1887

Abstract

Summary

We investigate deep learning approaches to inversion of a 1D model of the subsurface using synthetic surface seismic and VSP data. Several deep neural networks based on three different architectures are developed and tested. The matrix propagator technique is used to generate the synthetic data for network training. The pre-trained deep neural networks can instantly predict velocity models from new data in a single step. The synthetic datasets used in training can be extended by adding random noise to the existing data, thus making the method closer to real-world conditions.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201900765
2019-06-03
2020-04-10
Loading full text...

Full text loading...

References

  1. Das, V., Pollack, A., Wollner, U., and Mukerji, T.
    [2018] Convolutional neural network for seismic impedance inversion. In: SEG Technical Program Expanded Abstracts 2018, 2071–2075.
    [Google Scholar]
  2. Hochreiter, S. and Schmidhuber, J.
    [1997] Long short-term memory. Neural computation, 9(8), 1735–1780.
    [Google Scholar]
  3. Ma, Y., Loures, L., and Margrave, G. F.
    [2004] Seismic modeling with the reflectivity method. CREWES Research Report, 16-1.
    [Google Scholar]
  4. Puzyrev, V.
    [2018] Deep learning electromagnetic inversion with convolutional neural networks. Submitted. arXiv preprint arXiv:1812.10247.
    [Google Scholar]
  5. Siahkoohi, A., Kumar, R. and Herrmann, F.
    [2018] Seismic data reconstruction with generative adversarial networks. In: 80th EAGE Conference and Exhibition 2018.
    [Google Scholar]
  6. Wu, Y., Lin, Y., and Zhou, Z.
    [2018] InversionNet: Accurate and efficient seismic waveform inversion with convolutional neural networks. In: SEG Technical Program Expanded Abstracts 2018, 2096–2100.
    [Google Scholar]
  7. Zhang, G., Wang, Z., and Chen, Y.
    [2018] Deep learning for seismic lithology prediction. Geophysical Journal International, 215(2), 1368–1387.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201900765
Loading
/content/papers/10.3997/2214-4609.201900765
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