1887

Abstract

Summary

We present a study of seismic inversion using deep learning tools. The purpose is to investigate the feasibility of using neural networks to construct acoustic and elastic earth models directly from pre-stack seismic data. Training and testing of the neural networks are done using thousands of synthetic 1D earth models and seismic gathers. We use 2 different types of neural network architectures in our numerical experiments to investigate seismic inversion in different geological scenarios. In both cases, the quality of the prediction is comparable with that obtained from conventional model building processes as such as travel-time and waveform inversion methods. The predicted earth models contain abundant low and medium wave-number information. In terms of performance, training took only less than 30 minutes on 4 GPUs whilst prediction adds negligible cost.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201803008
2018-11-30
2019-12-05
Loading full text...

Full text loading...

References

  1. Lewis, W and Vigh, D.
    , [2017] Deep learning prior models from seismic images for full-waveform inversion. 87th Annual International Meeting, SEG, Expanded Abstract, 1512–1517
    [Google Scholar]
  2. Araya-Polo, M. Jennings, J., Adler, A. and Dahlke, T.
    , [2018] Deep-learning tomography. The Leading Edge, 37, 58–66
    [Google Scholar]
  3. Richardson, A.
    , [2018] Seismic Full-waveform Inversion Using Deep Learning Tools and Techniques. ArXiv e-prints1801.07232
    [Google Scholar]
  4. Kennet, B.L.N.
    , [1983] Seismic wave propagation in stratified media. Cambridge University Press.
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201803008
Loading
/content/papers/10.3997/2214-4609.201803008
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