Geological / geophysical interpretation of seismic survey commonly requires segmenting a seismic image into different layers/sequences, highlighting certain geobodies, or picking different horizon surfaces, for multiple purposes including, but not limited to, earth model building, velocity model building, stratigraphic analysis, etc. The traditional approach requires the interpreter significant amount of effort to interact with computer and label the data.

We demonstrated an innovative workflow for seismic image/sequence/geobody segmentation and horizon picking, where a key aspect is that, it requires much less labels and hence significantly reduce interpreter's workload.


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  1. Badrinarayanan, V., Kendall, A., and Cipolla, R.
    [2017] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.IEEE Transactions on Pattern Analysis and Machine Learning, DOI:10.1109/TPAMI.2016.2644615.
    https://doi.org/10.1109/TPAMI.2016.2644615 [Google Scholar]
  2. Di, H., Gao, D., and AlRegib, G.
    [2018] 3D dip vector-guided auto-tracking for weak seismic reflections: A new tool for shale reservoir visualization and interpretation.Interpretation, 6, SN47–SN56
    [Google Scholar]
  3. Kallenberg, M., Petersen, K., Nielsen, M., Ng, A., Diao, P., Igel, C., Vachon, C., Holland, K., Winkel, R., Karssemeijer, N., and Lillholm, M.
    [2016] Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.2016 IEEE Transactions on Medical Imaging, DOI:10.1109/TMI.2016.2532122
    https://doi.org/10.1109/TMI.2016.2532122 [Google Scholar]
  4. Leggett, M., Sandham, W. A., and Durrani, T. S.
    [1996] 3-D seismic horizon tracking using an artificial neural network.First Break, 14, 413–418.
    [Google Scholar]
  5. Long, J., Shelhamer, E., and Darrell, T.
    [2015] Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on CVPR, DOI: 10.1109/CVPR.2015.7298965.
    https://doi.org/10.1109/CVPR.2015.7298965 [Google Scholar]
  6. Ronneberger, O., Fischer, P., and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation.Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pp 234–241.
    [Google Scholar]
  7. Yu, Y., Kelley, C., and Mardanova, I.
    [2008] Seismic horizon autopicking using orientation vector field.U.S. Patent 8265876B1.
    [Google Scholar]
  8. Zeiler, M., Taylor, G., and Fergus, R.
    [2011] Adaptive deconvolutional networks for mid and high level feature learning. International Conference on Computer Vision, DOI:10.1109/ICCV.2011.6126474
    https://doi.org/10.1109/ICCV.2011.6126474 [Google Scholar]

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