AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
1
Download
41
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Nonlinear prestack inversion using the reflectivity method and quantum particle swarm optimization

XINGYE LIU1 ,  XIAOHONG CHEN2 ,  LI CHEN3 ,  JINGYE LI
Show Less
2 State Key Laboratory of Petroleum Resources and Prospecting, National Engineering Laboratory for Offshore Oil Exploration, China University of Petroleum-Beijing, Beijing 102249, P.R. China.,
3 BGP INC, China National Petroleum Corporation Southwest Geophysical Research Institute, Chengdu 610000, P.R. China.,
JSE 2020, 29(4), 305–326;
Submitted: 21 May 2019 | Accepted: 9 March 2020 | Published: 1 August 2020
© 2020 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

Liu, X.Y., Chen, X.H., Chen, L. and Li, J-Y., 2020. Nonlinear prestack inversion using the reflectivity method and quantum particle swarm optimization. Journal of Seismic Exploration, 29: 305-326. The vectorized reflectivity method is an economical and reliable method for solving the elastic wave equation under a one-dimensional assumption. It can obtain the information of full wave field and accurately describe diverse propagation effects of the seismic wave. The inversion method based on the reflectivity method finds suitable inverted parameters by minimizing the error between the synthetic seismograms and observed seismic data. The non-linear inversion problem can be solved by a gradient-based method or a global optimization method. The former relies heavily on the staring model and is prone to fall into a local minima. The global optimization algorithms demand for an accurate and rapid calculation of the forward modeling. The vectorize reflectivity method satisfies these requirements. We introduce and improve the quantum particle swarm optimization algorithm (QPSO), which has significant advantages in global search, into seismic inversion based on the reflectivity method, developing a nove nonlinear prestack inversion method in angle gather domain. The vectorized reflectivity method is able to synthesize seismic records quickly and accurately. Using the QPSO relieves reliance on the initial model. The Cauchy distribution is introduced to combat the possible premature convergence. The benefits of the vectorized reflectivity method and QPSO are combined. We apply the technique to model data and field data, which demonstrates the feasibility and reliability of the new method.

Keywords
prestack inversion
reflectivity method
wave propagation effects
quantum particle swarm optimization
elastic wave equation
References
  1. Akga, I. and Basokur, A.T., 2010. Extraction of structure-based geoelectric models byhybrid genetic algorithms. Geophysics, 75(1): F15-F22.
  2. Barboza, F.M., Medeiros, W.E. and Santana, J.M.. 2019. A user-driven feedbackapproach for 2D direct current resistivity inversion based on particle swarmoptimization. Geophysics, 84(2): E105-E124.
  3. Buland, A., and Omre, H., 2003. Bayesian linearized AVO inversion. Geophysics, 68(1):185-198.
  4. Chen, L., 2017. Prestack AVO Inversion Based on the Reflectivity method using
  5. Bayesian theory. China University of Petroleum-Beijing.
  6. Ciucivara, A. and Sen, M.K., 2009. A reflectivity method for laterally varying media.
  7. Expanded Abstr., 79th Ann. Internat. SEG Mtg., Houston: 2707-2711.
  8. Cui, X., Gao, J., Zhang, B. and Wang, Z., 2016. Poststack impedance inversion usingimproved particle swarm optimization. Expanded Abstr., 86th Ann. Internat. SEGMtg., Dallas: 3809-3813.
  9. Dariu, H., Garotta, R. and Granger, P.Y., 2003. Simultaneous inversion of PP and PSwave AVO/AVA data using simulated annealing. Expanded Abstr., 73rd Ann.Internat. SEG Mtg, Dallas: 120-123.
  10. Davis, L., 1991. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York.
  11. De Jong. K.. 1975. An analvsis of the behavior of a class of genetic adaptive systems. Ph.D. Thesis, University of Michigan.
  12. Fryer, G.J., 1980. A slowness approach to the reflectivity method of seismogramsynthesis. Geophys. J. Internat., 63: 747-758.
  13. Fuchs, K. and Miiller, G., 1971. Computation of synthetic seismograms with thereflectivity method and comparison with observations. Geophys. J. Internat., 23:417-433.
  14. Garabito, G., 2018. Global optimization strategies for implementing 3Dcommon-reflection-surface stack using the very fast simulated annealing algorithm:
  15. Application to real land data. Geophysics, 83(4): V253-V261.
  16. Gouveia, W.P. and Scales, J.A., 1998. Bayesian seismic waveform inversion: Parameterestimation and uncertainty analysis. J. Geophys. Res., Solid Earth, 103(B2):2759-2779.
  17. Huang, K.Y., Shen, L.C., Chen, K.J. and Huang, M.C., 2012. Particle swarmoptimization in multilayer perceptron learning for well log data inversion.
  18. Expanded Abstr., 82nd Ann. Internat. SEG Mtg., Las Vegas: 1-5.
  19. Jia, Y.G., Li, X.F., Zhang, M.G., Li, X.F. and Wang, T.K., 2005. Summarization ofresearch on seismic wave nonlinear inversion method. J. Disast. Prev. Mitigat.Engineer., 25: 345-351.
  20. Kennett, B.L.N., 1979. Theoretical reflection seismograms for elastic media. Geophys.Prosp., 27: 301-321.
  21. Kennett B.L.N., 1983. Seismic Wave Propagation in Stratified Media. CambridgeUniversity Press, Cambridge.
  22. Li, T. and Mallick, S.. 2013. Prestack waveform inversion of four-component.two-azimuth surface seismic data for orthorhombic elastic media parameters usinga nondominated sorting genetic algorithm. Expanded Abstr., 83rd Ann. Internat.SEG Mtg., Houston: 3283-3287.
  23. Li, T. and Mallick, S.. 2014. Prestack waveform inversion of three-component.two-azimuth surface seismic data for azimuthally dependent anisotropic parametersusing a parallelized nondominated sorting genetic algorithm. Expanded Abstr., 84thAnn. Internat. SEG Mtg., Denver: 3205-3210.
  24. Liu, H., Li, J., Chen, X., Hou, B. and Chen, L., 2016. Amplitude variation with offsetinversion using the reflectivity method. Geophysics, 81(4): R185-R195.
  25. Liu, W., 2017. The research of pseudo-wells’ construction method using a fastnon-dominated sorting genetic algorithm. China University of Petroleum-Beijing.
  26. Liu, X., Li, J., Chen, X., Guo, K., Li, C., Zhou, L., and Cheng, J., 2018. Stochasticinversion of facies and reservoir properties based on multi-point geostatistics. J.Geophys. Engineer., 15: 2455-2468.
  27. Liu, W.. Wang. Y.C.. Li. J-Y.. Liu. X.O. and Xie. W.. 2018. Prestack AVA jointinversion of PP and PS waves using the vectorized reflectivity method. Appl.Geophys., 15: 448-465.
  28. Luo, H.M., 2007. Quantum genetic algorithm and its application to inversion ofgeophysics. China University of Geosciences.
  29. Ma, Y., Loures, L. and Margrave, G.F., 2004. Seismic modeling with the reflectivitymethod. CREWES Research Report, 16-1.
  30. Mallick, S. and Frazer, L.N., 1987. Practical aspects of reflectivity modeling. Geophysics,52: 1355-1364.
  31. Mallick, S. and Frazer, L.N., 1990. Computation of synthetic seismograms for stratifiedazimuthally anisotropic media. J. Geophys. Res. - Solid Earth, 95(B6): 8513-8526.
  32. Mallick, S. and Adhikari, S., 2015. Amplitude-variation-with-offset and prestack-waveform inversion: A direct comparison using a real data example from the Rock
  33. Springs Uplift, Wyoming, USA. Geophysics, 80(2): B45-B59.
  34. Miiller, G., 1985. The reflectivity method - a tutorial. J. Geophys., 58: 153-174.Padhi, A. and Mallick, S., 2013. Accurate estim
  35. Sen, M.K. and Roy, I.G., 2003. Computation of differential seismograms and iterationadaptive regularization in prestack waveform inversion. Geophysics, 68:2026-2039.
  36. Shaw, R. and Srivastava, S., 2007. Particle swarm optimization: A new tool to invertgeophysical data. Geophysics, 72(2): F75-F83.
  37. Sun, J., Feng, B. and Xu, W., 2004. Particle swarm optimization with particles havingquantum behavior. Proc. 2004 Congr. Evolution. Computat., Portland, OR:325-331.
  38. Sun, J., Liu, J. and Xu, W., 2007. Using quantum-behaved particle swarm optimizationalgorithm to solve non-linear programming problems. Internat. J. Comput.Mathemat., 84: 261-272.
  39. Van den Bergh, F. and Engelbrecht, A.P., 2002. A new locally convergent particle swarmoptimiser. IEEE Internat. Conf. Systems, Man Cybernet., Tunis: 3-6.
  40. Xu, W. and Sun, J., 2005. Adaptive parameter selection of quantum-behaved particleswarm optimization on global level. Internat. Conf. Intellig. Comput., Hefei, China:420-428.
  41. Yin, C. and Hodges, G., 2007. Simulated annealing for airborne EM inversion.Geophysics, 72(4): F189-F195.
  42. Zhao, H., Ursin, B. and Amundsen, L., 1994. Frequency-wavenumber elastic inversion ofmarine seismic data. Geophysics, 59: 1868-1881.
  43. Zhou, L., Li, J., Chen, X., Liu, X. and Chen, L., 2017. Prestack AVA inversion of exact
  44. Zoeppritz equations based on modified Trivariate Cauchy distribution. J. Appl.Geophys., 138: 80-90.
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