1887
Volume 21, Issue 1
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

Abstract

We present a global–local full‐waveform inversion (FWI) of surface waves to estimate a high‐resolution shear wave velocity model of the near‐surface at the site of Grenoble (France). The seismic data we use have been acquired in the framework of the InterPACIFIC project. A first attempt is made by employing the multichannel analysis of surface waves (MASW) to invert the fundamental modes of Rayleigh waves. This inversion is solved through a global optimization driven by the neighbourhood algorithm. However, the limiting 1D assumption, together with the severely ill‐posedness of the MASW inversion motivate us to exploit all the information content of the recorded Rayleigh waves (i.e., travel‐times and amplitudes) by implementing a global–local FWI approach. We first perform a global FWI in which a genetic algorithm (GA) is used to minimize the L2 norm difference between observed and simulated seismic waveforms up to 30 Hz. This inversion employs a two‐grid scheme in which the subsurface is described by a fine grid input to the forward modelling, whereas the unknowns are defined on a coarse grid. A bilinear interpolation brings the coarse‐grid model into the fine grid. To accelerate the convergence, two strategies are considered; offset marching and frequency marching. Then, the long‐wavelength velocity profile provided by the GA inversion is used as the starting point for a local FWI for further refinement of the predicted model. In this case, we employ a frequency‐marching strategy up to a maximum frequency of 60 Hz. Both the global and local approaches use a finite difference code for the forward computation. Despite the limited a priori information infused into the inversion framework, the presented global–local FWI scheme provides reliable results as demonstrated by the good match between recorded and predicted seismograms and by the agreement of the estimated velocity with both nearby borehole data and stratigraphic information on the study site. Our global–local strategy also achieves better data fitting and improved matching with the available borehole data with respect to the velocity profile estimated when the local FWI is started from the MASW predictions.

Loading

Article metrics loading...

/content/journals/10.1002/nsg.12243
2023-01-18
2024-04-26
Loading full text...

Full text loading...

References

  1. Aleardi, M. & Stucchi, E. (2021) A hybrid residual neural network‐Monte Carlo approach to invert surface wave dispersion data. Near Surface Geophysics, 19(4), 397–414.
    [Google Scholar]
  2. Aleardi, M., Pierini, S. & Sajeva, A. (2019) Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems. Geophysics, 84(5), R767–R781.
    [Google Scholar]
  3. Aleardi, M. & Mazzotti, A. (2017) 1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach. Geophysical Prospecting, 65(1), 64–85.
    [Google Scholar]
  4. Aleardi, M., Vinciguerra, A., Stucchi, E. & Hojat, A. (2021) Stochastic electrical resistivity tomography with ensemble smoother and deep convolutional autoencoders. Near Surface Geophysics, 20(2), 160–177.
    [Google Scholar]
  5. Aleardi, M., Vinciguerra, A., Stucchi, E. & Hojat, A. (2022) Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator. Geophysical Prospecting, 70(5), 938–957.
    [Google Scholar]
  6. Alkhalifah, T.A. (2016) Full waveform inversion in an anisotropic world: where are the parameters hiding?Houten, The Netherlands: EAGE.
    [Google Scholar]
  7. Bergamo, P., Boiero, D. & Socco, L.V. (2012) Retrieving 2D structures from surface‐wave data by means of space‐varying spatial windowing. Geophysics, 77(4), EN39–EN51.
    [Google Scholar]
  8. Bergamo, P. & Socco, L.V. (2014) Detection of sharp lateral discontinuities through the analysis of surface‐wave propagation. Geophysics, 79(4), EN77–EN90.
    [Google Scholar]
  9. Bohlen, T., Kugler, S., Klein, G. & Theilen, F. (2004) 1.5 D inversion of lateral variation of Scholte‐wave dispersion. Geophysics, 69(2), 330–344.
    [Google Scholar]
  10. Boiero, D., Bergamo, P., Bruno Rege, R. & Socco, L.V. (2011) Estimating surface‐wave dispersion curves from 3D seismic acquisition schemes: Part 1—1D models. Geophysics, 76(6), G85–G93.
    [Google Scholar]
  11. Cercato, M. (2009) Addressing non‐uniqueness in linearized multichannel surface wave inversion. Geophysical Prospecting, 57(1), 27–47.
    [Google Scholar]
  12. Cercato, M. (2011) Global surface wave inversion with model constraints. Geophysical Prospecting, 59(2), 210–226.
    [Google Scholar]
  13. Dal Moro, G., Pipan, M. & Gabrielli, P. (2007) Rayleigh wave dispersion curve inversion via genetic algorithms and marginal posterior probability density estimation. Journal of Applied Geophysics, 61(1), 39–55.
    [Google Scholar]
  14. Farrugia, D., Paolucci, E., D'Amico, S. & Galea, P. (2016) Inversion of surface wave data for subsurface shear wave velocity profiles characterized by a thick buried low‐velocity layer. Geophysical Journal International, 206(2), 1221–1231.
    [Google Scholar]
  15. Feng, S., Sugiyama, T. & Yamanaka, H. (2005) Effectiveness of multimode surface wave inversion in shallow engineering site investigations. Exploration Geophysics, 36, 26–33.
    [Google Scholar]
  16. Forbriger, T., Groos, L. & Schäfer, M. (2014) Line‐source simulation for shallow‐seismic data. Part 1: Theoretical background. Geophysical Journal International, 198(3), 1387–1404.
    [Google Scholar]
  17. Foti, S., Lai, C.G., Rix, G.J. & Strobbia, C. (2014) Surface wave methods for near‐surface site characterization. Boca Raton, Florida: CRC press.
    [Google Scholar]
  18. Foti, S., Hollender, F., Garofalo, F., Albarello, D., Asten, M., Bard, P.Y. et al. (2018) Guidelines for the good practice of surface wave analysis: a product of the InterPACIFIC project. Bulletin of Earthquake Engineering, 16(6), 2367–2420.
    [Google Scholar]
  19. Garofalo, F., Foti, S., Hollender, F., Bard, P.Y., Cornou, C., Cox, B. et al. (2016a) InterPACIFIC project: comparison of invasive and non‐invasive methods for seismic site characterization. Part I: Intra‐comparison of surface wave methods. Soil Dynamics and Earthquake Engineering, 82, 222–240.
    [Google Scholar]
  20. Garofalo, F., Foti, S., Hollender, F., Bard, P.Y., Cornou, C., Cox, B. et al. (2016b) InterPACIFIC project: comparison of invasive and non‐invasive methods for seismic site characterization. Part II: Inter‐comparison between surface‐wave and borehole methods. Soil Dynamics and Earthquake Engineering, 82, 241–254.
    [Google Scholar]
  21. Gebraad, L., Boehm, C. & Fichtner, A. (2020) Bayesian elastic full‐waveform inversion using Hamiltonian Monte Carlo. Journal of Geophysical Research: Solid Earth, 125(3), e2019JB018428.
    [Google Scholar]
  22. Goldberg, D.E. (1989) Genetic algorithms in search, optimization and machine learning. Amsterdam: Kluwer Academic Publishers.
    [Google Scholar]
  23. Groos, L., Schäfer, M., Forbriger, T. & Bohlen, T. (2014) The role of attenuation in 2D full‐waveform inversion of shallow‐seismic body and Rayleigh waves. Geophysics, 79(6), R247–R261.
    [Google Scholar]
  24. Groos, L., Schäfer, M., Forbriger, T. & Bohlen, T. (2017) Application of a complete workflow for 2D elastic full‐waveform inversion to recorded shallow‐seismic Rayleigh waves. Geophysics, 82(2), R109–R117.
    [Google Scholar]
  25. Holland, J.H. (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. Michigan: The University of Michigan Press.
    [Google Scholar]
  26. Kanlı, A.I., Tildy, P., Prónay, Z., Pınar, A. & Hermann, L. (2006) VS 30 mapping and soil classification for seismic site effect evaluation in Dinar region, SW Turkey. Geophysical Journal International, 165(1), 223–235.
    [Google Scholar]
  27. Luo, Y., Xia, J., Miller, R.D., Xu, Y., Liu, J. & Liu, Q. (2009) Rayleigh‐wave mode separation by high‐resolution linear Radon transform. Geophysical Journal International, 179(1), 254–264.
    [Google Scholar]
  28. Mallick, S. (1995) Model‐based inversion of amplitude‐variations‐with‐offset data using a genetic algorithm. Geophysics, 60(4), 939–954.
    [Google Scholar]
  29. Mallick, S. (1999) Some practical aspects of prestack waveform inversion using a genetic algorithm: an example from the east Texas Woodbine gas sand. Geophysics, 64(2), 326–336.
    [Google Scholar]
  30. Maraschini, M. & Foti, S. (2010) A Monte Carlo multimodal inversion of surface waves. Geophysical Journal International, 182(3), 1557–1566.
    [Google Scholar]
  31. Masoni, I., Boelle, J.L., Brossier, R. & Virieux, J. (2016) Layer stripping FWI for surface waves. In: 2016 SEG International Exposition and Annual Meeting, pp. 1369–1373.
  32. Masoni, I., Brossier, R., Boelle, J.L. & Virieux, J. (2014) Robust full waveform inversion of surface waves. In: 2014 SEG Annual Meeting, pp. 5005–5009.
  33. Nagai, K., O'Neill, A., Sanada, Y. & Ashida, Y. (2005) Genetic algorithm inversion of Rayleigh wave dispersion from CMPCC gathers over a shallow fault model. Journal of Environmental & Engineering Geophysics, 10(3), 275–286.
    [Google Scholar]
  34. Pan, Y., Gao, L. & Bohlen, T. (2019) High‐resolution characterization of near‐surface structures by surface‐wave inversions: from dispersion curve to full waveform. Surveys in Geophysics, 40(2), 167–195.
    [Google Scholar]
  35. Park, C.B., Miller, R.D. & Xia, J. (1999) Multichannel analysis of surface waves. Geophysics, 64(3), 800–808.
    [Google Scholar]
  36. Picozzi, M. & Albarello, D. (2007) Combining genetic and linearized algorithms for a two‐step joint inversion of Rayleigh wave dispersion and H/V spectral ratio curves. Geophysical Journal International, 169(1), 189–200.
    [Google Scholar]
  37. Pierini, S. & Stucchi, E. (2020) Points per wavelength analysis in global elastic FWI of surface waves: a synthetic case study. In: NSG2020 26th European Meeting of Environmental and Engineering Geophysics, 2020(1), pp. 1–4.
  38. Pierini, S., Aleardi, M. & Mazzotti, A. (2019) A method to attenuate genetic drift in genetic‐algorithm optimizations: applications to analytic objective functions and two seismic optimization problems. Geophysics, 84(2), R295–R310.
    [Google Scholar]
  39. Richart, F., Hall, J. & Woods, R. (1970) Vibrations of soils and foundations. Upper Saddle River. Prentice‐Hall, Inc.
    [Google Scholar]
  40. Sambridge, M. (1999) Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space. Geophysical journal international, 138(2), 479–494.
    [Google Scholar]
  41. Sajeva, A., Aleardi, M., Stucchi, E., Bienati, N. & Mazzotti, A. (2016) Estimation of acoustic macro models using a genetic full‐waveform inversion: applications to the Marmousi model. Geophysics, 81(4), R173–R184.
    [Google Scholar]
  42. Sajeva, A., Aleardi, M. & Mazzotti, A. (2017) Genetic algorithm full‐waveform inversion: uncertainty estimation and validation of the results. Bulletin of Geophysics and Oceanography, 58(4), 395–414.
    [Google Scholar]
  43. Sajeva, A., Aleardi, M., Galuzzi, B., Stucchi, E., Spadavecchia, E. & Mazzotti, A. (2017) Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full‐waveform inversion, and residual static computation. Geophysical Prospecting, 65(S1), 322–346.
    [Google Scholar]
  44. Schäfer, M., Groos, L., Forbriger, T. & Bohlen, T. (2013) 2D full waveform inversion of recorded shallow seismic Rayleigh waves on a significantly 2D structure. In: Near surface geoscience 2013–19th EAGE European Meeting of Environmental and Engineering Geophysics, pp. cp‐354.
  45. Sen, M.K. & Stoffa, P.L. (1992) Rapid sampling of model space using genetic algorithms: examples from seismic waveform inversion. Geophysical Journal International, 108(1), 281–292.
    [Google Scholar]
  46. Sen, M.K. & Stoffa, P.L. (2013) Global optimization methods in geophysical inversion. Cambridge: Cambridge University Press.
    [Google Scholar]
  47. Socco, L.V. & Strobbia, C. (2004) Surface‐wave method for near‐surface characterization: a tutorial. Near surface geophysics, 2(4), 165–185.
    [Google Scholar]
  48. Socco, L.V. & Boiero, D. (2008) Improved Monte Carlo inversion of surface wave data. Geophysical Prospecting, 56(3), 357–371.
    [Google Scholar]
  49. Socco, L.V., Foti, S. & Boiero, D. (2010) Surface‐wave analysis for building near‐surface velocity models—established approaches and new perspectives. Geophysics, 75(5), 75A83–75A102.
    [Google Scholar]
  50. Strobbia, C. & Foti, S. (2006) Multi‐offset phase analysis of surface wave data (MOPA). Journal of Applied Geophysics, 59(4), 300–313.
    [Google Scholar]
  51. Tarantola, A. (1984) Linearized inversion of seismic reflection data. Geophysical prospecting, 32(6), 998–1015.
    [Google Scholar]
  52. Thorbecke, J.W. & Draganov, D. (2011) Finite‐difference modeling experiments for seismic interferometry. Geophysics, 76(6), H1–H18.
    [Google Scholar]
  53. Tognarelli, A., Stucchi, E. & Mazzotti, A. (2020) Velocity model estimation by means of full waveform inversion of transmitted waves: an example from a seismic profile in the geothermal areas of Southern Tuscany, Italy. Geothermics, 88, 101894.
    [Google Scholar]
  54. Tran, K.T., McVay, M., Faraone, M. & Horhota, D. (2013) Sinkhole detection using 2D full seismic waveform tomography. Geophysics, 78(5), R175–R183.
    [Google Scholar]
  55. Vignoli, G., Gervasio, I., Brancatelli, G., Boaga, J., Della Vedova, B. & Cassiani, G. (2016) Frequency‐dependent multi‐offset phase analysis of surface waves: an example of high‐resolution characterization of a riparian aquifer. Geophysical Prospecting, 64(1), 102–111.
    [Google Scholar]
  56. Vignoli, G., Guillemoteau, J., Barreto, J. & Rossi, M. (2021) Reconstruction, with tunable sparsity levels, of shear wave velocity profiles from surface wave data. Geophysical Journal International, 225(3), 1935–1951.
    [Google Scholar]
  57. Wald, L.A. & Mori, J. (2000) Evaluation of methods for estimating linear site‐response amplifications in the Los Angeles region. Bulletin of the Seismological Society of America, 90(6B), S32–S42.
    [Google Scholar]
  58. Wathelet, M., Jongmans, D. & Ohrnberger, M. (2004) Surface wave inversion using a direct search algorithm and its application to ambient vibration measurements. Near Surface Geophysics, 2, 211–221.
    [Google Scholar]
  59. Wathelet, M., Chatelain, J.L., Cornou, C., Di Giulio, G., Guillier, B., Ohrnberger, M. et al. (2020) Geopsy: a user‐friendly open‐source tool set for ambient vibration processing. Seismological Research Letters, 91(3), 1878–1889.
    [Google Scholar]
  60. Xia, J., Miller, R. & Park, C. (1999) Estimation of near‐surface shear‐wave velocity by inversion of Rayleigh wave. Geophysics, 64(4), 691–700.
    [Google Scholar]
  61. Xia, J., Miller, R.D., Park, C.B. & Tian, G. (2003) Inversion of high frequency surface waves with fundamental and higher modes. Journal of Applied Geophysics, 52(1), 45–57.
    [Google Scholar]
  62. Xing, Z. & Mazzotti, A. (2019a) Two‐grid full‐waveform Rayleigh‐wave inversion via a genetic algorithm—Part 1: Method and synthetic examples. Geophysics, 84(5), R805–R814.
    [Google Scholar]
  63. Xing, Z. & Mazzotti, A. (2019b) Two‐grid full‐waveform Rayleigh‐wave inversion via a genetic algorithm—Part 2: Application to two actual data sets. Geophysics, 84(5), R815–R825.
    [Google Scholar]
  64. Zeng, Y., He, J. & Liu, Q. (2001) The application of the perfectly matched layer in numerical modeling of wave propagation in poroelastic media. Geophysics, 66(4), 1258–1266.
    [Google Scholar]
  65. Zhao, Z. & Sen, M.K. (2021) A gradient‐based Markov chain Monte Carlo method for full‐waveform inversion and uncertainty analysis. Geophysics, 86(1), R15–R30.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.12243
Loading
/content/journals/10.1002/nsg.12243
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): inversion; surface wave

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error