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Abstract

Summary

Machine Learning (ML) has recently been helpful for many seismic processing and imaging tasks. However, these tasks are often handled separately with their own neural network model and training. We propose StorSeismic, a unified framework to store the features in seismic data and use them later for varying seismic processing tasks. Through the help of the self-attention mechanism embedded in the Bidirectional Encoder Representation from Transformers (BERT), a Transformer-based network architecture, we capture and store the local and global features of seismic data in the pre-training stage, then utilize them in various seismic processing tasks in the fine-tuning stage. Using this framework, we could achieve a more efficient and flexible training process than existing approaches. Two applications on denoising and velocity estimation demonstrate the flexibility and the potential of this proposed framework in adapting to various seismic processing tasks.

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/content/papers/10.3997/2214-4609.202210282
2022-06-06
2024-04-20

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References

  1. Abnar, S. and Zuidema, W. [2020] Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928.
     [Google Scholar]
  2. Birnie, C., Ravasi, M., Liu, S. and Alkhalifah, T. [2021] The potential of self-supervised networks for random noise suppression in seismic data. Artificial Intelligence in Geosciences, 2, 47–59.
     [Google Scholar]
  3. Devlin, J., Chang, M.W., Lee, K. and Toutanova, K. [2018] Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
     [Google Scholar]
  4. Hou, S. and Messud, J. [2021] Machine learning for seismic processing: The path to fulfilling promises. In: First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 3204–3208.
     [Google Scholar]
  5. Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J. and Han, J. [2020] On the Variance of the Adaptive Learning Rate and Beyond. In: Proceedings of the Eighth International Conference on Learning Representations (ICLR 2020).
     [Google Scholar]
  6. Sun, B. and Alkhalifah, T. [2020] Pretraining system and method for seismic data processing using machine learning. World Intellectual Property Organization. Application No. PCT/IB2021/059174.
     [Google Scholar]
  7. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I. [2017] Attention is all you need. In: Advances in neural information processing systems. 5998–6008.
     [Google Scholar]
  8. Yu, J. and Wu, B. [2021] Attention and Hybrid Loss Guided Deep Learning for Consecutively Missing Seismic Data Reconstruction. IEEE Transactions on Geoscience and Remote Sensing.
     [Google Scholar]
  9. Zhang, H., Zhu, P., Gu, Y. and Li, X. [2019] Automatic velocity picking based on deep learning. In: SEG Technical Program Expanded Abstracts 2019, Society of Exploration Geophysicists, 2604–2608.
     [Google Scholar]
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Storseismic: An Approach to Pre-Train a Neural Network to Store Seismic Data Features
Conference Proceedings 2022, 1 (2022); https://doi.org/10.3997/2214-4609.202210282
/content/papers/10.3997/2214-4609.202210282
/content/papers/10.3997/2214-4609.202210282
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