1887
Volume 49, Issue 6
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

[

We propose an empirical Bayesian approach to inferring shallow (depth ranges from a few to several tens of metres) S-wave velocity structures using microtremor arrays and execute numerical tests to assess the feasibility of this approach. In our approach, the estimate of the S-wave structure (posterior) is derived from an empirical S-wave structure model (prior) and phase velocities of Rayleigh waves obtained with microtremor arrays. In other words, we aim to find a model that is close to the empirical model and is able to explain phase velocities with a 1D surface-wave theory. The inversion is stabilised by the constraints from the prior model so that model parameterisation with many thin layers can be adopted. The velocity structure is individually estimated for each of two cases (assumptions): the case where we assume fundamental-mode dominance and the case where we take into account the higher modes. Optimal values of the model parameters (e.g. a thickness parameter) are found, based on Akaike’s Bayesian Information Criterion (ABIC), and the choice of the better assumption of the surface-wave theory is also based on ABIC. Numerical tests, where synthetic data is generated from a horizontally stratified two-layer model, indicate that the relative weight between a prior model and the observed data is appropriately adjusted by ABIC. It is revealed that a value of the thickness parameter required to reproduce the given two-layer model is successfully found by ABIC. We also suggest that we can make a plausible choice of the assumption of the surface-wave theory with ABIC, unless observation error is extremely large.

,

We have developed an empirical Bayesian approach to inferring shallow S-wave velocity structures. This approach has the potential to automatically determine the number of layers of a velocity structure model as well as to confirm the plausible assumption of a surface-wave theory.

]
Loading

Article metrics loading...

/content/journals/10.1071/EG18011
2018-11-01
2026-01-23
Loading full text...

Full text loading...

/deliver/fulltext/texg20/49/6/EG18011.html?itemId=/content/journals/10.1071/EG18011&mimeType=html&fmt=ahah

References

  1. Akaike, H., 1980, Likelihood and Bayes procedure, in J. M. Bernard, M. H. De Groot, D. U. Lindley, and A. F. M. Smith, eds., Bayesian statistics: University Press, Valencia, Spain, 143–203.
  2. Aki K. 1957 Space and time spectra of stationary stochastic waves, with special reference to microtremors:Bulletin of the Earthquake Research Institute, University of Tokyo35415457
    [Google Scholar]
  3. 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 America951766177810.1785/0120040243
    https://doi.org/10.1785/0120040243 [Google Scholar]
  4. Ballard, R. F., Jr, 1964, Determination of soil shear moduli at depths by in-situ vibratory techniques: Misc. Pap. No. 4–691, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Miss.
  5. Chimoto K. Yamanaka H. Tsuno S. Miyake H. Yamada N. 2016 Estimation of shallow S-wave velocity structure using microtremor array exploration at temporary strong motion observation stations for aftershocks of the 2016 Kumamoto earthquake:Earth, Planets, and Space6820610.1186/s40623‑016‑0581‑3
    https://doi.org/10.1186/s40623-016-0581-3 [Google Scholar]
  6. Cho I. Senna S. 2016 Constructing a system to explore shallow velocity structures using a miniature microtremor array: accumulating and utilizing large microtremor database:Synthesiology9869610.5571/synth.9.2_86
    https://doi.org/10.5571/synth.9.2_86 [Google Scholar]
  7. Cho I. Nakanishi I. Ling S. Okada H. 1999 Application of forking genetic algorithm fGA to an exploration method using microtremors:Butsuri Tansa52227246 [in Japanese with English abstract]
    [Google Scholar]
  8. Cho I. Tada T. Shinozaki Y. 2004 A new method to determine phase velocities of Rayleigh waves from microseisms:Geophysics691535155110.1190/1.1836827
    https://doi.org/10.1190/1.1836827 [Google Scholar]
  9. Cho I. Tsurugi M. Kagawa T. Iwata T. 2006 Modelling of deep sedimentary velocity structure for evaluation of broadband strong ground motions: site-amplification spectra in Osaka sedimentary basin:Journal of Japan Association for Earthquake Engineering6113132 [in Japanese with English abstract].10.5610/jaee.6.4_113
    https://doi.org/10.5610/jaee.6.4_113 [Google Scholar]
  10. Cho I. Senna S. Fujiwara H. 2013 Miniature array analysis of microtremors:Geophysics78KS13KS2310.1190/geo2012‑0248.1
    https://doi.org/10.1190/geo2012-0248.1 [Google Scholar]
  11. Cuéllar, V., 1994, Determination of the dynamic behaviour of soils using surface waves: Spanish experiences: Proceedings of the 10th World Conference on Earthquake Engineering, Balkema, Rotterdam, 6725–6734.
  12. Fukahata Y. 2009 Development of study of inversion analyses using ABIC in seismology:Zisin61S103S113 [in Japanese with English abstract].10.4294/zisin.61.103
    https://doi.org/10.4294/zisin.61.103 [Google Scholar]
  13. Fukahata Y. Wright T. J. 2008 A non-linear geodetic data inversion using ABIC for slip distribution on a fault with an unknown dip angle:Geophysical Journal International17335336410.1111/j.1365‑246X.2007.03713.x
    https://doi.org/10.1111/j.1365-246X.2007.03713.x [Google Scholar]
  14. Gazetas G. 1982 Vibrational characteristics of soil deposits with variable wave velocity:International Journal for Numerical and Analytical Methods in Geomechanics612010.1002/nag.1610060103
    https://doi.org/10.1002/nag.1610060103 [Google Scholar]
  15. Goto H. Mitsunaga H. Inatani M. Iiyama K. Hada K. Ikeda K. Takaya T. Kimura S. Akiyama R. Sawada S. Morikawa H. 2017 Shallow subsurface structure estimated from dense aftershock records and microtremor observation in Furukawa district, Miyagi, Japan:Exploration Geophysics48162710.1071/EG16113
    https://doi.org/10.1071/EG16113 [Google Scholar]
  16. Heukelom W. Foster C. R. 1960 Dynamic testing of pavements:Journal of Structural Division (ASCE)86128
    [Google Scholar]
  17. Hisada Y. 1994 An efficient method for computing Green’s functions for a layered half-space with sources and receivers at close depths:Bulletin of the Seismological Society of America8414561472
    [Google Scholar]
  18. Hisada Y. 1995 An efficient method for computing Green’s functions for a layered half-space with sources and receivers at close depths (Part 2):Bulletin of the Seismological Society of America8510801093
    [Google Scholar]
  19. Ikeda T. Matsuoka T. Tsuji T. Hayashi K. 2012 Multimode inversion with amplitude response of surface waves in the spatial autocorrelation method:Geophysical Journal International19054155210.1111/j.1365‑246X.2012.05496.x
    https://doi.org/10.1111/j.1365-246X.2012.05496.x [Google Scholar]
  20. Iwata T. 2013 Estimation of completeness magnitude considering daily variation in earthquake detection capability:Geophysical Journal International1941909191910.1093/gji/ggt208
    https://doi.org/10.1093/gji/ggt208 [Google Scholar]
  21. Iwata T. 2014 Decomposition of seasonality and long-term trend in seismological data: a Bayesian modelling of earthquake detection capability:Australian & New Zealand Journal of Statistics5620121510.1111/anzs.12079
    https://doi.org/10.1111/anzs.12079 [Google Scholar]
  22. Kass R. E. Raftery A. E. 1995 Bayes factors:Journal of the American Statistical Association9077379510.1080/01621459.1995.10476572
    https://doi.org/10.1080/01621459.1995.10476572 [Google Scholar]
  23. Koketsu K. Higashi S. 1992 Three-dimensional topography of the sediment/basement interface in the Tokyo metropolitan area, central Japan:Bulletin of the Seismological Society of America8223282349
    [Google Scholar]
  24. Ludwig, W. J., Nafe, J. E., and Drake, C. L., 1970, Seismic refraction, in A. E. Maxwell, ed., The sea: Wiley Interscience, 4, 53–84.
  25. MacKay D. J. C. 1995 Probable networks and plausible predictions: a review of practical Bayesian methods for supervised neural networks:Network: Computation in Neural Systems646950510.1088/0954‑898X_6_3_011
    https://doi.org/10.1088/0954-898X_6_3_011 [Google Scholar]
  26. Matsu’ura M. Noda A. Fukahata Y. 2007 Geodetic data inversion based on Bayesian formulation with direct and indirect prior information:Geophysical Journal International1711342135110.1111/j.1365‑246X.2007.03578.x
    https://doi.org/10.1111/j.1365-246X.2007.03578.x [Google Scholar]
  27. Ogata Y. Imoto M. Katsura K. 1991 3-D spatial variation of b-values of magnitude-frequency distribution beneath the Kanto District, Japan:Geophysical Journal International10413514610.1111/j.1365‑246X.1991.tb02499.x
    https://doi.org/10.1111/j.1365-246X.1991.tb02499.x [Google Scholar]
  28. Okada, H., 2003, The microtremor survey method: Society of Exploration Geophysicists, Geophysical Monograph Series 12.
  29. Pelekis P. C. Athanasopoulos G. A. 2011 An overview of surface wave methods and a reliability study of a simplified inversion technique:Soil Dynamics and Earthquake Engineering311654166810.1016/j.soildyn.2011.06.012
    https://doi.org/10.1016/j.soildyn.2011.06.012 [Google Scholar]
  30. Poggi V. Fäh D. Burjanek J. Giardini D. 2012 The use of Rayleigh-wave ellipticity for site-specific hazard assessment and microzonation: application to the city of Lucerne, Switzerland:Geophysical Journal International1881154117210.1111/j.1365‑246X.2011.05305.x
    https://doi.org/10.1111/j.1365-246X.2011.05305.x [Google Scholar]
  31. Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., 2007, Numerical recipes: the art of scientific computing (3rd edition): Cambridge University Press.
  32. Sekiguchi H. Irikura K. Iwata T. 2000 Fault geometry at the rupture termination of the 1995 Hyogo-ken Nanbu Earthquake:Bulletin of the Seismological Society of America9011713310.1785/0119990027
    https://doi.org/10.1785/0119990027 [Google Scholar]
  33. Society of Exploration Geophysicists of Japan Standardisation Committee, 2008, Applications manual of geophysical methods to engineering and environmental problems: Society of Exploration Geophysicists of Japan, 111–126 [in Japanese].
  34. 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 America972080209510.1785/0120070058
    https://doi.org/10.1785/0120070058 [Google Scholar]
  35. Tierney L. Kadane J. B. 1986 Accurate approximation for posterior moments and marginal densities:Journal of the American Statistical Association81828610.1080/01621459.1986.10478240
    https://doi.org/10.1080/01621459.1986.10478240 [Google Scholar]
  36. Tokimatsu K. Tamura S. Kojima H. 1992 Effects of multiple modes on Rayleigh wave dispersion characteristics:Journal of Geotechnical Engineering1181529154310.1061/(ASCE)0733‑9410(1992)118:10(1529)
    https://doi.org/10.1061/(ASCE)0733-9410(1992)118:10(1529) [Google Scholar]
  37. Wakai, A., Senna, S., Jin, K., Cho, I., Matsuyama, H., and Fujiwara, H., 2017, A method for setting engineering bedrock using records of miniature array microtremor observation in Kanto Area: JpGU-AGU Joint Meeting, 2017, SSS15-P19.
/content/journals/10.1071/EG18011
Loading
/content/journals/10.1071/EG18011
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): arrays; inversion; modelling; passive; shallow; surface wave; velocity

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