Channelized systems detection using deep learning with fast labelling strategy

References

1. Alaudah, Y., Michałowicz, P., Alfarraj, M. and AlRegib, G.
   [2019] A machine-learning benchmark for facies classification. Interpretation, 7(3), SE175–SE187.
   [Google Scholar]
2. Araya-Polo, M., T.Dahlke, C.Frogner, C.Zhang, T.Poggio and D.Hohl
   , [2017] Automated fault detection without seismic processing. The Leading Edge36(3): 208–214.
   [Google Scholar]
3. BoillotL., GolmohammadiA., GuillonS. and PivotF.
   [2021] Deep learning seismic facies detection: the Total journey at SEAM AI hackathon. 82nd EAGE Annual Conference and Exhibition.
   [Google Scholar]
4. Civitarese, D., Szwarcman, D., Vital Brazil, E. and Zadrozny. B.
   [2019] Semantic Segmentation of Seismic Images. ArXiv:1905.04307.
   [Google Scholar]
5. Di, H.
   [2018] Developing a seismic pattern interpretation network (SpiNet) for automated seismic interpretation. ArXiv:1810.08517.
   [Google Scholar]
6. Guillon, S., JoncourF., BarrallonP.E. and CastaniéL.
   [2020] Ground-truth uncertainty-aware metrics for machine learning applications on seismic image interpretation: Application to faults and horizon extraction. The Leading Edge39(10):734.
   [Google Scholar]
7. Kington, J.
   [2015] Semblance, coherence, and other discontinuity attributes. The Leading Edge, 34(12), 1510–1512.
   [Google Scholar]
8. Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B. and Belongie, S.
   [2017] Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2117–2125.
   [Google Scholar]
9. PhamN., FomelS. and DunlapD.
   [2019] Automatic channel detection using deep learning. Interpretation7(3), SE43–SE50.
   [Google Scholar]
10. Ronneberger, O., Fischer, P. and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv:1505.04597.
    [Google Scholar]
11. Shi, Y., Wu, X. and Fomel. S.
    [2019] SaltSeg: Automatic 3D Salt Segmentation Using a Deep Convolutional Neural Network. Interpretation7(3): SE113–22.
    [Google Scholar]
12. Wu, X., L.Liang, Y.Shi, and S.Fomel
    , [2019], Faultseg3D: using synthetic datasets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics84, 1–36.
    [Google Scholar]
13. ZhangL., ZhangM., ZhongZ., ZhaoT., WuY., WeiJ. and ZhanC.
    [2019] Deep learning approach in characterizing salt body on seismic images, SEG Technical Program Expanded Abstracts: 2594–2598.
    [Google Scholar]