1887
Volume 70, Issue 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

The physics‐guided neural network framework combines the effectiveness of data‐driven and physics‐based models, and it is, therefore, becoming increasingly popular in geophysical applications. We present a physics‐guided neural network–based approach to calibrate velocity models for microseismic data. In our implementation, the physics‐guided neural network comprises of a user‐selected number of fully connected layers, a scaling and shifting layer and a forward modelling operator layer. We input the observed P‐ and S‐wave arrival times to the neural network. In the forward pass, the network's output layer produces normalized P‐ and S‐wave velocities for the subsurface model. The scaling and shifting layer converts the normalized output to realistic velocity values. The forward modelling operator (i.e. a ray‐shooting algorithm) layer computes traveltimes using the velocities from the preceding scaling and shifting layer and the known source–receiver locations. We then evaluate a loss function that compares the predicted traveltimes with the input observed arrival times, and update network's weights and bias parameters. We also use a weight‐based saliency measure to evaluate whether the selected network architecture (i.e. number of hidden layers and neurons) is optimal for the model calibration problem. Finally, using synthetic data examples, we demonstrate that our unsupervised physics‐guided neural network–based approach can provide robust velocity model and uncertainty estimates.

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/content/journals/10.1111/1365-2478.13191
2022-04-14
2022-05-29
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References

  1. Akram, J. (2020) Understanding Downhole Microseismic Data Analysis: With Applications in Hydraulic Fracture Monitoring. Cham: Springer.
    [Google Scholar]
  2. Akram, J. and Eaton, D.W. (2017) 1D layered velocity models and microseismic event locations: synthetic examples for a case with a single linear receiver array. Journal of Geophysics and Engineering, 14, 1215–1224.
    [Google Scholar]
  3. Akram, J., Ovcharenko, O.O. and Peter, D.B. (2017) A robust neural network based approach for microseismic event detection. SEG Technical Program Expanded Abstracts, 2929–2933.
    [Google Scholar]
  4. Bardainne, T. and Gaucher, E. (2010) Constrained tomography of realistic velocity models in microseismic monitoring using calibration shots. Geophysical Prospecting, 58, 739–753.
    [Google Scholar]
  5. Biswas, R., Sen, M.K., Das, V. and Mukerji, T. (2019) Pre‐stack inversion using a physics‐guided convolutional neural network. SEG Technical Program Expanded Abstracts, 4967–4971.
    [Google Scholar]
  6. Duncan, P.M. (2010) Microseismic monitoring: technology state of play. SPE Unconventional Gas Conference, SPE131777.
    [Google Scholar]
  7. Glorot, X. and Bengio, Y. (2010) Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 249–256.
    [Google Scholar]
  8. Huang, G., Ba, J., Du, Q. and Carcione, J.M. (2019) Simultaneous inversion for velocity model and microseismic sources in layered anisotropic media. Journal of Petroleum Science and Engineering, 173, 1453–1463.
    [Google Scholar]
  9. Jansky, J., Plicka, V. and Eisner, L. (2010) Feasibility of joint 1D velocity model and event location inversion by the neighbourhood algorithm. Geophysical Prospecting, 58(2), 229–234.
    [Google Scholar]
  10. Jin, P., Feng, S., Lin, Y., Wohlberg, B., Moulton, D., Cromwell, E. and Chen, X. (2020) CycleFCN: a physics‐informed data‐driven seismic waveform inversion method. SEG Technical Program Expanded Abstracts, 3867–3871.
    [Google Scholar]
  11. Jones, G.A., Kendall, J.M., Bastow, I.D. and Raymer, D.G. (2014) Locating microseismic events using borehole data. Geophysical Prospecting, 62(1), 34–39.
    [Google Scholar]
  12. Kaderli, J., McChesney, W.D. and Minkoff, S.E. (2015) Microseismic event estimation in noisy data via full waveform inversion. SEG Technical Program Expanded Abstracts, 1159–1164.
    [Google Scholar]
  13. Karpatne, A., Watkins, W., Read, J. and Kumar, V. (2018) Physics‐guided neural networks (PGNN): an application in lake temperature modeling. arXiv:1710.11431.
  14. Kingma, D.P. and Ba, J. (2014) Adam: a method for stochastic optimization. arXiv:1412.6980.
  15. Maxwell, S., Bennett, L., Jones, M. and Walsh, J. (2010) Anisotropic velocity modeling for microseismic processing: Part 1—Impact of velocity model uncertainty. SEG Technical Program Expanded Abstracts, 2130–2134.
    [Google Scholar]
  16. Moya, A. and Irikura, K. (2010) Inversion of a velocity model using artificial neural networks. Computers & Geosciences, 36, 1474–1483.
    [Google Scholar]
  17. Nelson, G.D. and Vidale, J.E. (1990) Earthquake locations by 3‐D finite‐difference travel times. Bulletin of the Seismological Society of America, 80(2), 395–410.
    [Google Scholar]
  18. Pei, D., Quirein, J.A., Cornish, B.E., Quinn, D. and Warpinski, N.R. (2009) Velocity calibration for microseismic monitoring: a very fast simulated annealing (VFSA) approach for joint‐objective optimization. Geophysics, 74(6), WCB47–WCB55.
    [Google Scholar]
  19. Raissi, M., Perdikaris, P. and Karniadakis, G.E. (2019) Physics‐informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.
    [Google Scholar]
  20. Song, C. and Alkhalifah, T. (2019) Microseismic event estimation based on an efficient wavefield inversion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11), 4664–4671.
    [Google Scholar]
  21. Song, C., Alkhalifah, T. and Waheed, U.B. (2021) Solving the frequency‐domain acoustic VTI wave equation using physics‐informed neural networks. Geophysical Journal International, 225, 846–859.
    [Google Scholar]
  22. Steppe, J.M. and BauerJr, K.W. (1997) Feature saliency measures. Computers & Mathematics with Applications, 33, 109–126.
    [Google Scholar]
  23. Sun, J., Xue, Z., Formel, S., Zhu, T. and Nakata, N. (2016) Full waveform inversion of passive seismic data for sources and velocities. SEG Technical Program Expanded Abstracts, 1405–1410.
    [Google Scholar]
  24. Tan, Y., He, C. and Mao, Z. (2018) Microseismic velocity model inversion and source location: the use of neighborhood algorithm and master station method. Geophysics, 83(4), KS49–KS63.
    [Google Scholar]
  25. Tarr, G.L. (1991) Multilayered feedforward neural networks for image segmentation. PhD thesis, Air Force Institute of Technology.
  26. Tian, Y. and Chen, X. (2005) A rapid and accurate two‐point ray tracing method in horizontally layered velocity model. ACTA Seismologica Sinica, 18(2), 154–161.
    [Google Scholar]
  27. Voytan, D. and Sen, M.K. (2020) Wave propagation with physics informed neural networks. SEG Technical Program Expanded Abstracts, 3477–3481.
    [Google Scholar]
  28. Waheed, U., Haghighat, E., Alkhalifah, T., Song, C. and Hao, Q. (2020a) Eikonal solution using physics‐informed neural networks. EAGE Annual Conference & Exhibition, 1–5.
    [Google Scholar]
  29. Waheed, U.B., Haghighat, E. and Alkhalifah, T. (2020b) Anisotropic eikonal solution using physics‐informed neural networks. SEG Technical Program Expanded Abstracts, 1566–1570.
    [Google Scholar]
  30. Wang, H. and Alkhalifah, T. (2018) Microseismic imaging using a source function independent full waveform inversion method. Geophysical Journal International, 214(1), 46–57.
    [Google Scholar]
  31. Yang, Y., Wen, J. and Chen, X. (2015) Improvements on particle swarm optimization algorithm for velocity calibration in microseismic monitoring. Earthquake Science, 28(4), 263–273.
    [Google Scholar]
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