The Discovery of new possibles reserves is an critical activities for the oil and gas industry. The most used methods to understand the sub-surface are based on seismic surveys. however, the process of the interpretation of these surveys are very expensive and due to the volume of data it overload the human capabilities. On the other hand, deep learning techniques have been increasingly applied in several areas of science to help in tasks that were considered human-centered, such as image classification and language translation, among others. We propose a machine learning methodology to classify seismic data at the pixel level, producing an interpretation mask suggestion. Our methodology comprises three main parts: model selection, dataset preparation, and training. We also present Danet-FCN3, a deep neural network specifically designed to classify seismic images at pixel level resolution. We have recently demonstrated that our deep learning models can distinguish among different rock layers helping the expert to interpret new seismic images. The dataset preparation processes the raw post-stacked data and the interpretation labels to produce training, validation and testing sets.


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  1. Chevitarese, D. S., Szwarcman, D., Gama e Silva, R., and Vital Brazil, E.
    [2018a] Deep Learning Applied to Seismic Facies Classification: A Methodology for Training. In EAGE Saint Petersburg International Conference 2018.
    [Google Scholar]
  2. [2018b] Transfer Learning Applied to Seismic Images Classification. In ACE 2018 Annual Convention & Exhibition 2018. Salt Lake City: AAPG.
    [Google Scholar]
  3. [2018c] Seismic Facies Segmentation Using Deep Learning. In ACE 2018 Annual Convention & Exhibition 2018. Salt Lake City: AAPG.
    [Google Scholar]
  4. Chevitarese, D. S., Szwarcman, D., Vital Brazil, E., and BZadrozny
    [2018d] Efficient Classification of Seismic Textures. In 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro: IEEE.
    [Google Scholar]
  5. He, K., Zhang, X., Ren, S., and Sun, J.
    [2016] Identity Mappings in Deep Residual Networks. In European Conference on Computer Vision, 2016, 630–45.
    [Google Scholar]
  6. Li, W.
    [2018] Classifying geological structure elements from seismic images using deep learning. SEG Technical Program Expanded Abstracts2018: pp. 4643–4648
    [Google Scholar]
  7. Ronneberger, O., Fischer, P., and Brox, T.
    [2015] U-Net: Convolutional Networks for Biomedical Image Segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, 234–41.
    [Google Scholar]
  8. Shi, Y., Wu, X. and Fomel, S.
    [2018] Automatic salt-body classification using deep-convolutional neural network. SEG Technical Program Expanded Abstracts2018: pp. 1971–1975.
    [Google Scholar]
  9. Waldeland, A., Jensen, A., Gelius, L. and SolbergA.
    , [2018] Convolutional neural networks for automated seismic interpretation. The Leading Edge, 37(7), 529–537, Jul. 2018.
    [Google Scholar]
  10. Zao, T.
    [2018] Seismic facies classification using different deep convolutional neural networks. SEG Technical Program Expanded Abstracts2018: pp. 2046–2050.
    [Google Scholar]

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