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.

Description: Summary SWinvert is a workflow developed at The University of Texas at Austin for the inversion of surface wave dispersion data. SWinvert encourages analysts to investigate inversion uncertainty and non-uniqueness in shear wave velocity (Vs) by providing a systematic procedure and specific actionable recommendations for surface wave inversion. In particular, the workflow encourages the use of multiple layering parameterizations to address the inversion's non-uniqueness, multiple global searches for each parameterization to address the inverse problem's non-linearity, and quantification of Vs uncertainty in the resulting profiles. While the workflow uses the Dinver module of the popular open-source Geopsy software as its inversion engine, the principles presented are of relevance to analysts using other inversion programs. To illustrate the effectiveness of the SWinvert workflow and to develop a set of benchmarks for use in future surface wave inversion studies, synthetic experimental dispersion data for 12 subsurface models of varying complexity are inverted. While the effects of inversion uncertainty and non-uniqueness are shown to be minimal for simple subsurface models characterized by broadband dispersion data, these effects cannot be ignored in the Vs profiles derived for more complex models with band-limited dispersion data. To encourage adoption of the SWinvert workflow, an open-source Python package (SWprepost), for pre- and post-processing of surface wave inversion data, and an application on the DesignSafe-Cyberinfrastructure (SWbatch), for performing batch-style surface wave inversions with Dinver using high-performance computing, have been developed and released in conjunction with this work. The SWinvert workflow is shown to provide a methodical procedure and a powerful set of tools for performing rigorous surface wave inversions and quantifying the uncertainty in the resulting Vs profiles.

Description: Seismic cone penetration testing (SCPT) is a powerful geotechnical site characterization tool, allowing for simultaneous collection of routine cone penetration testing data and rapid downhole-type measurements of shear-wave velocity (VS). However, the uncertainties associated with developing VS profiles from SCPT measurements are rarely considered or communicated to the end-user. One important source of VS uncertainty is related to how the shear wave travel times are interpreted from the recorded waveforms, while another critical source of uncertainty is related to the analysis method used to transform the travel times to velocities. In this study, four common ways of obtaining travel times were considered: (i) first arrival picks, (ii) peaks and troughs picks, (iii) crossover picks, and (iv) the peak response of the cross-correlation function. Using these different travel times, a number of VS profiles were developed using four different velocity analysis methods: (i) pseudo-interval, (ii) true-interval, (iii) corrected vertical travel time slope-based, and (iv) raytracing. Through consideration of multiple wave arrival time and velocity analysis methods, a robust and meaningful quantification of the intramethod, depth-dependent epistemic uncertainty in VS obtained from several example SCPT datasets has been developed. VS uncertainty is further examined through consideration of the intermethod variability and bias between SCPT and direct-push crosshole testing.

Description: The horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site's resonance frequency (${f_0}$). For sites with a strong impedance contrast, the HVSR peak frequency (${f_{0,mathrm{ HVSR}}}$) has been shown to be a good estimate of ${f_0}$. However, the random nature of ambient noise (both in time and space), in conjunction with variable environmental conditions and sensor coupling issues, can lead to uncertainty in ${f_{0,mathrm{ HVSR}}}$ estimates. Hence, it is important to report ${f_{0,mathrm{ HVSR}}}$ in a statistical manner (e.g. as a mean or median value with standard deviation). In this paper, we first discuss widely accepted procedures to process HVSR data and estimate the variance in ${f_{0,mathrm{ HVSR}}}$. Then, we propose modifications to improve these procedures in two specific ways. First, we propose using a lognormal distribution to describe ${f_{0,mathrm{ HVSR}}}$ rather than the more commonly used normal distribution. The use of a lognormal distribution for ${f_{0,mathrm{ HVSR}}}$ 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. Finally, we use examples of 114 high-variance HVSR measurements and 77 low-variance HVSR measurements collected at two case study sites to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach. To encourage their adoption, and promote standardization, the rejection algorithm and lognormal statistics presented in this paper have been incorporated into hvsrpy, an open-source Python package for HVSR processing.

Description: Common procedures used to account for spatial variability of shear wave velocity (Vs) in one-dimensional (1D) ground response analyses (GRAs), such as stochastic randomization of Vs or increasing small-strain damping, have been shown to improve seismic site response predictions relative to 1D GRAs where no attempts are made to account for spatial variability. However, even after attempting to account for spatial variability using common procedures, 1D GRAs often still yield results that are different than ground motions recorded at many downhole array sites. When 1D predictions differ from observations, the site is typically considered to be too spatially variable to effectively use 1D GRAs. While there is no doubt that some sites are indeed too variable for 1D GRAs, it is also possible that simple 1D analyses could still be effectively used at many sites if spatial variability is accounted for through a more rational, site-specific approach. In this study, an H/V geostatistical approach for building pseudo-3D Vs models is implemented to account for spatial variability in 1D GRAs. The geostatistical approach is used to generate a uniform grid of Vs profiles that have been scaled to match fundamental site frequency estimates from horizontal-to-vertical spectral ratio (H/V) noise measurements. In this article, 1D GRAs are performed for each grid point and the results are statistically combined to reflect the average site response and its variability. This 1D application is demonstrated at the Treasure Island and Delaney Park Downhole Array sites, where it is shown to produce superior fits to the small-strain recorded site response relative to existing approaches used to account for spatial variability in 1D GRAs. Using the proposed approach, we also investigate the lateral area that is likely influencing site response at each site and show that it could extend to significant distances (as much as 1 km) from the boreholes.

Description: Many recent studies have shown that we are generally unable to accurately replicate recorded ground motions at most borehole array sites using available subsurface geotechnical information and one-dimensional (1D) ground response analyses (GRAs). When 1D GRAs fail to accurately predict recorded site response, the site is often considered too complex to be effectively modeled as 1D. While three-dimensional (3D) numerical GRAs are possible and believed to be more accurate, there is rarely a 3D subsurface model available for these analyses. The lack of affordable and reliable site characterization methods to quantify spatial variability in subsurface conditions, particularly regarding shear wave velocity (Vs) measurements needed for GRAs, has pushed researchers to adopt stochastic approaches, such as Vs randomization and spatially correlated random fields. However, these stochastically generated models require the assumption of generic, or guessed, input parameters, introducing significant uncertainties into the site response predictions. This article describes a new geostatistical approach that can be used for building pseudo-3D Vs models as a means to rationally account for spatial variability in GRAs, increase model accuracy, and reduce uncertainty. Importantly, it requires only a single measured Vs profile and a number of simple, cost-effective, horizontal-to-vertical spectral ratio (H/V) noise measurements. Using Gaussian geostatistical regression, irregularly sampled estimates of fundamental site frequency from H/V measurements ( f0,H/V) are used to generate a uniform grid of f0,H/V across the site with accompanying Vs profiles that have been scaled to match each f0,H/V value, thereby producing a pseudo-3D Vs model. This approach is demonstrated at the Treasure Island and Delaney Park Downhole Array sites (TIDA and DPDA, respectively). While the pseudo-3D Vs models can be used to incorporate spatial variability into 1D, two-dimensional (2D), or 3D GRAs, their implementation in 1D GRAs at TIDA and DPDA is discussed in a companion paper.

Description: A Texas-specific [Formula: see text] map that uses geostatistical kriging integrated with a region-specific geologic proxy, field measurements of [Formula: see text], and P-wave seismogram estimates of [Formula: see text] is developed. The region-specific geologic proxy is used first to predict [Formula: see text] from the surface geologic conditions across the state, and then geostatistical kriging with an external drift is used to incorporate the local [Formula: see text] measurements/estimates into the map. Compared with the [Formula: see text] map of Texas developed from a topographic slope proxy, the Texas-specific [Formula: see text] map predicts larger [Formula: see text] values across much of Texas, except for the Gulf Coast region where the values are similar. The utilization of kriging brings the Texas-specific [Formula: see text] map into better agreement with the in situ measurements and estimates of [Formula: see text]. The sensitivity of predicted ground motions by ShakeMap to changes in [Formula: see text] values is evaluated with a scenario earthquake in the Dallas–Fort Worth area. The results suggest smaller predicted ground motions due to the generally larger values of [Formula: see text] in the Texas-specific [Formula: see text] map as compared to the [Formula: see text] from the topographic proxy.