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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: 13 July 2017 | Accepted: 12 January 2018 | Published: 1 April 2018
© 2018 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 ofmultimode Rayleigh wave dispersion curves for geological structure. Geophys. J.
  2. Internat., 151: 622- 631. doi: 10.1046/ j.1365-246X.2002.01809. x.
  3. Caldwell, W.B., Klemperer, S.L., Rai, S.S. and Lawrence, J.F., 2009. Partial melt in theupper-middle crust of the northwest Himalaya revealed by Rayleigh wave dispersion.
  4. Tectonophys, 477: 58-65. doi: 10.1016/j.tecto. 2009.01.013.
  5. Chang, J.P., de Ridder, S.A. and Biondi, B.L., 2016. High-frequency Rayleigh-wavetomography using traffic noise from long beach, California. Geophysics, 81:B43-B53. doi: 10.1190/GEO-2015 -0415.1.
  6. Chen, X. and Sun, J., 2006. An improved equivalent homogenous half-space method andreverse fitting analysis of Rayleigh wave dispersion curve (in Chinese). Chin. J.Geophys., 49: 569-576. doi: 10.1002/ cjg2.859.
  7. Cui, J., Liao, Z. and Huang, Z., 1994. Simplified stripping method for inversing shearwave velocity (in Chinese). China Civil Engin. J., 27: 50-58.
  8. Guo, Z., Chen, Y. and Yin, W., 2015. Three-dimensional crustal model of Shanxi grabenfrom 3D joint inversion of ambient noise surface wave and Bouguer gravityanomalies (in Chinese). Chin. J. Geophys., 58: 821-831. doi: 10.6038/cjg20150312.
  9. Junior, S.B.L., Prado, R.L. and Mendes, R.M., 2012. Application of multichannelanalysis of surface waves method (MASW) in an area susceptible to landslide at
  10. Ubatuba City, Brazil. Revis. Brasil. Geofis., 30: 213-224.
  11. Knopoff, L., 1964. A matrix method for elastic wave problems. Bull. Seismol. Soc. Am.,54: 431-438.
  12. Laake, A., Strobbia, C. and Cutts, A., 2008. Integrated approach to 3D near surfacecharacterization in desert region. First Break, 26: 109-112.
  13. Liu, B.. Liu, Z.. Song, J.. Nie. L.. Wang, C. and Chen, L., 2017. Joint inversion methodof 3D electrical resistivity detection base on inequality constraints (in Chinese), Chin.
  14. J. Geophys., 60: 820-832. doi:10.6038/cjg 20170232.
  15. Luo, Y., Xia, J., Liu, J. and Liu, Q., 2008. Joint inversion of fundamental and highermode Rayleigh waves (in Chinese). Chin. J. Geophys., 51: 242-249.
  16. Lu, J., Li, S., Li, W. and Tang, L., 2014. A hybrid inversion method of damped leastsquares with simulated annealing used for Rayleigh wave dispersion curve inversion.
  17. Earthq. Engineer. Vibrat., 13: 13-21. doi:10.1007/s11803-014-0208-2.
  18. Malagnini, L., Herrmann, R.B., Biella, G. and Franco R.D., 1995. Rayleigh waves in
  19. Quaternary alluvium from explosive sources: determination of shear-wave velocityand Q structure. Bull. Seismol. Soc. Am., 85: 900-922.
  20. Madsen, K., Nielsen, H.B. and Tingleff, O., 2004. Methods for Non-Linear Least SquaresProblems. Lecture Note.
  21. Moro, G.D., Pipan, M., Forte, E. and Finetti, I., 2003. Determination of Rayleigh wavedispersion curves for near surface applications in unconsolidated sediments.
  22. Expanded Abstr., 73rd Ann. Internat. SEG Mtg., Dallas: 1247-1250.doi:10.1190/1.1817508.
  23. Moro, G.D., Pipan, M. and Gabrielli, P., 2007a. Rayleigh wave dispersion curveinversion via genetic algorithms and Marginal Posterior Probability Densityestimation. J. Appl. Geophys., 61: 39-55. doi:10.1016/j. jappgeo.2006.04.002.
  24. Moro, G.D., Pipan, M. and Gabrielli, P., 2007b. Joint inversion of surface wavedispersion curves and reflection travel times via multi-objective evolutionaryalgorithms. J. Appl. Geophys., 61: 56-81. doi:10.1016/j.jappgeo.2006.04.001.
  25. Nazarian, S., Stokoe, K.H. Il and Hudson, W., 1983. Use of spectral analysis of surfacewaves method for determination of module and thicknesses of pavement systems.Transport. Res. Rec., 930: 38-45.
  26. Park, C.B., Mille R.D. and Xia, J., 1999. Multichannel analysis of surface wave.Geophysics, 64: 800-808. doi:10.1190/ 1.1444590.
  27. Meier, R.W. and Rix, G.J., 1993. An initial study of surface wave inversion usingartificial neural networks. Geotech. Test. J., 16: 425-431. doi:10.1520/GTJ10282J.
  28. Shen, H., Chen, C., Yan, Y. and Zhang, B., 2016. Multiple-transient surface wave phasevelocity analysis in expanded f-k domain and its application. J. Seismic Explor., 25:299-319.
  29. Strobbia, C.L., Vermeer, P.L., Laake, A., Glushchenko, A. and Re, S., 2010a. Surfacewaves: processing, inversion and removal. First break, 28: 85-91.
  30. Strobbia, C.L., Eman, A.E., Al-Genai, J. and Roth, J., 2010b. Rayleigh wave inversionfor the near-surface characterization of shallow targets in a heavy oil field in Kuwait.First Break, 28: 103-109.
  31. Strobbia, C.L., Laake, A., Vermeer, P. and Glushchenko, A, 2011. Surface waves: usethem then lose them. Surface-wave analysis, inversion and attenuation in landreflection seismic surveying. Near-Surf. Geophys., 9: 503-513.doi: 10.3997/1873-0604.201 1022.
  32. Sussmann, T.R., Thompson, H.B., Stark, T.D., Wilk. S.T. and Ho, C.L., 2015. Use ofseismic surface wave testing to assess track substructure condition. RailwayEngineering Conf., Edinburgh.
  33. Xia, J., Miller, R. and Park, C.B., 1999. Estimation of near-surface shear-wave velocityby inversion of Rayleigh wave. Geophysics, 64: 691-700. doi:10.1190/1.1444578.
  34. Xia, J., Milller, R., Park, C., Hunter, J., Harris, J. and Ivanov, J., 2002a. Comparing shearwave velocity profiles inverted form multichannel surface wave with boreholemeasurements. Soil Dynam. Earthq. Engineer., 22: 181-190.doi:10.1016/S0267-7261(02)00008-8.
  35. Xia, J., Milller, R., Park, C. and Tian, G., 2002b. Determining Q of near-surface materialsform Rayleigh waves. J. Appl. Geophys., 51: 121-129.doi: 10.1016/S0926-985 1(02)00228-8.
  36. Xia, J., Miller, R.D., Park, C.B. and Tian, G., 2003. Inversion of high frequency surfacewaves with fundamental and higher modes. J. Appl. Geophys., 52: 45-57.doi:10.1016/ S0926- 9851(02)00239-2.
  37. Xia, J., Miller, R., Park, C.B., Ivanov, J., Tian, G. and Chen, C., 2004. Utilization ofhigh-frequency Rayleigh waves in near-surface geophysics. The Leading Edge, 23753-759. doi:10.1190/1. 1786895.
  38. Xia, J., Xu, Y., Luo, Y., Miller, R., Cakir, R. and Zeng, C., 2012. Advantages of usingmultichannel analysis of Love waves (MALW) to estimate near surface shear-wavevelocity. Surv. Geophys., 33: 841-860. doi: 10.1007/s10712-012-9174-2.
  39. Xia, J., Gao, X., Pan, Y., Shen, C. and Yi, X., 2015. New findings in high-frequencysurface wave method. Chin. J. Geophys., 58: 2591-2605. doi:10.6038/cig20150801.
  40. Yamanaka, H. and Ishida, H., 1996. Application of genetic algorithms to an inversion ofsurface-wave dispersion data. Bull. Seismol. Soc. Am., 86: 436-444.
  41. Yilmaz, O., Eser, M. and Berilgen, M., 2005. A case study of seismic zonation inmunicipal 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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