AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
1
Download
4
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Q-factor estimation from Vertical Seismic Profiling (VSP) with deep learning algorithm, CUDNNLSTM

BEHNIA AZIZZADEH MEHMOST OLYA,  REZA MOHEBIAN*
Show Less
School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran.,
JSE 2023, 32(1), 89–104;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
Abstract

Azizzadeh Mehmandost Olya, B. and Mohebian, R., 2023. Q-factor estimation from Vertical Seismic Profiling (VSP) with deep learning algorithm, CUDNNLSTM. Journal of Seismic Exploration, 32: 89-104. As seismic reflection waves pass through the different layers and formations of the Earth, they are affected by the attenuation phenomenon that occurs after passing through each layer. One of the most effective and important criteria that can be used in the assessment of attenuation is to check the amount of the Q-value. This value can be used to monitor the amount of attenuation. A key point to remember is that the calculation of Q is always associated with various computational and operational challenges; in other words, the value of Q cannot be calculated in all of the wells that are in a hydrocarbon field. The purpose of this paper is to present an approach to the problem of estimating the Q-factor by using the latest artificial intelligence method, which is deep learning. By using the CUDNNLSTM algorithm in this paper, we were able to estimate the Q-factor accurately. we achieved an accuracy of 98.5% and a validation loss of 1.3% in estimating the Q-factor. With our Q-factor estimating by deep learning, along with speeding up calculations, we will be able to resolve the problem of lacking suitable VSP seismic data to calculate the Q-factor, as well.

Keywords
Q-factor
Vertical Seismic Profiling (VSP)
deep learning algorithm
CUDNNLSTM
References
  1. An, Y., 2015. Fracture prediction using prestack Q calculation and attenuation
  2. anisotropy. Appl. Geophys., 12: 432-440.
  3. doi:https://doi.org/10.1007/s11770-015-0493-1
  4. Blias, E., 2012. Accurate interval Q-factor estimation from VSP data. Geophysics, 77(3):
  5. 1MJ-Z74. doi:https://doi.org/10.1190/geo2011-0270.1
  6. Castagna, J., Sun, S. and Siegfried, R., 2003. Instantaneous spectral analysis: Detection
  7. of low-frequency shadows associated with hydrocarbons. The Leading Edge, 22:
  8. 96-173. doi:https://doi.org/10.1190/1.1559038
  9. Engelhard, L., 1996. Determination of seismic-wave attenuation by complex trace
  10. analysis. Geophys. J. Internat., 125: 608-622.
  11. doi:https://doi.org/10.1111/j.1365-246X.1996.tb00023.x
  12. Gladwin, M. and Stacey, F., 1974. Anelastic degradation of acoustic pulses in rock. Phys.
  13. Earth Planet. Inter., 8: 332-336.
  14. doi:https://doi.org/10.1016/003 1-9201(74)9004 1-7
  15. Guerra, R. and Leaney, S., 2006. Q(z) model building using walkaway VSP data.
  16. Geophysics, 71(5). doi:https://doi.org/10.1190/1.2329866
  17. Haase, A. and Stewart, R.R., 2004. Attenuation estimates from VSP and log data.
  18. Expanded Abstr., 73rd Ann. Internat. SEG Mtg., Dallas: 2586.
  19. Hauge, P., 1981. Measurements of attenuation from vertical seismic profiles. Geophysics,
  20. 46: 1508-1618. doi:https://doi.org/10.1190/1.1441161
  21. Hochreiter, S. and Schmidhuber, J., 1997. Long Short-Term Memory. Neural Computat.,
  22. 9:1735-1780. doi:10.1162/neco.1997.9.8.1735
  23. Jannsen, D., Voss, J. and Theilen, F., 1985. Comparison of methods to determine Q in
  24. shallow marine sediments from vertical reflection seismograms. Geophys. Prosp.,
  25. doi:https://doi.org/10.1111/j.1365-2478.1985.tb00762.x
  26. Levenberg, K., 1944. A method for the solution of certain non-linear problems in least
  27. squares. Quart. Appl. Mathemat., 2: 164-168.
  28. Liu, N., Zhang, B., Gao, J., Gao, Z. and Li, S., 2018. Seismic attenuation estimation using
  29. the modified log spectral ratio method. J. Appl. Geophys., 159: 386-394.
  30. doi:https://doi.org/10.1016/j.jappgeo.2018.09.014
  31. Marquardt, D., 1963. An algorithm for least-squares estimation of nonlinear parameters.
  32. J. Soc. Industr. Appl. Mathemat., 11: 431-441.
  33. Matheney, M. and Nowack, R., 1995. Seismic attenuation values obtained from
  34. instantaneous-frequency matching and spectral ratios. Geophys. J. Internat., 123:
  35. 1-15. doi:https://doi.org/10.1111/j.1365-246X.1995.tb06658.x
  36. Mirzanejad, M.T., Tran, K. and Wang, Y., 2022. Three-dimensional Gauss—Newton
  37. constant-Q viscoelastic full-waveform inversion of near-surface seismic
  38. wavefields. Geophys. J. Internat., 231:1767-1785.
  39. doi:https://doi.org/10.1093/gji/ggac287
  40. Rubino, J., Velis, D. and Holliger, K., 2012. Permeability effects on the seismic response
  41. of gas reservoirs. Geophys. J. Internat., 189: 448-468.
  42. doi:https://doi.org/10.1111/j.1365-246X.2011.05322.x
  43. Sain, K. and Kumar Singh, A., 2011. Seismic quality factors across a bottom simulating
  44. reflector in the Makran Accretionary Prism, Arabian Sea. Mar. Petrol. Geol., 28:
  45. 1838-1843. doi:https://doi.org/10.1016/j.marpetgeo.2011.03.013
  46. Sangwan, P., Kumar, D., Chakraborty, S., Mundayat, V. and Balasubramaniam, M.,
  47. Nonlinear approach to spectral ratio method for estimation of seismic
  48. quality factor from VSP data. J. Appl. Geophys., 167: 33-41.
  49. doi:https://doi.org/10.1016/j.jappgeo.2019.04.001
  50. Tonn, R., 1990. The determination of the seismic quality factor Q from VSP data: a
  51. comparison of different computational methods. Geophys. Prosp., 39: 1-27.
  52. doi:https://doi.org/10.1111/j.1365-2478.1991.tb00298.x
  53. Turhan Taner, M. and Treitel, S., 2003.. A robust method for Q estimation. Expanded
  54. Abstr., 73rd Ann. Internat. SEG Mtg., Dallas: 2452.
  55. doi:https://doi.org/10.1190/1.1818032
  56. Vesnaver, A., Bohm, G., Busetti, M., Dal Cin, M. and Zgur, F., 2021. Broadband Q-
  57. factor Imaging for Geofluid Detection in the Gulf of Trieste (Northern Adriatic
  58. Sea). Solid Earth Geophys., 9: 640194.
  59. doi:https://doi.org/10.3389/feart.202 1.640194
  60. Wang, S., Yang, D., Li, J. and Song, H., 2015. Q-factor estimation based on the method
  61. of logarithmic spectral area difference. Geophysics, 80: 1942-2156.
  62. doi:https://doi.org/10.1190/geo2014-0257.1
  63. Xue, Y.-J., Cao, J.-X, Wang, X.-J. and Du, H.-K., 2020. Estimation of seismic quality
  64. factor in the time-frequency domain using variational mode decomposition.
  65. Geophysics, 85(4): 1JA-Z18.
  66. doi:https://doi.org/10.1190/geo2019-0404.1
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept