DeltaVs: A Method for Detecting Significant Layer Boundaries in Surface Wave Inversion Results
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 149, Issue 2
Abstract
Surface wave testing is a powerful tool for noninvasive seismic site characterization. It includes a wide variety of active-source and passive-wavefield methods [e.g., spectral analysis of surface waves (SASW), multichannel analysis of surface waves (MASW), microtremor array measurements (MAM)] that can be applied to many different field conditions. The dispersion data collected with all these methods can be inverted to produce (one-dimensional) 1D shear wave velocity () profiles of the subsurface. A critical part of this inversion procedure is the need to develop several trial model parameterizations with different numbers of layers as a means for investigating epistemic uncertainty and nonuniqueness when attempting to fit the experimental dispersion data. This is especially important when a priori information either is not available or does not extend to great enough depths to constrain the inversions. These trial parameterizations are used to vary the number of potential layers present in the subsurface models and to set the acceptable search ranges for depths to boundaries and stiffness in each layer. The inversion results are highly affected by the parameterizations. When performing high-quality inversions, it is important to use different parameterization options to fully explore potential conditions at the site and characterize the epistemic uncertainty of the inversion process. While this practice allows for a robust analysis of the experimental dispersion data and its uncertainty, it also generates many potential subsurface models for the site, which can represent a challenge for engineers when deciding which profiles best represent the true subsurface layering for use in subsequent analysis and design (e.g., seismic site response). This paper presents an approach, called the DeltaVs method, for systematically evaluating the depths of layer boundaries across all acceptable models determined from several different inversion layering parameterizations as a means to identify distinct clusters of boundaries. Using these clusters, the number and location of meaningful layer boundaries can be determined for many sites, thereby allowing for the elimination of some parameterization options containing more, or fewer, boundaries and a reduction in epistemic uncertainty. The distributions of layer boundaries within these clusters can then be used to determine statistics for the depths of layer boundaries that are consistent with the epistemic uncertainty of the models produced by the inversion procedure. The DeltaVs method is demonstrated on 12 synthetic ground models as well as one field data set.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The surface wave inversion benchmarks developed by Vantassel and Cox (2020) are publicly available through the DesignSafe-CI. The subsurface models developed during this study are available upon reasonable request from the corresponding author.
Acknowledgments
This work was supported by the US National Science Foundation (NSF) Graduate Research Fellowship under Grant Nos. DGE-2137420, CMMI-2037900, and CMMI-1931162. However, any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF. Special thanks to Dr. Kevin Anderson at the Austin Water Center for Environmental Research for access to the Hornsby Bend Biosolids Management Plant test site.
References
Andrus, R. D., N. P. Mohanan, P. Piratheepan, B. S. Ellis, and T. L. Holzer. 2007. “Predicting shear-wave velocity from cone penetration resistance.” In Vol. 2528 of Proc., 4th Int. Conf. on Earthquake Geotechnical Engineering. London: International Society for Soil Mechanics and Geotechnical Engineering.
Baise, L. G., J. Kaklamanos, B. M. Berry, and E. M. Thompson. 2016. “Soil amplification with a strong impedance contrast: Boston, Massachusetts.” Eng. Geol. 202 (Mar): 1–13. https://doi.org/10.1016/j.enggeo.2015.12.016.
Cox, B. R., and D. P. Teague. 2016. “Layering ratios: A systematic approach to the inversion of surface wave data in the absence of a priori information.” Geophys. J. Int. 207 (1): 422–438. https://doi.org/10.1093/gji/ggw282.
Cox, B. R., and C. M. Wood. 2011. “Surface wave benchmarking exercise: Methodologies, results, and uncertainties.” In Geo-Risk 2011: Risk Assessment and Management, 845–852. Reston VA: ASCE.
Cox, B. R., C. M. Wood, and D. P. Teague. 2014. “Synthesis of the UTexas1 surface wave dataset blind-analysis study: Inter-analyst dispersion and shear wave velocity uncertainty.” In Geo-Congress 2014: Geo-Characterization and Modeling for Sustainability, 850–859. Reston VA: ASCE.
Crocker, J. A., J. P. Vantassel, U. Arslan, and B. R. Cox. 2021. “Limitations of the multichannel analysis of surface waves (MASW) method for subsurface anomaly detection.” In Proc., 6th Int. Conf. on Geotechnical and Geophysical Site Characterization. London: International Society for Soil Mechanics and Geotechnical Engineering.
Dal Moro, G., S. S. Moustafa, and N. S. Al-Arifi. 2018. “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 Appl. Geophys. 175 (1): 67–88. https://doi.org/10.1007/s00024-017-1694-8.
Di Giulio, G., A. Savvaidis, M. Ohrnberger, M. Wathelet, C. Cornou, B. Knapmeyer-Endrun, F. Renalier, N. Theodoulidis, and P. Y. Bard. 2012. “Exploring the model space and ranking a best class of models in surface-wave dispersion inversion: Application at European strong-motion sites.” Geophysics 77 (3): B147–B166. https://doi.org/10.1190/geo2011-0116.1.
Fairchild, G. M., J. W. Lane, E. B. Voytek, and D. R. LeBlanc. 2013. “Bedrock topography of western Cape Cod, Massachusetts, based on bedrock altitudes from geologic borings and analysis of ambient seismic noise by the horizontal-to-vertical spectral-ratio method.” US Geol. Surv. Sci. Invest. Map 3233 (1): 17.
Foti, S., C. Comina, D. Boiero, and L. V. Socco. 2009. “Non-uniqueness in surface-wave inversion and consequences on seismic site response analyses.” Soil Dyn. Earthquake Eng. 29 (6): 982–993. https://doi.org/10.1016/j.soildyn.2008.11.004.
Foti, S., C. G. Lai, G. J. Rix, and C. Strobbia. 2015. Surface wave methods for near-surface site characterization. Boca Raton, FL: CRC Press.
Garofalo, F., 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 Dyn. Earthquake Eng. 82 (Mar): 222–240. https://doi.org/10.1016/j.soildyn.2015.12.010.
Garofalo, F., 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 Dyn. Earthquake Eng. 82 (Mar): 241–254. https://doi.org/10.1016/j.soildyn.2015.12.009.
Griffiths, S. C., B. R. Cox, E. M. Rathje, and D. P. Teague. 2016. “Surface-wave dispersion approach for evaluating statistical models that account for shear-wave velocity uncertainty.” J. Geotech. Geoenviron. Eng. 142 (11): 04016061. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001552.
Haskell, N. A. 1953. “The dispersion of surface waves on multilayered media.” Bull. Seismol. Soc. Am. 43 (1): 17–34. https://doi.org/10.1785/BSSA0430010017.
Hollender, F., et al. 2018. “Characterization of site conditions (soil class, V S30, velocity profiles) for 33 stations from the French permanent accelerometric network (RAP) using surface-wave methods.” Bull. Earthquake Eng. 16 (6): 2337–2365. https://doi.org/10.1007/s10518-017-0135-5.
Kaklamanos, J., and B. A. Bradley. 2018. “Challenges in predicting seismic site response with 1D analyses: Conclusions from 114 KiK-net vertical seismometer arrays challenges in predicting seismic site response with 1D analyses.” Bull. Seismol. Soc. Am. 108 (5A): 2816–2838. https://doi.org/10.1785/0120180062.
Kayen, R., R. E. S. Moss, E. M. Thompson, R. B. Seed, K. O. Cetin, A. Der Kiureghian, Y. Tanaka, and K. Tokimatsu. 2013. “Shear-wave velocity–based probabilistic and deterministic assessment of seismic soil liquefaction potential.” J. Geotech. Geoenviron. Eng. 139 (3): 407. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000743.
Lai, C. G., S. Foti, and G. J. Rix. 2005. “Propagation of data uncertainty in surface wave inversion.” J. Environ. Eng. Geophys. 10 (2): 219–228. https://doi.org/10.2113/JEEG10.2.219.
Mayne, P. W. 2007. “In-situ test calibrations for evaluating soil parameters.” In Vol. 3 of Characterization & engineering properties of natural soils, 1601–1652. London: Taylor & Francis.
Nocedal, J., and S. Wright. 2006. Numerical optimization. New York: Springer.
Park, C. B., R. D. Miller, and J. Xia. 1999. “Multichannel analysis of surface waves.” Geophysics 64 (3): 800–808. https://doi.org/10.1190/1.1444590.
Pei, D., J. N. Louie, and S. K. Pullammanappallil. 2007. “Application of simulated annealing inversion on high-frequency fundamental-mode Rayleigh wave dispersion curves.” Geophysics 72 (5): R77–R85. https://doi.org/10.1190/1.2752529.
Pratt, T. L., and L. S. Schleicher. 2021. “Characterizing ground-motion amplification by extensive flat-lying sediments: The seismic response of the eastern US Atlantic Coastal Plain strata.” Bull. Seismol. Soc. Am. 111 (4): 1795–1823. https://doi.org/10.1785/0120200328.
Ramírez-Guzmán, L., O. S. Boyd, S. Hartzell, and R. A. Williams. 2012. “Seismic velocity model of the central United States (version 1): Description and simulation of the 18 April 2008 Mt. Carmel, Illinois, earthquake.” Bull. Seismol. Soc. Am. 102 (6): 2622–2645. https://doi.org/10.1785/0120110303.
Robertson, P. K. 2009. “Interpretation of cone penetration tests—A unified approach.” Can. Geotech. J. 46 (11): 1337–1355. https://doi.org/10.1139/T09-065.
Sambridge, M. 1999a. “Geophysical inversion with a neighbourhood algorithm—I. Searching a parameter space.” Geophys. J. Int. 138 (2): 479–494. https://doi.org/10.1046/j.1365-246X.1999.00876.x.
Sambridge, M. 1999b. “Geophysical inversion with a neighbourhood algorithm—II. Appraising the ensemble.” Geophys. J. Int. 138 (3): 727–746. https://doi.org/10.1046/j.1365-246x.1999.00900.x.
Shaw, J. H., et al. 2015. “Unified structural representation of the southern California crust and upper mantle.” Earth Planet. Sci. Lett. 415 (Apr): 1–15. https://doi.org/10.1016/j.epsl.2015.01.016.
Shible, H., A. Laurendeau, P. Y. Bard, and F. Hollender. 2018. “Importance of local scattering in high frequency motion: Lessons from interpacific project sites, application to the KiK-net database and derivation of new hard-rock GMPE.” In Proc., 16th European Conf. on Earthquake Engineering, 18–21. Istanbul, Turkey: European Association for Earthquake Engineering.
Socco, L. V., and D. Boiero. 2008. “Improved Monte Carlo inversion of surface wave data.” Geophys. Prospect. 56 (3): 357–371. https://doi.org/10.1111/j.1365-2478.2007.00678.x.
Socco, L. V., S. Foti, and D. Boiero. 2010. “Surface-wave analysis for building near-surface velocity models—Established approaches and new perspectives.” Geophysics 75 (5): 75A83–75A102. https://doi.org/10.1190/1.3479491.
Stokoe, K. H., B. R. Cox, P. Clayton, and F. Menq. 2020. “NHERI@UTexas experimental facility with large-scale mobile shakers for field studies.” Front. Built Environ. 6 (Nov): 575973. https://doi.org/10.3389/fbuil.2020.575973.
Teague, D., B. Cox, B. Bradley, and L. Wotherspoon. 2018. “Development of deep shear wave velocity profiles with estimates of uncertainty in the complex interbedded geology of Christchurch, New Zealand.” Earthquake Spectra 34 (2): 639–672. https://doi.org/10.1193/041117EQS069M.
Teague, D. P., and B. R. Cox. 2016. “Site response implications associated with using non-unique Vs profiles from surface wave inversion in comparison with other commonly used methods of accounting for Vs uncertainty.” Soil Dyn. Earthquake Eng. 91 (Dec): 87–103. https://doi.org/10.1016/j.soildyn.2016.07.028.
Thabet, M. 2019. “Site-specific relationships between bedrock depth and HVSR fundamental resonance frequency using KiK-NET data from Japan.” Pure Appl. Geophys. 176 (11): 4809–4831. https://doi.org/10.1007/s00024-019-02256-7.
Thomson, W. T. 1950. “Transmission of elastic waves through a stratified solid medium.” J. Appl. Phys. 21 (2): 89–93. https://doi.org/10.1063/1.1699629.
Vantassel, J. P., and B. R. Cox. 2020. Surface wave inversion benchmarks. Miami: DesignSafe-CI. https://doi.org/10.17603/ds2-cpmr-v194.
Vantassel, J. P., and B. R. Cox. 2021a. “A procedure for developing uncertainty-consistent Vs profiles from inversion of surface wave dispersion data.” Soil Dyn. Earthquake Eng. 145 (Jun): 106622. https://doi.org/10.1016/j.soildyn.2021.106622.
Vantassel, J. P., and B. R. Cox. 2021b. “SWinvert: A workflow for performing rigorous 1-D surface wave inversions.” Geophys. J. Int. 224 (2): 1141–1156. https://doi.org/10.1093/gji/ggaa426.
Vantassel, J. P., and B. R. Cox. 2022. “SWprocess: A workflow for developing robust estimates of surface wave dispersion uncertainty.” J. Seismol. 26: 731–756. https://doi.org/10.1007/s10950-021-10035-y.
Wair, B. R., J. T. DeJong, and T. Shantz. 2012. Guidelines for estimation of shear wave velocity profiles. Berkeley, CA: Pacific Earthquake Engineering Research Center.
Wathelet, M. 2008. “An improved neighborhood algorithm: Parameter conditions and dynamic scaling.” Geophys. Res. Lett. 35 (9). https://doi.org/10.1029/2008GL033256.
Wathelet, M., J. L. Chatelain, C. Cornou, G. D. Giulio, B. Guillier, M. Ohrnberger, and A. Savvaidis. 2020. “Geopsy: A user-friendly open-source tool set for ambient vibration processing.” Seismol. Res. Lett. 91 (3): 1878–1889. https://doi.org/10.1785/0220190360.
Wathelet, M., D. Jongmans, and M. Ohrnberger. 2004. “Surface-wave inversion using a direct search algorithm and its application to ambient vibration measurements.” Near Surf. Geophys. 2 (4): 211–221. https://doi.org/10.3997/1873-0604.2004018.
Wathelet, M., D. Jongmans, and M. Ohrnberger. 2005. “Direct inversion of spatial autocorrelation curves with the neighborhood algorithm.” Bull. Seismol. Soc. Am. 95 (5): 1787–1800. https://doi.org/10.1785/0120040220.
Yamanaka, H., and H. Ishida. 1996. “Application of genetic algorithms to an inversion of surface-wave dispersion data.” Bull. Seismol. Soc. Am. 86 (2): 436–444. https://doi.org/10.1785/BSSA0860020436.
Yong, A., A. Martin, K. Stokoe, and J. Diehl. 2013. ARRA-funded VS30 measurements using multi-technique approach at strong-motion stations in California and central-eastern United States.. Washington, DC: USGS.
Yust, M. B., B. R. Cox, and T. Cheng. 2018. “Epistemic uncertainty in Vs profiles and Vs30 values derived from joint consideration of surface wave and H/V data at the FW07 TexNet station.” In Geotechnical earthquake engineering and soil dynamics V: Seismic hazard analysis, earthquake ground motions, and regional-scale assessment, 387–399. Reston, VA: ASCE.
Yust, M. B. S. 2018. “Dynamic site characterization of TexNet ground motion stations.” Master’s thesis, Dept. of Civil, Architectural, and Environmental Engineering, Univ. of Texas at Austin.
Zywicki, D. J. 1999. Advanced signal processing methods applied to engineering analysis of seismic surface waves. Atlanta: Georgia Institute of Technology.
Information & Authors
Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 18, 2022
Accepted: Sep 9, 2022
Published online: Nov 22, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 22, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Michael B. S. Yust, Brady R. Cox, Joseph P. Vantassel, Peter G. Hubbard, Christian Boehm, Lion Krischer, Near-Surface 2D Imaging via FWI of DAS Data: An Examination on the Impacts of FWI Starting Model, Geosciences, 10.3390/geosciences13030063, 13, 3, (63), (2023).