1887

Abstract

Summary

Majority of the deep learning techniques in seismic image analysis focus on solving one task at a time and ignore the richness of presence of many other structures in the vicinity and their correlation with the task of interest at hand. These approaches work best in solving the identification of simple structures in the shallow areas of the survey where the signal-to-noise ratio is high and struggle in deeper areas as the signal becomes weaker. In addition, it is a challenge to acquire the right data and quality labels to train the deep learning models for some of the fundamental challenges in geoscience. In this paper, we present two recent applications of deep learning in seismic processing that are targeted to reduce the cycle time of seismic processing projects. In the first example, we present the concept of learning multiple related tasks and demonstrate on a use case that the multi-task learning learns common representations of related tasks (salt body and salt boundary) and improves the accuracy of identifying and segmenting salt boundary structures even in low signal-to-noise areas. In the second example, we reconstruct the regularly and irregularly missing in narrow-azimuth data using an encoder-decoder style convolutional neural network.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202130009
2021-02-23
2021-06-20
Loading full text...

Full text loading...

References

  1. SebastianRuder
    , “An Overview of Multi-Task Learning in Deep Neural Networks”, 2017, ArXiv e-prints.
    [Google Scholar]
  2. XinmingWu, LumingLiang, YunzhiShi, ZhichengGeng and SergeyFomel
    , “Multi-task learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network”, Geophysical Journal International, Volume 219, Issue 3, December2019, Pages 2097-2109.
    [Google Scholar]
  3. Mandelli, S., Borra, F., Lipari, V., Bestagini, P., Sarti, A. and Tubaro, S.
    , 2018. Seismic data interpolation through convolutional autoencoder. In SEG Technical Program Expanded Abstracts2018 (pp. 4101-4105). Society of Exploration Geophysicists.
    [Google Scholar]
  4. Wang, B., Zhang, N., Lu, W. and Wang, J.
    , 2019. Deep-learning-based seismic data interpolation: A preliminary result. Geophysics, 84(1), pp.V11-V20.
    [Google Scholar]
  5. Chai, X., Gu, H., Li, F., Duan, H., Hu, X. and Lin, K.
    , 2020. Deep learning for irregularly and regularly missing data reconstruction. Scientific Reports, 10(1), pp.1-18.
    [Google Scholar]
  6. Ronneberger, O., Fischer, P. and Brox, T.
    , 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.
    [Google Scholar]
  7. Jin, Y., Wu, X., Chen, J., Han, Z. and Hu, W.
    , 2018. Seismic data denoising by deep-residual networks. In SEG Technical Program Expanded Abstracts 2018 (pp. 4593-4597). Society of Exploration Geophysicists.
    [Google Scholar]
  8. Siahkoohi, A., Kumar, R. and Herrmann, F.
    , 2018, June. Seismic data reconstruction with generative adversarial networks. In 80th EAGE Conference and Exhibition 2018 (Vol. 2018, No. 1, pp. 1-5). European Association of Geoscientists & Engineers.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202130009
Loading
/content/papers/10.3997/2214-4609.202130009
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error