1887
Volume 72, Issue 9
  • E-ISSN: 1365-2478

Abstract

Abstract

This article introduces an improved method for geological fault interpretation utilizing a 2D convolutional neural network approach with a focus on coalbed horizons, dealing with the problem as an image classification task. At the beginning, linear annotations, reflecting geological fault features, are applied to multiple seismic sections. By considering texture differences between fault and non‐fault areas, we construct samples that represent these distinct zones for training deep neural networks. Initially, fault annotations are transformed into single dots to facilitate pixel‐based processing. To depict a specific dot's geological structure, we employ a matrix clipped around the point, determined by a combination of range and step parameters. Convolutional layers generate filters equivalent to seismic data transformation, streamlining the need for analysis and selection of seismic attributes. The article discusses enhancing the efficiency of 2D convolutional neural network–based fault interpretation by optimizing sample selection, data extraction and model construction procedures. Through the incorporation of data from two mining areas (totalling 27.09 km2) in sample creation, the overall accuracy exceeds 0.99. Recognition extends seamlessly to unlabelled sections, showcasing the innovative technical route and methodology of fault interpretation with linear annotation and pixel‐based thinking. This study presents a method that integrates planar and raster thinking, transitioning from vision‐oriented geological structure annotation to algorithm‐oriented pixel location. The proposed 2D convolutional neural network–based matrix‐oriented fault/non‐fault binary classification demonstrates feasibility and reproducibility, offering a new automated approach for fault detection in coalbeds through convolutional neural network algorithms.

© 2024 European Association of Geoscientists & Engineers. © 2024 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13606
2024-10-11
2025-11-11

  • From This Site

    /content/journals/10.1111/1365-2478.13606
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Al‐Dossary, S. & Marfurt, K.J. (2006) 3D volumetric multispectral estimates of reflector curvature and rotation. Geophysics, 71, 41–51.
     [Google Scholar]
  2. Albawi, S., Mohammed, T.A. & Al‐Zawi, S. (2017) Understanding of a convolutional neural network. In: The International Conference on Engineering and Technology 2017, 21–23 August 2017, Antalya, Turkey. New York City: IEEE. pp. 1–6.
  3. Alzubaidi, L., Zhang, J., Humaidi, A.J., Al‐Dujaili, A., Duan, Y., Al‐Shamma, O. et al. (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 1–74.
     [Google Scholar]
  4. Alzubi, J., Nayyar, A. & Kumar, A. (2018) Machine learning from theory to algorithms: an overview. Journal of Physics: Conference Series, 1142, 012012.
     [Google Scholar]
  5. An, Y., Du, H., Ma, S., Niu, Y., Liu, D., Wang, J. et al. (2023) Current state and future directions for deep learning based automatic seismic fault interpretation: a systematic review. Earth‐Science Reviews, 243, 104509.
     [Google Scholar]
  6. Bahorich, M. & Farmer, S. (1995) 3‐D seismic discontinuity for faults and stratigraphic features: the coherence cube. The Leading Edge, 14, 1053–1058.
     [Google Scholar]
  7. Brunette, E.S., Flemmer, R.C. & Flemmer, C.L. (2009) A review of artificial intelligence. In: 4th International Conference on Autonomous Robots and Agents, ICARA 2009, February 10–12, 2009, Wellington, New Zealand. New York City: IEEE. pp. 385–392.
  8. Candes, E.J. & Donoho, D.L. (2000) Curvelets: a surprisingly effective nonadaptive representation for objects with edges. Stanford, CA: Stanford University, Department of Statistics.
     [Google Scholar]
  9. Chaudhary, S., Yadav, S., Kushwaha, S. & Shahi, S.R.P. (2020) A brief review of machine learning and its applications. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12, 218–223.
     [Google Scholar]
  10. Colorni, A., Dorigo, M. & Maniezzo, V. (1991) Distributed optimization by ant colonies. In: Proceedings of ECAL91 – European Conference on Artificial Life, Paris, France. Amsterdam, the Netherlands: Elsevier Publishing. pp. 134–142.
  11. Dai, H. & MacBeth, C. (1995) Automatic picking of seismic arrivals in local earthquake data using an artificial neural network. Geophysical Journal International, 120, 758–774.
     [Google Scholar]
  12. Di, H., Gao, D. & AlRegib, G. (2019) Developing a seismic texture analysis neural network for machine‐aided seismic pattern recognition and classification. Geophysical Journal International, 218, 1262–1275.
     [Google Scholar]
  13. Dorn, G.A. (2013) Domain transform: A tool for imaging andinterpreting geomorphology and stratigraphy in seismic volumes. The Leading Edge, 32, 146–153.
     [Google Scholar]
  14. Du, Y. & Du, D. (2018) Fault detection and diagnosis using empirical mode decomposition based principal component analysis. Computers & Chemical Engineering, 115, 1–21.
     [Google Scholar]
  15. Fish, B.C. & Kusuma, T. (1994) A neural network approach to automate velocity picking. In: SEG Technical Program Expanded Abstracts 1994. Houston, TX, Society of Exploration Geophysicists. pp. 185–188.
  16. Gersztenkorn, A. & Marfurt, K.J. (1999) Eigenstructure‐based coherence computations as an aid to 3‐D structural and stratigraphic mapping. Geophysics, 64, 1468–1479.
     [Google Scholar]
  17. Guo, Y., Peng, S., Du, W. & Li, D. (2020) Fault and horizon automatic interpretation by CNN: a case study of coalfield. Journal of Geophysics and Engineering, 17, 1016–1025.
     [Google Scholar]
  18. Helm, J.M., Swiergosz, A.M., Haeberle, H.S., Karnuta, J.M., Schaffer, J.L., Krebs, V.E. et al. (2020) Machine learning and artificial intelligence: definitions, applications, and future directions. Current Reviews in Musculoskeletal Medicine, 13, 69–76.
     [Google Scholar]
  19. LeCun, Y., Bengio, Y. & Hinton, G. (2015) Deep learning. Nature, 521, 436–444.
     [Google Scholar]
  20. Li, D., Peng, S., Lu, Y., Guo, Y. & Cui, X. (2019) Seismic structure interpretation based on machine learning: a case study in coal mining. Interpretation, 7, SE69–SE79.
     [Google Scholar]
  21. Li, Z., Liu, F., Yang, W., Peng, S. & Zhou, J. (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems, 33, 6999–7019.
     [Google Scholar]
  22. Lungarella, M., Iida, F., Bongard, J. & Pfeifer, R. (2007) 50 Years of artificial intelligence: essays dedicated to the 50th anniversary of artificial intelligence. Berlin, Heidelberg: Springer.
     [Google Scholar]
  23. Ma, Z. & Mei, G. (2021) Deep learning for geological hazards analysis: data, models, applications, and opportunities. Earth‐Science Reviews, 223, 103858.
     [Google Scholar]
  24. Mahesh, B. (2020) Machine learning algorithms‐a review. International Journal of Science and Research (IJSR) [Internet], 9, 381–386.
     [Google Scholar]
  25. Marfurt, K.J., Kirlin, R.L., Farmer, S.L. & Bahorich, M.S. (1998) 3‐D seismic attributes using a semblance‐based coherency algorithm. Geophysics, 63, 1150–1165.
     [Google Scholar]
  26. Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N. & Terzopoulos, D. (2021) Image segmentation using deep learning: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 3523–3542.
     [Google Scholar]
  27. Murat, M.E. & Rudman, A.J. (1992) Automated first arrival picking: a neural network approach1. Geophysical Prospecting, 40, 587–604.
     [Google Scholar]
  28. Oke, S.A. (2008) A literature review on artificial intelligence. International Journal of Information and Management Sciences, 19, 535–570.
     [Google Scholar]
  29. Peters, B., Haber, E. & Granek, J. (2019) Neural networks for geophysicists and their application to seismic data interpretation. The Leading Edge, 38, 534–540.
     [Google Scholar]
  30. Ray, S. (2019) A quick review of machine learning algorithms. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 14–16 February 2019, Faridabad, India. New York City: IEEE. pp. 35–39.
  31. Ren, K., Zou, G., Peng, S., Yeh, H.‐G., Deng, B. & Ji, Y. (2022) Fault identification based on the kernel principal component analysis‐genetic particle swarm optimization‐support vector machine algorithm for seismic attributes in the Sihe Coal Mine, Qinshui Basin, China. Interpretation, 11, T59–T73.
     [Google Scholar]
  32. Ren, K., Zou, G., Zhang, S., Peng, S., Gong, F. & Liu, Y. (2023) Fault identification and reliability evaluation using an SVM model based on 3‐D seismic data volume. Geophysical Journal International, 234, 755–768.
     [Google Scholar]
  33. Roberts, A. (2001) Curvature attributes and their application to 3D interpreted horizons. First Break, 19, 85–100.
     [Google Scholar]
  34. Shrestha, A. & Mahmood, A. (2019) Review of deep learning algorithms and architectures. IEEE Access: Practical Innovations, Open Solutions, 7, 53040–53065.
     [Google Scholar]
  35. Sietsma, J. & Dow, R.J. (1991) Creating artificial neural networks that generalize. Neural Networks, 4, 67–79.
     [Google Scholar]
  36. Spichak, V. & Popova, I. (2000) Artificial neural network inversion of magnetotelluric data in terms of three‐dimensional earth macroparameters. Geophysical Journal International, 142, 15–26.
     [Google Scholar]
  37. Waldeland, A.U., Jensen, A.C., Gelius, L.J. & Solberg, A.H.S. (2018) Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37, 529–537.
     [Google Scholar]
  38. Wang, H., Yan, J.‐Y., Fu, G.‐M. & Wang, X. (2020) Current status and application prospect of deep learning in geophysics. Progress in Geophysics, 35, 642–655.
     [Google Scholar]
  39. Wang, P., Fan, E. & Wang, P. (2021) Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters, 141, 61–67.
     [Google Scholar]
  40. Wu, X., Liang, L., Shi, Y. & Fomel, S. (2019) FaultSeg3D: using synthetic data sets to train an end‐to‐end convolutional neural network for 3D seismic fault segmentation. Geophysics, 84, IM35–IM45.
     [Google Scholar]
  41. Wu, X. & Zhu, Z. (2017) Methods to enhance seismic faults and construct fault surfaces. Computers & Geosciences, 107, 37–48.
     [Google Scholar]
  42. Zhang, Y. & Paulson, K. (1997) Magnetotelluric inversion using regularized Hopfield neural networks [Link]. Geophysical Prospecting, 45, 725–743.
     [Google Scholar]
  43. Zou, G., Ren, K., Sun, Z., Peng, S. & Tang, Y. (2019) Fault interpretation using a support vector machine: a study based on 3D seismic mapping of the Zhaozhuang coal mine in the Qinshui Basin, China. Journal of Applied Geophysics, 171, 103870.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13606
Loading
An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking
Geophysical Prospecting 72, 3350 (2024); https://doi.org/10.1111/1365-2478.13606
/content/journals/10.1111/1365-2478.13606
/content/journals/10.1111/1365-2478.13606
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): imaging; interpretation; modelling; 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