Overcoming the limited Field of View of convolutional neural network for fault detection: a multiscale approach

References

1. Etchebes, M., Bounaim, A., Brenna, T. and Steckhan, D.
   [2019]. Fault Characterization Using Advanced Seismic Interpretation Techniques–Quantitative Input for Optimal Well Placement in Structurally Complex Reservoirs. Conference Proceedings, 81st EAGE Conference and Exhibition, 2019, Jun 2019, Volume 2019, 1 – 5 (Extended Abstract).
   [Google Scholar]
2. Luo, W., Li, Y., Urtasun, r. and Zeme, R.
   [2017]. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 2016 30th Annual Conference on Neural Information Processing Systems, 4905 – 4913
   [Google Scholar]
3. Mccormack, K. and McClay, K.
   [2018] Orthorhombic faulting in the Beagle Sub-basin, North West Shelf, Australia. Geological Society London Special Publications,476(1), 205–230.
   [Google Scholar]
4. Sarajaervi, M., Hellem Bø, T., Goledowski, B., and Nickel, M.
   [2020] Robust Evaluation of Fault Prediction Results: Machine Learning Using Synthetic Seismic. Conference Proceedings, 1st EAGE Digitalization Conference and Exhibition, Nov 2020, Volume 2020, 1 – 5 (Extended Abstract).
   [Google Scholar]