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Rayleigh wave dispersion curve inversion combining with GA and DSL

YUHANG LEI,  HONGYAN SHEN,  SHENGJIE XIE,  YIDONG LI
JSE 2018, 27(2), 151–165;
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/ )
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Abstract

Lei, Y.H., Shen, H.Y., Xie, S.J. and Li, Y.D., 2018. Rayleigh wave dispersion curve inversion combining with GA and DSL. Journal of Seismic Exploration, 27: 151-165. Rayleigh wave dispersion curve inversion is a multi-parameter highly non-linear iterative optimization process. The conventional single linear or non-linear inversion method has some limitations for the complex seismic geologic conditions, which can lead to more prominent multi-solution problem. But the defects both of the methods can be supplemented by the advantages of each other. In order to further improve the inversion accuracy, we proposed a joint inversion method via complementing and nesting the linear (damping least squares) and non-linear (genetic algorithm) methods. Firstly, the genetic algorithm (GA) is utilized based on the loose constraints of prior geological information to lock in the target near the global optimal solution. Then, use the damping least squares (DLS) method to achieve higher precision of Rayleigh wave dispersion curve inversion. The effectiveness of the method has been verified by a typical layered model. And we use the method to further process actual seismic data. Results show that the method not only absorbs the advantages of GA with global optimization and strong adaptability, but also inherits the advantages of DLS with fast convergence and stable inversion. And better results are achieved in suppressing multi-solution, getting rid of the initial model highly dependent, and improving inversion accuracy.

Keywords
Rayleigh wave
dispersion curves
genetic algorithm (GA)
damping least squares (DLS)
joint inversion
References
  1. Beaty, K.S., Schmitt, D.R. and Sacchi, M., 2002. Simulated annealing inversion of
  2. multimode Rayleigh wave dispersion curves for geological structure. Geophys. J.
  3. Internat., 151: 622- 631. doi: 10.1046/ j.1365-246X.2002.01809. x.
  4. Caldwell, W.B., Klemperer, S.L., Rai, S.S. and Lawrence, J.F., 2009. Partial melt in the
  5. upper-middle crust of the northwest Himalaya revealed by Rayleigh wave dispersion.
  6. Tectonophys, 477: 58-65. doi: 10.1016/j.tecto. 2009.01.013.
  7. Chang, J.P., de Ridder, S.A. and Biondi, B.L., 2016. High-frequency Rayleigh-wave
  8. tomography using traffic noise from long beach, California. Geophysics, 81:
  9. B43-B53. doi: 10.1190/GEO-2015 -0415.1.
  10. Chen, X. and Sun, J., 2006. An improved equivalent homogenous half-space method and
  11. reverse fitting analysis of Rayleigh wave dispersion curve (in Chinese). Chin. J.
  12. Geophys., 49: 569-576. doi: 10.1002/ cjg2.859.
  13. Cui, J., Liao, Z. and Huang, Z., 1994. Simplified stripping method for inversing shear
  14. wave velocity (in Chinese). China Civil Engin. J., 27: 50-58.
  15. Guo, Z., Chen, Y. and Yin, W., 2015. Three-dimensional crustal model of Shanxi graben
  16. from 3D joint inversion of ambient noise surface wave and Bouguer gravity
  17. anomalies (in Chinese). Chin. J. Geophys., 58: 821-831. doi: 10.6038/cjg20150312.
  18. Junior, S.B.L., Prado, R.L. and Mendes, R.M., 2012. Application of multichannel
  19. analysis of surface waves method (MASW) in an area susceptible to landslide at
  20. Ubatuba City, Brazil. Revis. Brasil. Geofis., 30: 213-224.
  21. Knopoff, L., 1964. A matrix method for elastic wave problems. Bull. Seismol. Soc. Am.,
  22. 54: 431-438.
  23. Laake, A., Strobbia, C. and Cutts, A., 2008. Integrated approach to 3D near surface
  24. characterization in desert region. First Break, 26: 109-112.
  25. Liu, B.. Liu, Z.. Song, J.. Nie. L.. Wang, C. and Chen, L., 2017. Joint inversion method
  26. of 3D electrical resistivity detection base on inequality constraints (in Chinese), Chin.
  27. J. Geophys., 60: 820-832. doi:10.6038/cjg 20170232.
  28. Luo, Y., Xia, J., Liu, J. and Liu, Q., 2008. Joint inversion of fundamental and higher
  29. mode Rayleigh waves (in Chinese). Chin. J. Geophys., 51: 242-249.
  30. Lu, J., Li, S., Li, W. and Tang, L., 2014. A hybrid inversion method of damped least
  31. squares with simulated annealing used for Rayleigh wave dispersion curve inversion.
  32. Earthq. Engineer. Vibrat., 13: 13-21. doi:10.1007/s11803-014-0208-2.
  33. Malagnini, L., Herrmann, R.B., Biella, G. and Franco R.D., 1995. Rayleigh waves in
  34. Quaternary alluvium from explosive sources: determination of shear-wave velocity
  35. and Q structure. Bull. Seismol. Soc. Am., 85: 900-922.
  36. Madsen, K., Nielsen, H.B. and Tingleff, O., 2004. Methods for Non-Linear Least Squares
  37. Problems. Lecture Note.
  38. Moro, G.D., Pipan, M., Forte, E. and Finetti, I., 2003. Determination of Rayleigh wave
  39. dispersion curves for near surface applications in unconsolidated sediments.
  40. Expanded Abstr., 73rd Ann. Internat. SEG Mtg., Dallas: 1247-1250.
  41. doi:10.1190/1.1817508.
  42. Moro, G.D., Pipan, M. and Gabrielli, P., 2007a. Rayleigh wave dispersion curve
  43. inversion via genetic algorithms and Marginal Posterior Probability Density
  44. estimation. J. Appl. Geophys., 61: 39-55. doi:10.1016/j. jappgeo.2006.04.002.
  45. Moro, G.D., Pipan, M. and Gabrielli, P., 2007b. Joint inversion of surface wave
  46. dispersion curves and reflection travel times via multi-objective evolutionary
  47. algorithms. J. Appl. Geophys., 61: 56-81. doi:10.1016/j.jappgeo.2006.04.001.
  48. Nazarian, S., Stokoe, K.H. Il and Hudson, W., 1983. Use of spectral analysis of surface
  49. waves method for determination of module and thicknesses of pavement systems.
  50. Transport. Res. Rec., 930: 38-45.
  51. Park, C.B., Mille R.D. and Xia, J., 1999. Multichannel analysis of surface wave.
  52. Geophysics, 64: 800-808. doi:10.1190/ 1.1444590.
  53. Meier, R.W. and Rix, G.J., 1993. An initial study of surface wave inversion using
  54. artificial neural networks. Geotech. Test. J., 16: 425-431. doi:10.1520/GTJ10282J.
  55. Shen, H., Chen, C., Yan, Y. and Zhang, B., 2016. Multiple-transient surface wave phase
  56. velocity analysis in expanded f-k domain and its application. J. Seismic Explor., 25:
  57. 299-319.
  58. Strobbia, C.L., Vermeer, P.L., Laake, A., Glushchenko, A. and Re, S., 2010a. Surface
  59. waves: processing, inversion and removal. First break, 28: 85-91.
  60. Strobbia, C.L., Eman, A.E., Al-Genai, J. and Roth, J., 2010b. Rayleigh wave inversion
  61. for the near-surface characterization of shallow targets in a heavy oil field in Kuwait.
  62. First Break, 28: 103-109.
  63. Strobbia, C.L., Laake, A., Vermeer, P. and Glushchenko, A, 2011. Surface waves: use
  64. them then lose them. Surface-wave analysis, inversion and attenuation in land
  65. reflection seismic surveying. Near-Surf. Geophys., 9: 503-513.
  66. doi: 10.3997/1873-0604.201 1022.
  67. Sussmann, T.R., Thompson, H.B., Stark, T.D., Wilk. S.T. and Ho, C.L., 2015. Use of
  68. seismic surface wave testing to assess track substructure condition. Railway
  69. Engineering Conf., Edinburgh.
  70. Xia, J., Miller, R. and Park, C.B., 1999. Estimation of near-surface shear-wave velocity
  71. by inversion of Rayleigh wave. Geophysics, 64: 691-700. doi:10.1190/1.1444578.
  72. Xia, J., Milller, R., Park, C., Hunter, J., Harris, J. and Ivanov, J., 2002a. Comparing shear
  73. wave velocity profiles inverted form multichannel surface wave with borehole
  74. measurements. Soil Dynam. Earthq. Engineer., 22: 181-190.
  75. doi:10.1016/S0267-7261(02)00008-8.
  76. Xia, J., Milller, R., Park, C. and Tian, G., 2002b. Determining Q of near-surface materials
  77. form Rayleigh waves. J. Appl. Geophys., 51: 121-129.
  78. doi: 10.1016/S0926-985 1(02)00228-8.
  79. Xia, J., Miller, R.D., Park, C.B. and Tian, G., 2003. Inversion of high frequency surface
  80. waves with fundamental and higher modes. J. Appl. Geophys., 52: 45-57.
  81. doi:10.1016/ S0926- 9851(02)00239-2.
  82. Xia, J., Miller, R., Park, C.B., Ivanov, J., Tian, G. and Chen, C., 2004. Utilization of
  83. high-frequency Rayleigh waves in near-surface geophysics. The Leading Edge, 23
  84. 753-759. doi:10.1190/1. 1786895.
  85. Xia, J., Xu, Y., Luo, Y., Miller, R., Cakir, R. and Zeng, C., 2012. Advantages of using
  86. multichannel analysis of Love waves (MALW) to estimate near surface shear-wave
  87. velocity. Surv. Geophys., 33: 841-860. doi: 10.1007/s10712-012-9174-2.
  88. Xia, J., Gao, X., Pan, Y., Shen, C. and Yi, X., 2015. New findings in high-frequency
  89. surface wave method. Chin. J. Geophys., 58: 2591-2605. doi:10.6038/cig20150801.
  90. Yamanaka, H. and Ishida, H., 1996. Application of genetic algorithms to an inversion of
  91. surface-wave dispersion data. Bull. Seismol. Soc. Am., 86: 436-444.
  92. Yilmaz, O., Eser, M. and Berilgen, M., 2005. A case study of seismic zonation in
  93. municipal areas. The Leading Edge, 25: 319-330. doi: 10.1190/1.2147874
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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