the middle‐northern section of the Xiaojiang fault zone. Seismological Society of America, 93(5): 2484−2497. DOI: 10.1785/0220220116.

Gong Y, Zhu C, Zhu G, et al. 2024. Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm. Interpretation, 12(1): B1−B15. DOI: 10.1190/INT-2023-0025.1.

He T, Wu B, Zhu X. 2021. Seismic data consecutively missing trace interpolation based on multistage neural network training process. IEEE Geoscience and Remote Sensing Letters, 19: 1−5. DOI: 10.1109/LGRS.2021.3089585.

Hosseini-Fard E, Roshandel-Kahoo A, Soleimani-Monfared M, et al. 2022. Automatic seismic image segmentation by introducing a novel strategy in histogram of oriented gradients. Journal of Petroleum Science and Engineering, 209: 109971. DOI: 10.1016/j.petrol.2021.109971.

Hu G, Hu Z, Liu J, et al. 2020. Seismic fault interpretation using deep learning-based semantic segmentation method. IEEE Geoscience and Remote Sensing Letters, 19: 1−5. DOI: 10.1109/LGRS.2020.3041301.

Isaac DB, Abu El Ata AS. 2023. Improvement of seismic data quality and recognition of fault discontinuities through seismic data conditioning applications: A case study of Issaran oil field, Gulf of Suez, Egypt. Journal of Petroleum Exploration and Production Technology, 13(2): 591−607. DOI: 10.1007/s13202-022-01574-2.

Jang J, So BD, Yuen DA. 2023. A machine learning algorithm with random forest for recognizing hidden control factors from seismic fault distribution. Geosciences Journal, 27(1): 113−126. DOI: 10.1007/s12303-022-0029-7.

Khayer K, Hosseini Fard E, Roshandel Kahoo A, et al. 2023. Integration of feature extraction, attribute combination and image segmentation for object delineation on seismic images. Acta Geophysica, 71(1): 275−292. DOI: 10.1007/s11600-022-00921-5.

Laudon C, Qi J, Rondon A, et al. 2021. An enhanced fault detection workflow combining machine learning and seismic attributes yields an improved fault model for Caspian Sea asset. First break, 39(10): 53−60. DOI: 10.3997/1365-2397.fb2021075.

Lefevre M, Souloumiac P, Cubas N, et al. 2020. Experimental evidence for crustal control over seismic fault segmentation. Geology, 48(8): 844−848. DOI: 10.1130/G47115.1.

Li JT, Wu XM, Hu ZX. 2021. Deep learning for simultaneous seismic image super-resolution and denoising. IEEE Transactions on Geoscience and Remote Sensing, 60: 1−11. DOI: 10.1109/TGRS.2021.3057857.

Li S, Yang C, Sun H, et al. 2019. Seismic fault detection using an encoder–decoder convolutional neural network with a small training set. Journal of Geophysics and Engineering, 16(1): 175−189. DOI: 10.1093/jge/gxy015.

Li W, Wang J. 2021. Residual learning of cycle-GAN for seismic data denoising. IEEE access, 9: 11585−11597. DOI: 10.1109/ACCESS.2021.3049479.

Li X, Li K, Xu Z, et al. 2023. Fault-Seg-Net: A method for seismic fault segmentation based on multi-scale feature fusion with imbalanced classification. Computers and Geotechnics, 158: 105412. DOI: 10.1016/j.compgeo.2023.105412.

Li X, Li K, Xu Z, et al. 2024. Fault-Seg-LNet: A method for seismic fault identification based on lightweight and dynamic scalable network. Engineering Applications of Artificial Intelligence, 127: 107316. DOI: 10.1016/j.engappai.2023.107316.

Li X, Li, K. 2024. Fault-attri-attention: A method for fault identification based on seismic attributes attention. Neural Computing and Applications, 36(7): 3645−3661. DOI: 10.1007/s00521-023-09265-7.

Lima G, Zeiser FA, Da Silveira A, et al. 2024. An encoder–decoder deep neural network for binary segmentation of seismic facies. Computers & Geosciences, 183: 105507. DOI: 10.1016/j.cageo.2023.105507.

Lin L, Zhong Z, Cai Z, et al. 2022. Automatic geologic fault identification from seismic data using 2. 5 D channel attention U-net. Geophysics, 87(4): IM111−IM124. DOI: 10.1190/geo2021-0805.1.

Liu ZN, Zhou C, Hu GM, et al. 2020, May. Interpretability-guided convolutional neural networks for seismic fault segmentation. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4312−4316). IEEE. DOI: 10.1109/ICASSP40776.2020.9053472.

Lyu B, Qi J, Machado G, et al. 2019. Seismic fault enhancement using spectral decomposition assisted attributes. In SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists: 1938−1942. DOI: 10.1190/segam2019-3215703.1.

Ma X, Yao G, Zhang F, et al. 2023. 3D Seismic Fault Detection Using Recurrent Convolutional Neural Networks with Compound Loss. IEEE Transactions on Geoscience and Remote Sensing, 61: 1−15. DOI: 10.1109/TGRS.2023.3275951.

Mahadik R, Singh G, Routray A. 2021. Multispectral coherence analysis for better fault visualization in seismic data. IEEE Geoscience and Remote Sensing Letters, 19: 1−5. DOI: 10.1109/LGRS.2021.3076213.

Michie EA, Mulrooney MJ, Braathen A. 2021. 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. Solid Earth Discussions, 12(6): 1259–1286. DOI: 10.5194/se-12-1259-2021.

Mizutani A, Yomogida K, Tanioka Y. 2020. Early tsunami detection with near-fault ocean-bottom pressure gauge records based on the comparison with seismic data. Journal of Geophysical Research: Oceans, 125(9): e2020JC016275. DOI: 10.1029/2020JC016275.

Martín-Martín M, Bullejos M, Cabezas D, et al. 2023. Using Python libraries and k-Nearest neighbors algorithms to delineate syn-sedimentary faults in sedimentary porous media. Marine and Petroleum Geology, 153: 106283. DOI: 10.1016/j.marpetgeo.2023.106283.

Mousavi J, Radad M, Soleimani Monfared M, et al. 2022. Fault enhancement in seismic images by introducing a novel strategy integrating attributes and image analysis techniques. Pure and Applied Geophysics, 179(5): 1645−1660. DOI: 10.1007/s00024-022-03014-y.

Mustafa A, Rastegar R, Brown T, et al. 2024. Visual attention guided learning with incomplete labels for Seismic Fault Interpretation. IEEE Transactions on Geoscience and Remote Sensing, 62: 1−12. DOI: 10.1109/TGRS.2024.3370037.

Noori M, Hassani H, Javaherian A, et al. 2019. Automatic fault detection in seismic data using Gaussian process regression. Journal of Applied Geophysics, 163: 117−131. DOI: 10.1016/j.jappgeo.2019.02.018.

Otchere DA, Tackie-Otoo BN, Mohammad MAA, et al. 2022. Improving seismic fault map through data conditioning using a pre-trained deep convolutional neural network: A case study on Groningen field. Journal of Petroleum Science and Engineering, 213: 110411. DOI: 10.1016/j.petrol.2022.110411.

Palo P, Routray A, Mahadik R, et al. 2023. Fault detection in seismic data using graph convolutional network. The Journal of Supercomputing, 79(11): 12737−12765. DOI: 10.1007/s11227-023-05173-8.

Ren K, Zou G, Zhang S, et al. 2023. Fault identification and reliability evaluation using an SVM model based on 3-D seismic data volume. Geophysical Journal International, 234(1): 755−768. DOI: 10.1093/gji/ggad095.

Share PE, Allam AA, Ben-Zion Y, et al. 2019. Structural properties of the San Jacinto fault zone at Blackburn Saddle from seismic data of a dense linear array. Pure and Applied Geophysics, 176: 1169−1191. DOI: 10.1007/s00024-018-1988-5.

Sheng M, Chu R, Peng Z, et al. 2022. Earthquakes triggered by fluid diffusion and boosted by fault reactivation in Weiyuan, China due to hydraulic fracturing. Journal of Geophysical Research: Solid Earth, 127(5): e2021JB022963. DOI: 10.1029/2021JB022963.

Titos M, Gutiérrez L, Benítez C, et al. 2023. Multi-station volcano tectonic earthquake monitoring based on transfer learning. Frontiers in Earth Science, 11: 1204832. DOI: 10.3389/feart.2023.1204832.

Ul Islam MS. 2020. Using deep learning-based methods to classify salt bodies in seismic images. Journal of Applied Geophysics, 178: 104054. DOI: 10.1016/j.jappgeo.2020.104054.

Visini F, Valentini A, Chartier T, et al. 2020. Computational tools for relaxing the fault segmentation in probabilistic seismic hazard modelling in complex fault systems. Pure and Applied Geophysics, 177: 1855−1877. DOI: 10.1007/s00024-019-02114-6.

Vu MT, Jardani A. 2022. Mapping discrete fracture networks using inversion of hydraulic tomography data with convolutional neural network: SegNet-Fracture. Journal of Hydrology, 609: 127752. DOI: 10.1016/j.jhydrol.2022.127752.

Vu MT, Jardani, A. 2022. Multi-task neural network in hydrological tomography to map the transmissivity and storativity simultaneously: HT-XNET. Journal of Hydrology, 612: 128167. DOI: 10.1016/j.jhydrol.2022.128167.

Waheed UB, Alkhalifah T, Haghighat E, et al. 2021. PINNtomo: Seismic tomography using physics-informed neural networks. arXiv preprint arXiv: 2104.01588. DOI: 10.48550/arXiv.2104.01588.

Wang QZ, Zhu ZY, Ding JC, et al. 2023. Research and application of intelligent fault detection method based on multi-scale fusion and Enhancement. In International Field Exploration and Development Conference, Singapore: Springer Nature Singapore, 543-553. DOI: 10.1007/978-981-97-0483-5_53.

Wang Z, Li B, Liu N, et al. 2020. Distilling knowledge from an ensemble of convolutional neural networks for seismic fault detection. IEEE Geoscience and Remote Sensing Letters, 19: 1−5. DOI: 10.1109/LGRS.2020.3034960.

Wei XL, Zhang CX, Kim SW, et al. 2022. Seismic fault detection using convolutional neural networks with focal loss. Computers & Geosciences, 158: 104968. DOI: 10.1016/j.cageo.2021.104968.

Wu J, Liu B, Zhang H, et al. 2021. Fault detection based on fully convolutional networks (FCN). Journal of Marine Science and Engineering, 9(3): 259. DOI: 10.3390/jmse9030259.

Wu J, Shi Y, Wang W. 2022. Fault imaging of seismic data based on a modified u-net with dilated convolution. Applied Sciences, 12(5): 2451. DOI: 10.3390/app12052451.

Wu X, Liang L, Shi Y, et al. 2019. Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network. Geophysical Journal International, 219(3): 2097−2109. DOI: 10.1093/gji/ggz418.

Wu X, Liang L, Shi Y, et al. 2019. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, 84(3): IM35−IM45. DOI: 10.1190/geo2018-0646.1.

Xu F, Li Z, Wen B, et al. 2021. The use of 3D convolutional autoencoder in fault and fracture network characterization. Geofluids, 1-11. DOI: 10.1155/2021/6650823.

Yan Z, Liu S, Gu H. 2019. Fault image enhancement using a forward and backward diffusion method. Computers & Geosciences, 131: 1−14. DOI: 10.1016/j.cageo.2019.06.004.

Yan Z, Zhang Z, Liu S. 2021. Improving performance of seismic fault detection by fine-tuning the convolutional neural network pre-trained with synthetic samples. Energies, 14(12): 3650. DOI: 10.3390/en14123650.

Yuan Z, Huang H, Jiang Y, et al. 2019. An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learning. Interpretation, 7(3): T713−T725. DOI: 10.1190/INT-2018-0180.1.

Zeng A, Yan L, Huang Y, et al. 2021. Intelligent detection of small faults using a support vector machine. Energies, 14(19): 6242. DOI: 10.3390/en14196242.

Zeng L, Niu Y, Ren W, et al. 2024. A method for intelligent identification of faults in seismic using an attention-based ES-UNet network with model re-training learning. Journal of Applied Geophysics, 105344. DOI: 10.1016/j.jappgeo.2024.105344.

Zhang R, Sun Q, Mao Y, et al. 2022. Accelerating hydraulic fracture imaging by deep transfer learning. IEEE Transactions on Antennas and Propagation, 70(7): 6117−6121. DOI: 10.1109/TAP.2022.3161325.

Zhang Z, Chen R, Ma J. 2024. Improving seismic fault recognition with Self-Supervised Pre-Training: A Study of 3D transformer-based with multi-scale decoding and fusion. Remote Sensing, 16(5): 922. DOI: 10.3390/rs16050922.

Zhou R, Yao X, Hu, G, et al. 2021. Learning from unlabelled real seismic data: Fault detection based on transfer learning. Geophysical Prospecting, 69(6): 1218−1234. DOI: 10.1111/1365-2478.13097.

Zhou R, Yao X, Wang Y, et al. 2021. Seismic fault detection with progressive transfer learning. Acta Geophysica, 69: 2187−2203. DOI: 10.1007/s11600-021-00668-5.

Zini JE, Rizk Y, Awad M. 2019. A deep transfer learning framework for seismic data analysis: A case study on bright spot detection. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3202−3212. DOI: 10.1109/TGRS.2019.2950888.

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