4.6 Article

Horizontal-to-Vertical Spectral Ratio of Ambient Vibration Obtained with Hilbert-Huang Transform

Journal

SENSORS
Volume 21, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/s21093292

Keywords

HVSR; non-stationary; data processing

Funding

  1. TOTAL
  2. Spanish government through the 'Severo Ochoa Centre of Excellence' accreditation [CEX2019-000928-S]
  3. Junta de Andalucia (Spain) [RNM-194]
  4. Spanish Ministry of Economy and Competitiveness [CGL2014-59908-JIN]
  5. European Regional Development Fund

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HVSR is a common tool for exploring near surface shear wave velocity structure, commonly used in earthquake risk assessments and civil engineering projects. Comparing FFT and HHT-based HVSR estimates, it was found that HHT may lead to better data fit, more suitable Vs model, and increased confidence in the achieved model.
The Horizontal-to-Vertical Spectral Ratio (HVSR) of ambient vibration measurements is a common tool to explore near surface shear wave velocity (Vs) structure. HVSR is often applied for earthquake risk assessments and civil engineering projects. Ambient vibration signal originates from the combination of a multitude of natural and man-made sources. Ambient vibration sources can be any ground motion inducing phenomena, e.g., ocean waves, wind, industrial activity or road traffic, where each source does not need to be strictly stationary even during short times. Typically, the Fast Fourier Transform (FFT) is applied to obtain spectral information from the measured time series in order to estimate the HVSR, even though possible non-stationarity may bias the spectra and HVSR estimates. This problem can be alleviated by employing the Hilbert-Huang Transform (HHT) instead of FFT. Comparing 1D inversion results for FFT and HHT-based HVSR estimates from data measured at a well studied, urban, permanent station, we find that HHT-based inversion models may yield a lower data misfit chi(2) by up to a factor of 25, a more appropriate Vs model according to available well-log lithology, and higher confidence in the achieved model.

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