1887
Volume 73, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Fault interpretation is crucial for subsurface resource extraction. Recent research has demonstrated that deep learning techniques can successfully detect faults. However, the network's prediction results still suffer from discontinuity and low accuracy problems due to insufficient exploitation of the spatial and global distribution characteristics of faults. This paper presents a novel approach for seismic fault detection using a dual‐attention mechanism and multi‐scale feature fusion. The proposed network uses ResNeSt residual blocks as encoders to extract multi‐scale features of faults. During multi‐scale feature fusion, a global context and a spatial dual‐attention module are introduced to suppress interference from non‐fault features. This improves the ability to detect faults. Five adjacent seismic slices were used as inputs to obtain the spatial distribution characteristics of faults. Data augmentation methods were used to enrich the fault morphology of synthetic seismic data. The Tversky loss function was used in the proposed model to alleviate the effect of data imbalance on fault identification tasks. Transfer learning methods were also used to evaluate the model's performance on field data from the F3 block in the Dutch North Sea and field data from the New Zealand Great South Basin. The model's performance was compared with some state‐of‐the‐art methods, including DeepLabV3+, Pyramid Scene Parsing Network, Feature Pyramid Network and U‐Net. The results show that the proposed fault detection method has excellent accuracy and fault continuity.

© 2025 European Association of Geoscientists & Engineers. © 2025 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.70048
2025-07-30
2026-02-07

  • From This Site

    /content/journals/10.1111/1365-2478.70048
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Alaudah, Y., P.Michałowicz, M.Alfarraj, and G.AlRegib. 2019. “A Machine‐Learning Benchmark for Facies Classification.” Interpretation7: SE175–SE187.
     [Google Scholar]
  2. An, Y., J.Guo, Q.Ye, C.Childs, J.Walsh, and R.Dong. 2021. “Deep Convolutional Neural Network for Automatic Fault Recognition From 3D Seismic Datasets.” Computers & Geosciences153: 104776.
     [Google Scholar]
  3. Bahorich, M., and S.Farmer. 1995. “3‐D Seismic Discontinuity for Faults and Stratigraphic Features: The Coherence Cube.” The Leading Edge14: 1053–1058.
     [Google Scholar]
  4. Blumentritt, C. H., K. J.Marfurt, and E. C.Sullivan. 2006. “Volume‐Based Curvature Computations Illuminate Fracture Orientations — Early to Mid‐Paleozoic, Central Basin Platform, West Texas.” Geophysics71: B159–B166.
     [Google Scholar]
  5. Chen, L.‐C., G.Papandreou, I.Kokkinos, K.Murphy, and A. L.Yuille. 2016. “Semantic Image Segmentation With Deep Convolutional Nets and Fully Connected CRFs.” https://arxiv.org/abs/1412.7062.
  6. Chen, L.‐C., G.Papandreou, I.Kokkinos, K.Murphy, and A. L.Yuille. 2018. “DeepLab: Semantic Image Segmentation With Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.” IEEE Transactions on Pattern Analysis and Machine Intelligence40: 834–848.
     [Google Scholar]
  7. Chen, L.‐C., G.Papandreou, F.Schroff, and H.Adam. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” http://arxiv.org/abs/1706.05587.
  8. Cunha, A., A.Pochet, H.Lopes, and M.Gattass. 2020. “Seismic Fault Detection in Real Data Using Transfer Learning From a Convolutional Neural Network Pre‐Trained With Synthetic Seismic Data.” Computers & Geosciences135: 104344.
     [Google Scholar]
  9. Di, H., Z.Wang, and G.AlRegib. 2018. “Seismic Fault Detection From Post‐Stack Amplitude by Convolutional Neural Networks.” In 80th EAGE Conference and Exhibition 2018, vol. 2018, 1–5. European Association of Geoscientists & Engineers.
  10. Fu, J., J.Liu, J.Jiang, Y.Li, Y.Bao, and H.Lu. 2021. “Scene Segmentation With Dual Relation‐Aware Attention Network.” IEEE Transactions on Neural Networks and Learning Systems32: 2547–2560.
     [Google Scholar]
  11. Gao, K., L.Huang, and Y.Zheng. 2022. “Fault Detection on Seismic Structural Images Using a Nested Residual U‐Net.” IEEE Transactions on Geoscience and Remote Sensing60: 1–15.
     [Google Scholar]
  12. Gao, K., L.Huang, Y.Zheng, R.Lin, H.Hu, and T.Cladohous. 2021. “Automatic Fault Detection on Seismic Images Using a Multiscale Attention Convolutional Neural Network.” Geophysics87: N13–N29.
     [Google Scholar]
  13. Guo, J., L.Xu, J.Ding, B.He, S.Dai, and F.Liu. 2021. “A Deep Supervised Edge Optimization Algorithm for Salt Body Segmentation.” IEEE Geoscience and Remote Sensing Letters18: 1746–1750.
     [Google Scholar]
  14. Guo, Y., S.Peng, W.Du, and D.Li. 2020. “Fault and Horizon Automatic Interpretation by CNN: A Case Study of Coalfield.” Journal of Geophysics and Engineering17: 1016–1025.
     [Google Scholar]
  15. Hale, D.2013. “Methods to Compute Fault Images, Extract Fault Surfaces, and Estimate Fault Throws From 3D Seismic Images.” Geophysics78: O33–O43.
     [Google Scholar]
  16. Hariharan, B., P.Arbeláez, R.Girshick, and J.Malik. 2015. “Hypercolumns for Object Segmentation and Fine‐Grained Localization.” In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 447–456. IEEE.
  17. Hu, G., Z.Hu, J.Liu, F.Cheng, and D.Peng. 2022. “Seismic Fault Interpretation Using Deep Learning‐Based Semantic Segmentation Method.” IEEE Geoscience and Remote Sensing Letters19: 1–5.
     [Google Scholar]
  18. Jing, J., Z.Yan, Z.Zhang, H.Gu, and B.Han. 2022. “Fault Detection Using a CNN Trained with PSF‐Convolution‐Based Samples.” Geophysics88: IM1–IM14.
     [Google Scholar]
  19. Li, D., S.Peng, Y.Lu, Y.Guo, and X.Cui. 2019. “Seismic Structure Interpretation Based on Machine Learning: A Case Study in Coal Mining.” Interpretation7: SE69–SE79.
     [Google Scholar]
  20. Li, H., Q.Hu, Y.Mao, F.Niu, and C.Liu. 2022. “Deep Learning‐Based Model for Automatic Salt Rock Segmentation.” Rock Mechanics and Rock Engineering55: 3735–3747.
     [Google Scholar]
  21. Li, X., K.Li, Z.Xu, Z.Huang, and Y.Dou. 2023. “Fault‐Seg‐Net: A Method for Seismic Fault Segmentation Based on Multi‐Scale Feature Fusion With Imbalanced Classification.” Computers & Geotechnics158: 105412.
     [Google Scholar]
  22. Li, Y., S.Peng, and D.He. 2025. “SaltFormer: A Hybrid CNN‐Transformer Network for Automatic Salt Dome Detection.” Computers & Geosciences195: 105772.
     [Google Scholar]
  23. Liao, Z., L.Hu, X.Huang, B. M.Carpenter, K. J.Marfurt, S.Vasileva, and Y.Zhou. 2020. “Characterizing Damage Zones of Normal Faults Using Seismic Variance in the Wangxuzhuang Oilfield, China.” Interpretation8: SP53–SP60.
     [Google Scholar]
  24. Lin, L., C.Li, Y.Kuang, X.Xin, and Z.Zhong. 2025. “Applications of Deep Learning‐Based Resolution‐Enhanced Seismic Data in Fault Identification.” Geophysical Prospecting73: 523–542.
     [Google Scholar]
  25. Lin, L., Z.Zhong, Z.Cai, A.Sun, and C.Li. 2022. “Automatic Geological Fault Identification from Seismic Data Using 2.5D Channel Attention U‐Net.” Geophysics87: IM111–IM124.
     [Google Scholar]
  26. Lin, T.‐Y., P.Dollár, R.Girshick, K.He, B.Hariharan, and S.Belongie. 2017. “Feature Pyramid Networks for Object Detection.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 936–944. IEEE.
  27. Long, Z., Y.Alaudah, and M. A.Qureshi. 2018. “A Comparative Study of Texture Attributes for Characterizing Subsurface Structures in Seismic Volumes.” Interpretation6: T1055–T1066.
     [Google Scholar]
  28. Marfurt, K. J., R. L.Kirlin, S. L.Farmer, and M. S.Bahorich. 1998. “3‐D Seismic Attributes Using a Semblance‐based Coherency Algorithm.” Geophysics63: 1150–1165.
     [Google Scholar]
  29. Niu, Z., G.Zhong, and H.Yu. 2021. “A Review on the Attention Mechanism of Deep Learning.” Neurocomputing452: 48–62.
     [Google Scholar]
  30. Pan, L., J.Gao, Y.Yang, Z.Wang, and Z.Gao. 2023. “Automatic Seismic Lithology Interpretation via Multi‐Attribute Integrated Deep Learning.” IEEE Geoscience and Remote Sensing Letters20: 1–5.
     [Google Scholar]
  31. Pham, N., and S.Fomel. 2024. “Seismic Data Augmentation for Automatic Fault Picking Using Deep Learning.” Geophysical Prospecting72: 125–141.
     [Google Scholar]
  32. Ramachandran, P., B.Zoph, and Q. V.Le. 2017. “Searching for Activation Functions.” http://arxiv.org/abs/1710.05941.
  33. Ronneberger, O., P.Fischer, and T.Brox. 2015. “U‐Net: Convolutional Networks for Biomedical Image Segmentation.” In Medical Image Computing and Computer‐Assisted Intervention – MICCAI 2015, 234–241. Springer International.
     [Google Scholar]
  34. Roy, A. G., N.Navab, and C.Wachinger. 2018. “Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks.” In MICCAI 2018, edited by A. F.Frangi, J. A.Schnabel, C.Davatzikos, C.Alberola‐López and G.Fichtinger, 421–429. Lecture Notes in Computer Science, vol. 11070. Springer International .
  35. Salehi, S. S. M., D.Erdogmus, and A.Gholipour. 2017. “Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks.” In Machine Learning in Medical Imaging, edited by Q.Wang, Y.Shi, H.‐I.Suk, and K.Suzuki, 379–387. Lecture Notes in Computer Science. Springer International.
  36. Sandler, M., A.Howard, M.Zhu, A.Zhmoginov, and L.‐C.Chen. 2018. “MobileNetV2: Inverted Residuals and Linear Bottlenecks.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4510–4520. IEEE.
  37. Shen, S., H.Li, W.Chen, X.Wang, and H.Binke. 2022. “Seismic Fault Interpretation Using 3D Scattering Wavelet Transform CNN.” IEEE Geoscience and Remote Sensing Letters19: 1–5.
     [Google Scholar]
  38. Tang, Z., B.Wu, W.Wu, and D.Ma. 2023. “Fault Detection via 2.5D Transformer U‐Net with Seismic Data Pre‐Processing.” Remote Sensing15: 1–20.
     [Google Scholar]
  39. Wang, X., S.Shi, X.Yao, Y.Wang, H.Yang, D.Liu, T.Wei, Y.Wang, and J.Pei. 2023. “Automatic Identification of Seismic Faults via the Integration of ResNet‐50 Residual Blocks and Convolutional Attention Modules.” Applied Geophysics20: 1–16.
     [Google Scholar]
  40. Wang, Z., B.Li, N.Liu, B.Wu, and X.Zhu. 2020. “Distilling Knowledge From an Ensemble of Convolutional Neural Networks for Seismic Fault Detection.” IEEE Geoscience and Remote Sensing Letters19: 1–5.
     [Google Scholar]
  41. Wei, X.‐L., C.‐X.Zhang, S.‐W.Kim, K.‐L.Jing, Y.‐J.Wang, S.Xu, and Z.‐Z.Xie. 2022. “Seismic Fault Detection Using Convolutional Neural Networks with Focal Loss.” Computers & Geosciences158: 104968.
     [Google Scholar]
  42. Woo, S., J.Park, J.‐Y.Lee, and I. S.Kweon. 2018. “CBAM: Convolutional Block Attention Module.” In Computer Vision – ECCV 2018, edited by V.Ferrari, M.Hebert, C.Sminchisescu, and Y.Weiss, 3–19Springer International Publishing.
  43. Wu, W., Y.Yang, B.Wu, D.Ma, Z.Tang, and X.Yin. 2023. “MTL‐FaultNet: Seismic Data Reconstruction Assisted Multitask Deep Learning 3‐D Fault Interpretation.” IEEE Transactions on Geoscience and Remote Sensing61: 1–15.
     [Google Scholar]
  44. Wu, X., and S.Fomel. 2018. “Automatic Fault Interpretation With Optimal Surface Voting.” Geophysics83: O67–O82.
     [Google Scholar]
  45. Wu, X., L.Liang, Y.Shi, and S.Fomel. 2019. “FaultSeg3D: Using Synthetic Data Sets to Train an End‐to‐End Convolutional Neural Network for 3D Seismic Fault Segmentation.” Geophysics84: IM35–IM45.
     [Google Scholar]
  46. Xiong, W., X.Ji, Y.Ma, Y.Wang, N.BenHassan, M.Ali, and Y.Luo. 2018. “Seismic Fault Detection With Convolutional Neural Network.” Geophysics83: 1SO–Z29.
     [Google Scholar]
  47. Yan, H., Z.Yan, J.Jing, Z.Zhang, H.Li, H.Gu, and S.Liu. 2024. “Enhancing the Seismic Response of Faults by Using a Deep Learning‐Based Method.” Geophysical Prospecting72: 2615–2633.
     [Google Scholar]
  48. Zhang, H., C.Wu, Z.Zhang, Y.Zhu, H.Lin, Z.Zhang, Y.Sun, T.He, J.Mueller, R.Manmatha, M.Li, and A.Smola. 2022. “ResNeSt: Split‐Attention Networks.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2735–2745. IEEE.
  49. Zhao, H., J.Shi, X.Qi, X.Wang, and J.Jia. 2017. “Pyramid Scene Parsing Network.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Honolulu, HI, 6230–6239. IEEE.
  50. Zhao, J., and S. Z.Sun. 2013. “Automatic Fault Extraction Using a Modified Ant‐Colony Algorithm.” Journal of Geophysics and Engineering10: 025009.
     [Google Scholar]
  51. Zhou, R., X.Yao, G.Hu, and F.Yu. 2021. “Learning From Unlabelled Real Seismic Data: Fault Detection Based on Transfer Learning.” Geophysical Prospecting69: 1218–1234.
     [Google Scholar]
  52. Zhou, R., C.Zhou, Y.Wang, X.Yao, G.Hu, and F.Yu. 2023. “Frequency Adaptive Fault Detection by Feature Pyramid Network With Wavelet Transform.” Geophysics88: IM113–IM130.
     [Google Scholar]
  53. Zhou, Z., M. M. R.Siddiquee, N.Tajbakhsh, and J.Liang. 2020. “UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation.” IEEE Transactions on Medical Imaging39: 1856–1867.
     [Google Scholar]
/content/journals/10.1111/1365-2478.70048
Loading
Seismic Fault Detection Using Dual‐Attention Multi‐Scale Fusion Networks With Deep Supervision
Geophysical Prospecting 73, e70048 (2025); https://doi.org/10.1111/1365-2478.70048
/content/journals/10.1111/1365-2478.70048
/content/journals/10.1111/1365-2478.70048
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): attention mechanism; convolutional neural network; deep learning; interpretation; seismics

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error