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Effective Active and Passive Seismics for the Characterization of Urban and Remote Areas: Four Channels for Seven Objective Functions

  • Giancarlo Dal MoroEmail author
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Abstract

An efficient system for the joint acquisition and analysis of multi-component active and passive seismic data is presented. It is shown how, in spite of the limited field equipment (the system requires just a 4-channel seismograph, one 3-component and four vertical-component geophones), it is nevertheless possible to define up to seven different (but mutually related and complementary) objects used to constrain a multi-objective joint inversion capable of providing a robust subsurface shear-wave velocity (VS) profile for both geotechnical and seismic-hazard studies. The presented approach relies on acquisition techniques that require simple and straightforward field procedures useful in particular, but not solely, in the characterization of urban and remote areas where, due to logistical problems, standard acquisition procedures cannot be easily applied. Active data recorded by a single 3-component geophone are processed so to define up to five objective functions: the group-velocity spectra of the three components, the radial-to-vertical spectral ratio and the Rayleigh-wave particle motion frequency curve. Passive data are used to compute two further objects: the horizontal-to-vertical spectral ratio and the phase-velocity dispersion curve obtained via miniature array analysis of microtremors. These seven objects are jointly inverted by means of a multi-objective inversion procedure based on the Pareto criterion. Performances are assessed through a comprehensive field dataset acquired in an urban area of NW-Italy. The consistency of the overall procedure is assessed by comparing the results with the analyses accomplished by considering classical multi-channel active and passive data and methodologies (multi-component MASW, multichannel analysis of surface waves and ESAC, extended spatial auto-correlation).

Keywords

Surface wave dispersion Rayleigh waves Love waves joint inversion of seismic data holistic analysis of surface waves (HS) miniature array analysis of microtremors (MAAM) Rayleigh-wave particle motion (RPM) radial-to-vertical spectral ratio (RVSR) horizontal-to-vertical spectral ratio (HVSR) ESAC (extended spatial auto-correlation) MASW (multichannel analysis of surface waves) Vs30 

Notes

Acknowledgements

This work was partly supported by the Institute of Rock Structure and Mechanics (Czech Academy of Sciences—Prague, CZ) in the framework of the long-term conceptual development project RVO 67985891 (Institute grant for the “Extreme Seismics” project). The author is also grateful to Prof. Herrmann for his help in clarifying the sign convention adopted by his Computer Programs in Seismology. The paper significantly benefitted from the comments and suggestions of two anonymous reviewers whose comments were highly appreciated.

References

  1. Abbate, E., Fanucci, F., Benvenuti, M., Bruni, P., Cipriani, N., Falorni, P., Fazzuoli, M., Morelli, D., Pandeli, E., Papini, M., Sagri, M., Reale, V., & Vannucchi, P. (2004). Carta Geologica d’Italia—F° 248—La Spezia. Regione Liguria. http://www.isprambiente.gov.it/Media/carg/note_illustrative/248_LaSpezia.pdf. Accessed June 2018 (extended English abstract).
  2. American Society of Civil Engineers (ASCE). (2010). Minimum design loads for buildings and other structure, ASCE7-05, p 608. ISBN:0784410852.Google Scholar
  3. Arai, H. & Tokimatsu, K. (2000). Effects of Rayleigh and Love waves on icrotremor H/V spectra. In: Proceedings of 12th world conference of earthquake engineering, Auckland, New Zealand, Paper no. 2232.Google Scholar
  4. Arai, H., & Tokimatsu, K. (2004). S-wave velocity profiling by inversion of microtremor H/V spectrum. Bulletin of the Seismological Society of America, 94, 53–63.CrossRefGoogle Scholar
  5. Arai, H., & Tokimatsu, K. (2005). S-Wave velocity profiling by joint inversion of microtremor dispersion curve and horizontal-to-vertical (H/V) spectrum. Bulletin of the Seismological Society of America, 95, 1766–1778.CrossRefGoogle Scholar
  6. Asten, M. W. (2006). On bias and noise in passive seismic data from finite circular array data processed using SPAC methods. Geophysics, 71, 153–162.CrossRefGoogle Scholar
  7. Asten, M. W., Aysegul, A., Ezgi, E. E., Nurten, S. F., & Beliz, U. (2014). Site characterisation in north-western Turkey based on SPAC and HVSR analysis of microtremor noise. Exploration Geophysics, 45, 74–85.CrossRefGoogle Scholar
  8. Asten, M.W., Dhu, T., & Lam, N. (2004). Optimised array design for microtremor array studies applied to site classification; comparison of results with SCPT Logs. In Proceedings of the 13th world conference on earthquake engineering, Vancouver, paper 2903.Google Scholar
  9. Asten, M. W., & Hayashi, K. (2018). Application of the spatial auto-correlation method for shear-wave velocity studies using ambient noise. Surveys In Geophysics, 39, 633–659.CrossRefGoogle Scholar
  10. Bhattacharya, S. N. (1983). Higher order accuracy in multiple filter technique. Bulletin of the Seismological Society of America, 73, 1395–1406.Google Scholar
  11. Bonnefoy-Claudet, S., Köhler, A., Cornou, C., Wathelet, M., & Bard, P.-Y. (2008). Effects of Love waves on microtremor H/V ratio. Bulletin of the Seismological Society of America, 98, 288–300.CrossRefGoogle Scholar
  12. Bour, M., Fouissac, D., Dominique, P., & Martin, C. (1998). On the use of microtremor recordings in seismic microzonation. Soil Dynamics and Earthquake Engineering, 17, 465–474.CrossRefGoogle Scholar
  13. Brown, R. J., Stewart, R. R., & Lawton, D. C. (2002). A proposed polarity standard for multicomponent seismic data. Geophysics, 67, 1028–1037.CrossRefGoogle Scholar
  14. CEN (Comité Européen de Normalisation). (2004). EN 1998-5:2004b: Eurocode 8: Design of Structures for Earthquake Resistance—Part 1: General rules, seismic actions and rules for buildings. Brussels, Belgium: CEN.Google Scholar
  15. Cho, I., Senna, S., & Fujiwara, H. (2013). Miniature array analysis of microtremors. Geophysics, 78, KS13–KS23.CrossRefGoogle Scholar
  16. Cho, I., Tada, T., & Shinozaki, Y. (2006). Centerless circular array method: Inferring phase velocities of Rayleigh waves in broad wavelength ranges using microtremor records. Journal of Geophysical Research, 111, B09315.  https://doi.org/10.1029/2005JB004235.CrossRefGoogle Scholar
  17. Dal Moro, G. (2014). Surface wave analysis for near surface applications (p. 252). Oxford: Elsevier.Google Scholar
  18. Dal Moro, G. (2015). Joint inversion of rayleigh-wave dispersion and HVSR of lunar seismic data from the Apollo 14 and 16 sites. Icarus, 254, 338–349.CrossRefGoogle Scholar
  19. Dal Moro, G., Moura, R. M., & Moustafa, S. R. (2015a). Multi-component joint analysis of surface waves. Journal of Applied Geophysics, 119, 128–138.CrossRefGoogle Scholar
  20. Dal Moro, G., Moustafa, S.R., & Al-Arifi, N. (2015b). Efficient acquisition and holistic analysis of Rayleigh waves. In Proceedings of the near-surface EAGE 2015 (Turin—Italy).Google Scholar
  21. Dal Moro, G., Ponta, R., & Mauro, R. (2015c). Unconventional optimized surface wave acquisition and analysis: Comparative tests in a perilagoon area. Journal of Applied Geophysics, 114, 158–167.CrossRefGoogle Scholar
  22. Dal Moro, G., Keller, L., & Poggi, V. (2015d). A comprehensive seismic characterization via multi-component analysis of active and passive data. First Break, 33, 45–53.CrossRefGoogle Scholar
  23. Dal Moro, G., Keller, L., Moustafa, S. R., & Al-Arifi, N. (2016). Shear-wave velocity profiling according to three alternative approaches: A comparative case study. Journal of Applied Geophysics, 134, 112–124.CrossRefGoogle Scholar
  24. Dal Moro, G., Al-Arifi, N., & Moustafa, S. R. (2017a). Analysis of Rayleigh-wave particle motion from active seismics. Bulletin of the Seismological Society of America, 107, 51–62.CrossRefGoogle Scholar
  25. Dal Moro, G., Moustafa, S. R., & Al-Arifi, N. (2017b). Improved holistic analysis of rayleigh waves for single- and multi-offset data: Joint inversion of rayleigh-wave particle motion and vertical- and radial-component velocity spectra. Pure and Applied Geophysics, 175, 67–88.  https://doi.org/10.1007/s00024-017-1694-8.CrossRefGoogle Scholar
  26. Dal Moro, G., & Pipan, M. (2007). Joint inversion of surface wave dispersion curves and reflection travel times via multi-objective evolutionary algorithms. Journal of Applied Geophysics, 61, 56–81.CrossRefGoogle Scholar
  27. Dal Moro, G., & Puzzilli, L. M. (2017). Single- and multi-component inversion of Rayleigh waves acquired by a single 3-component geophone: An illustrative case study. Acta Geodynamics et Geomaterialia, 14, 431–444.  https://doi.org/10.13168/AGG.2017.0024.CrossRefGoogle Scholar
  28. Dimitriu, P., Kalogeras, I., & Theodulidis, N. (1999). Evidence of nonlinear site response in horizontal-to-vertical spectral ratio from near-field earthquakes. Soil Dynamics and Earthquake Engineering, 18, 423–435.CrossRefGoogle Scholar
  29. Dou, S., & Ajo-Franklin, J. B. (2014). Full-wavefield inversion of surface waves for mapping embedded low-velocity zones in permafrost. Geophysics, 79, EN107-EN124.CrossRefGoogle Scholar
  30. Dziewonski, A., Bloch, S., & Landisman, N. (1969). A technique for the analysis of transient seismic signals. Bulletin of the Seismological Society of America, 59, 427–444.Google Scholar
  31. Fasan, M., Magrin, A., Amadio, C., Romanelli, F., Vaccari, F., & Panza, G. F. (2016). A seismological and engineering perspective on the 2016 Central Italy earthquakes. International Journal of Earthquake and Impact Engineering, 1, 395–420.  https://doi.org/10.1504/IJEIE.2016.10004076.CrossRefGoogle Scholar
  32. Haghshenas, E., Bard, P. Y., Theodulidis, N., et al. (2008). Empirical evaluation of microtremor H/V spectral ratio. Bull Earthquake Eng, 6, 75–108.  https://doi.org/10.1007/s10518-007-9058-x.CrossRefGoogle Scholar
  33. Herrmann, R. B. (2013). Computer programs in seismology: An evolving tool for instruction and research. Seismological Research Letters, 84, 1081–1088.CrossRefGoogle Scholar
  34. Herrmann, R.B. (2018). Computer programs in seismology. Open files. http://www.eas.slu.edu/eqc/eqccps.html. Accessed Aug 2018.
  35. Ikeda, T., Asten, M.W., & Matsuoka, T. (2013). Joint inversion of spatial autocorrelation curves with HVSR for site characterization in Newcastle, Australia: Extended abstracts of the 23rd ASEG conference and exhibition. http://www.publish.csiro.au/ex/pdf/ASEG2013ab315. Accessed Aug 2018.
  36. International Building Code (IBC). (2009). International Building Code. ISBN:580017258.Google Scholar
  37. International Code Council (ICC). (2009). International Code Council. ISBN:978-1-58001-727-5.Google Scholar
  38. Kacoaglu, A. H., & Long, L. T. (1993). A review of time-frequency analysis techniques for estimation of group velocities. Seismological Research Letters, 64, 157–167.Google Scholar
  39. Lefebvre, G., & Karray, M. (2009). Techniques for mode separation in Rayleigh wave testing. Soil Dynamics and Earthquake Engineering, 29, 607–619.  https://doi.org/10.1016/j.soildyn.2008.07.005.CrossRefGoogle Scholar
  40. Louie, J. N. (2001). Faster, better: Shear-wave velocity to 100 meters depth from refraction microtremor arrays. Bulletin of the Seismological Society of America, 91, 347–364.CrossRefGoogle Scholar
  41. Lunedei, E., & Albarello, D. (2009). On the seismic noise wavefield in a weakly dissipative layered Earth. Geophysical Journal International, 177, 1001–1014.CrossRefGoogle Scholar
  42. Luzón, F., Al Yuncha, Z., Sánchez-Sesma, F. J., & Ortiz-Alemán, C. (2001). A numerical experiment in the horizontal to vertical spectral ratio in flat sedimentary basins. Pure and Applied Geophysics, 158, 2451–2461.CrossRefGoogle Scholar
  43. Macau, A., Benjumea, B., Gabas, A., Figueras, S., & Vila, M. (2015). The effect of shallow Quaternary deposits on the shape of the H/V spectral ratio. Surveys In Geophysics, 36, 185–208.CrossRefGoogle Scholar
  44. Mark, N., & Sutton, G. H. (1975). Lunar shear velocity structure at Apollo sites 12, 14, and 15. Journal of Geophysical Research, 80, 4932–4938.CrossRefGoogle Scholar
  45. Nakamura, Y. (1989). A method for dynamic characteristic estimation of subsurface using microtremor on the ground surface. QR Railway Technique Research Institute, 30(1), 25–33.Google Scholar
  46. Natale, M., Nunziata, C., & Panza, G.F. (2004). FTAN method for the detailed definition of V S in urban areas. In 13th World conference on earthquake engineering (p. 2694). Vancouver, B.C., Canada.Google Scholar
  47. Ohori, M., Nobata, A., & Wakamatsu, K. (2002). A comparison of ESAC and FK methods of estimating phase velocity using arbitrarily shaped microtremor analysis. Bulletin of the Seismological Society of America, 92, 2323–2332.CrossRefGoogle Scholar
  48. Park, C. B., Miller, R. D., & Xia, J. (1999). Multichannel analysis of surface waves. Geophysics, 64, 800–808.CrossRefGoogle Scholar
  49. 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, 189–200.CrossRefGoogle Scholar
  50. Prodehl, C., Kennett, B., Artemieva, I. M., & Thybo, H. (2013). 100 years of seismic research on the Moho. Tectonophysics, 609, 9–44.CrossRefGoogle Scholar
  51. Ryden, N., Park, C. B., Ulriksen, P., & Miller, R. D. (2004). Multimodal approach to seismic pavement testing. Journal of Geotechnical and Geoenvironmental Engineering, 130, 636–645.CrossRefGoogle Scholar
  52. Sawaragi, Y., Nakayama, H., & Tamino, T. (1985). Theory of multiobjective optimization (p. 296). Orlando, Florida: Academic.Google Scholar
  53. Scales, J.A., Smith, M.L., & Treitel, S. (2001). Introductory geophysical inverse theory (p. 193). Open file, Samizdat Press. http://www.e-booksdirectory.com/details.php?ebook=9154. Accessed 21 Nov 2018.
  54. Sleeman, R., van Wettum, A., & Trampert, J. (2006). Three-channel correlation analysis: A new technique to measure instrumental noise of digitizers and seismic sensors. Bulletin of the Seismological Society of America, 96(1), 258–271.CrossRefGoogle Scholar
  55. Tada, T., Cho, I., & Shinozaki, Y. (2007). Beyond the SPAC method: Exploiting the wealth of circular-array methods for microtremor exploration. Bulletin of the Seismological Society of America, 97, 2080–2095.CrossRefGoogle Scholar
  56. Tada, T., Cho, I., & Shinozaki, Y. (2009). New circular-array microtremor techniques to infer love-wave phase velocities. Bulletin of the Seismological Society of America, 99, 2912–2926.CrossRefGoogle Scholar
  57. Tokeshi, J. C., Karkee, M. B., & Sugimura, Y. (2006). Reliability of Rayleigh wave dispersion curve obtained from f–k spectral analysis of microtremor array measurement. Soil Dynamics and Earthquake Engineering, 26, 163–174.  https://doi.org/10.1016/j.soildyn.2005.02.013.CrossRefGoogle Scholar
  58. Tokimatsu, K., Tamura, S., & Kojima, H. (1992). Effects of multiple modes on rayleigh wave dispersion characteristics. Journal of Geotechnical Engineering, 118, 1529–1543.CrossRefGoogle Scholar
  59. Tong, V. C. H., & Garcia, R. A. (Eds.). (2015). Extraterrestrial seismology (p. 491). Cambridge: Cambridge University Press.  https://doi.org/10.1017/CBO9781107300668.CrossRefGoogle Scholar
  60. Zealand. (2004). NZS 1170.5:2004 Structural design actions part 5: Earthquake actions-New Zealand. New Zealand.Google Scholar
  61. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257–271.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of SeismotectonicsInstitute of Rock Structure and Mechanics (Czech Academy of Sciences)PragueCzech Republic

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