A Statistical Representation and Frequency-Domain Window-Rejection Algorithm to Account for Azimuthal Variability in Single-Station HVSR Measurements

Session: Data Fusion and Uncertainty Quantification in Near-Surface Site Characterization [Poster]
Type: Poster
Date: 4/30/2020
Time: 08:00 AM
Room: Ballroom
Description: 

The horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site’s resonance frequency (f₀). For sites with a strong impedance contrast, the HVSR peak frequency (f_0,HVSR) has been shown to be a good estimate of f₀. However, the random nature of ambient noise (both in time and space), in conjunction with variable subsurface conditions and sensor coupling issues, can lead to uncertainty in f_0,HVSR estimates, particularly at sites with complex subsurface conditions, where azimuthal variability in f_0,HVSR can be significant. Hence, it is important to report f_0,HVSR in a statistical manner (e.g., as a mean or median value with standard deviation). We discuss widely accepted procedures to process HVSR data and estimate the variance in f_0,HVSR. Then, we propose modifications to improve these procedures in three specific ways. First, we propose using a lognormal distribution to describe f_0,HVSR rather than the more commonly used normal distribution. The use of a lognormal distribution for f_0,HVSR has several advantages, including consistency with earthquake ground motion processing and allowing for a seamless transition between HVSR statistics in terms of both frequency and its reciprocal, period. Second, we introduce a new frequency-domain window-rejection algorithm to decrease variance and enhance data quality. Third, we propose a method to statistically combine f_0,HVSR estimates as a function of azimuth, such that a single, unbiased lognormal median f_0,HVSR and sigma value can be obtained. We use examples of high-variance and low-variance HVSR data collected around the world to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach.

Presenting Author: Joseph P. Vantassel

Authors