Building 3D Shear-Wave Velocity Models Using Surface Waves Testing: The Tarcento Basin Case History

Piatti, Claudio; Foti, Sebastiano; Socco, Laura; Boiero, Daniele

doi:10.1785/0120120089

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Luo et al, 2008; Mi et al, 2017; Parolai et al, 2001) and 3D (e.g. Pan et al, 2018; Piatti et al, 2013; Strobbia et al, 2011) Vs models by spatial interpolation techniques. For example, this approach has been applied to construct pseudo-2D Vs profiles from multi-channel analysis of surface waves (MASW) by interpolating a series of 1D Vs models derived from inversion of waveforms collected using different receiver arrays (e.g.…”

Section: Introductionmentioning

confidence: 99%

An H/V geostatistical approach for building pseudo-3D Vs models to account for spatial variability in ground response analyses Part I: Model development

Hallal

Cox

2021

Earthquake Spectra

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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.

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Section: Introductionmentioning

confidence: 99%

An H/V geostatistical approach for building pseudo-3D Vs models to account for spatial variability in ground response analyses Part I: Model development

Hallal

Cox

2021

Earthquake Spectra

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“…Within this context, several methods for constructing 2D/3D Vs models using in-situ measurements have been developed. The simplest approach is to utilize multiple spatially distributed 1D Vs profiles to construct pseudo-2D (e.g., Luo et al, 2008;Mi et al, 2017;Parolai et al, 2001) and 3D (e.g., Pan et al, 2018;Piatti et al, 2013;Strobbia et al, 2011) Vs models by spatial interpolation techniques. For example, this approach has been applied to construct pseudo-2D Vs profiles from multichannel analysis of surface waves (MASW) by interpolating a series of 1D Vs models derived from inversion of waveforms collected using different receiver arrays (e.g., Ismail et al, 2014;Mahvelati and Coe, 2017;Park, 2005).…”

Section: Introductionmentioning

confidence: 99%

An H/V Geostatistical Approach for Building Pseudo-3D Vs Models to Account for Spatial Variability in Ground Response Analyses I: Model Development

Hallal¹,

Cox²

2020

Preprint

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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 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 assumed, input parameters, introducing significant uncertainties into the site response predictions. This paper 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, 2D, or 3D GRAs, their implementation in 1D GRAs at TIDA and DPDA is discussed in a companion paper.

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“…Nowadays, it is accepted that also invasive tests are subjected to a non-negligible uncertainty, particularly for shallow depths (Garofalo et al 2016b). Several inter-method studies showed that the agreement between the results is good if both invasive and non-invasive tests are conducted and interpreted using state of the art methods (Foti et al 2011;Kim et al 2013;Piatti et al 2013b;Cox et al 2014;Garofalo et al 2016a;Garofalo et al 2016b).…”

Section: Introductionmentioning

confidence: 99%

The Polito Surface Wave flat-file Database (PSWD): statistical properties of test results and some inter-method comparisons

Passeri

Comina

Foti

et al. 2021

Bull Earthquake Eng

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The compilation and maintenance of experimental databases are of crucial importance in all research fields, allowing for researchers to develop and test new methodologies. In this work, we present a flat-file database of experimental dispersion curves and shear wave velocity profiles, mainly from active surface wave testing, but including also data from passive surface wave testing and invasive methods. The Polito Surface Wave flat-file Database (PSWD) is a gathering of experimental measurements collected within the past 25 years at different Italian sites. Discussion on the database content is reported in this paper to evaluate some statistical properties of surface wave test results. Comparisons with other methods for shear wave velocity measurements are also considered. The main novelty of this work is the homogeneity of the PSWD in terms of processing and interpretation methods. A common processing strategy and a new inversion approach were applied to all the data in the PSWD to guarantee consistency. The PSWD can be useful for further correlation studies and is made available as a reference benchmark for the validation and verification of novel interpretation procedures by other researchers.

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Building 3D Shear-Wave Velocity Models Using Surface Waves Testing: The Tarcento Basin Case History

Cited by 10 publications

References 29 publications

An H/V geostatistical approach for building pseudo-3D Vs models to account for spatial variability in ground response analyses Part I: Model development

An H/V geostatistical approach for building pseudo-3D Vs models to account for spatial variability in ground response analyses Part I: Model development

An H/V Geostatistical Approach for Building Pseudo-3D Vs Models to Account for Spatial Variability in Ground Response Analyses I: Model Development

The Polito Surface Wave flat-file Database (PSWD): statistical properties of test results and some inter-method comparisons

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