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Seismic site classification from HVSR data using the Rayleigh wave ellipticity inversion: A case study in Singapore

  • Abdialim, Shynggys (Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University) ;
  • Hakimov, Farkhod (RWTH Aachen University, Neotectonics and Natural Hazards) ;
  • Kim, Jong (Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University) ;
  • Ku, Taeseo (Department of Civil and Environmental Engineering, National University of Singapore) ;
  • Moon, Sung-Woo (Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University)
  • Received : 2021.04.06
  • Accepted : 2021.06.10
  • Published : 2021.09.25
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Abstract

Shear wave velocity (Vs) profile is one of the critical geotechnical measurements of soil layers for seismic hazard assessment and liquefaction potential evaluation. Enhancing the effectiveness of in-situ Vs profiling by reducing time and cost is of great interest. For that reason, this study aims at assessing Vs profile generation from a single-station three-component geophone with additional borehole log data for constraining parameter space. Based on multichannel analysis of surface waves (MASW), and microtremor array Measurements (MAM) conducted previously at seven sites located in Bukit Timah Granite, Singapore, this study utilized HVSR signals for Rayleigh wave ellipticity (ellipticity curve) inversion with additional inversion constraint using borehole log data. The resulting Vs profiles and reference Vs profiles from MASW and MAM were quantitatively compared using average Vs of 30 m (Vs30). The profiles generated from ellipticity curve inversion revealed a good agreement with Vs reference profiles. Vs30 based site classification results also indicated a good fit of two test results. Therefore, HVSR measurements for further ellipticity curve inversion, with already available borehole log data for constraint, is considered as a promising cost and time-effective site classification approach.

Keywords

Acknowledgement

The authors appreciate the financial support from the Nazarbayev University (Grant No. 110119FD4508).

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