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Abstract

Summary

The proposed approach considers the calculation of traveltimes on a coarse grid followed by neural network training for interpolating these traveltimes on a fine grid. Using the neural network approximation has two advantages: it reduces computational burden for complicated models (when numerical eikonal solvers should be used for traveltime computation on a fine grid), it also reduces memory requirements (as compared to storing all traveltimes computed on the fine grid). We derived the neural network architecture with a single hidden layer and performed the numerical tests, including the application of the proposed approach to the microseismic data imaging. The numerical test showed that for laterally inhomogeneous velocity model (2D) a neural network with 100 neurons on hidden layer provides a mean absolute error of about 2.7 ms and for thin-layered inhomogeneous velocity model (1D) a neural network with 4 neurons on hidden layer provides a mean absolute error of about 1 ms. The achieved accuracy is enough for the imaging objectives. Besides, the proposed approach allows to speed up the imaging performance by 4 times (2D) and by 20 times (1D) and also significantly reduce the memory for storage.

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/content/papers/10.3997/2214-4609.201901193
2019-06-03
2024-03-29
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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. Artman, B.
    [2006] Imaging passive seismic data. Geophysics, 71(4), SI177–SI187.
    [Google Scholar]
  3. Blias, E.
    [2013] Moveout approximation for vertical seismic profile geometry in a 2D model with anisotropic layers. Geophysical Prospecting, 61(3), 574–581.
    [Google Scholar]
  4. Cybenko, G.
    [1992] Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems (MCSS), 5(4), 455–455.
    [Google Scholar]
  5. Duncan, P.M.
    [2005] Is there a future for passive seismic?First Break, 23(6).
    [Google Scholar]
  6. Ivanov, Y. and Stovas, A.
    [2017] Traveltime parameters in tilted orthorhombic medium. Geophysics, 82(6), C187–C200.
    [Google Scholar]
  7. Nikitin, A.A., Serdyukov, A.S. and Duchkov, A.A.
    [2018] Cache-efficient parallel eikonal solver for multicore CPUs. Computational Geosciences, 22(3), 775–787.
    [Google Scholar]
  8. Staněk, F., Anikiev, D., Valenta, J. and Eisner, L.
    [2015] Semblance for microseismic event detection. Geophysical Journal International, 201(3), 1362–1369.
    [Google Scholar]
  9. Stovas, A. and Fomel, S.
    [2018] Generalized velocity approximation. Geophysics, 84(1), 1–65.
    [Google Scholar]
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