ALBERT

Description: SUMMARY The horizontal-to-vertical spectral ratio (HVSR) of ambient noise is commonly used to infer a site's resonance frequency (${f_{0,site}}$). HVSR calculations are performed most commonly using the Fourier amplitude spectrum obtained from a single merged horizontal component (e.g. the geometric mean component) from a three-component sensor. However, the use of a single merged horizontal component implicitly relies on the assumptions of azimuthally isotropic seismic noise and 1-D surface and subsurface conditions. These assumptions may not be justified at many sites, leading to azimuthal variability in HVSR measurements that cannot be accounted for using a single merged component. This paper proposes a new statistical method to account for azimuthal variability in the peak frequency of HVSR curves (${f_{0,HVSR}}$). The method uses rotated horizontal components at evenly distributed azimuthal intervals to investigate and quantify azimuthal variability. To ensure unbiased statistics for ${f_{0,HVSR}}$ are obtained, a frequency-domain window-rejection algorithm is applied at each azimuth to automatically remove contaminated time windows in which the ${f_{0,HVSR}}$ values are statistical outliers relative to those obtained from the majority of windows at that azimuth. Then, a weighting scheme is used to account for different numbers of accepted time windows at each azimuth. The new method is applied to a data set of 114 HVSR measurements with significant azimuthal variability in ${f_{0,HVSR}}$, and is shown to reliably account for this variability. The methodology is also extended to the estimation of a complete lognormal-median HVSR curve that accounts for azimuthal variability. To encourage the adoption of this statistical approach to accounting for azimuthal variability in single-station HVSR measurements, the methods presented in this paper have been incorporated into hvsrpy, an open-source Python package for HVSR processing.