1887

Abstract

Summary

We explore the feasibility of a deep learning approach for tomography by comparing it with the current velocity prediction techniques used in the industry. This is accomplished through quantitative and qualitative comparisons of velocity models predicted by a Machine Learning (ML) system and those of two variations of full-waveform inversion (FWI). Additionally, we compare the computational aspects of the two approaches. The results show that the ML-based reconstructed models are competitive to the FWI-produced models in terms selected metrics, and widely less expensive to compute.

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/content/papers/10.3997/2214-4609.201803073
2018-09-21
2020-04-09
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References

  1. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T.
    [2018] Deep-learning tomography. The Leading Edge, 37(1), 58–66.
    [Google Scholar]
  2. Bunks, C., Saleck, F.M., Zaleski, S. and Chavent, G.
    [1995] Multiscale seismic waveform inversion. Geophysics, 60(5), 1457–1473.
    [Google Scholar]
  3. Claerbout, J.F.
    [1971] Toward a Unified Theory of Reflector Mapping. Geophysics, 36(3), 467–481.
    [Google Scholar]
  4. Taner, M.T. and Koehler, F.
    [1969] Velocity spectra-Digital computer derivation applications of velocity functions. Geophysics, 34(6), 859–881.
    [Google Scholar]
  5. Tarantola, A.
    [1984] Inversion of seismic reflection data in the acoustic approximation. Geophysics, 49(8), 1259–1266.
    [Google Scholar]
  6. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26
    [Google Scholar]
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