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Surface wave methods gained in the past decades a primary role in many seismic projects. Specifically, they are often used to retrieve a 1D shear wave velocity model or to estimate the V-s,V-30 at a site. The complexity of the interpretation process and the variety of possible approaches to surface wave analysis make it very hard to set a fixed standard to assure quality and reliability of the results. The present guidelines provide practical information on the acquisition and analysis of surface wave data by giving some basic principles and specific suggestions related to the most common situations. They are primarily targeted to non-expert users approaching surface wave testing, but can be useful to specialists in the field as a general reference. The guidelines are based on the experience gained within the InterPACIFIC project and on the expertise of the participants in acquisition and analysis of surface wave data.
The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V-S30), the topographical slope (slope), the fundamental resonance frequency (f(0)) and the depth beyond which V-s exceeds 800 m/s (H800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [V-S30-f(0)], [V-S30-H-800], [f(0)-slope], [H-800-slope], [V-S30-slope] and [f(0)-H-800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and Mw, RJB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median groundmotion prediction, it does impact the level of aleatory uncertainty. VS30 is found to perform the best of single proxies at short periods (T < 0.6 s), while f(0) and H-800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [V-S30-H-800] and [f(0)-slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the "stiff" spectral ordinate at the considered period.
The main scope of the InterPACIFIC (Intercomparison of methods for site parameter and velocity profile characterization) project is to assess the reliability of in-hole and surface-wave methods, used for estimating shear wave velocity. Three test-sites with different subsurface conditions were chosen: a soft soil, a stiff soil and a rock outcrop. This paper reports the surface-wave methods results. Specifically 14 teams of expert users analysed the same experimental surface-wave datasets, consisting of both passive and active data. Each team adopted their own strategy to retrieve the dispersion curve and the shear-wave velocity profile at each site. Despite different approaches, the dispersion curves are quite in agreement with each other. Conversely, the shear-wave velocity profiles show a certain variability that increases in correspondence of major stratigraphic interfaces. This larger variability is mainly due to non-uniqueness of the solution and lateral variability. As expected, the observed variability in V-s,V-30 estimatesis small, as solution non-uniqueness plays a limited role. (C) 2015 Elsevier Ltd. All rights reserved.
We compare the ability of various site-condition proxies (SCPs) to reduce the aleatory variability of ground motion prediction equations (GMPEs). Three SCPs (measured V-S30, inferred V-S30, local topographic slope) and two accelerometric databases (RESORCE and NGA-West2) are considered. An artificial neural network (ANN) approach including a random-effect procedure is used to derive GMPEs setting the relationship between peak ground acceleration (PGA), peak ground velocity (PGV), pseudo-spectral acceleration [PSA(T)], and explanatory variables (M-w, R-JB, and V-S30 or Slope). The analysis is performed using both discrete site classes and continuous proxy values. All "non-measured" SCPs exhibit a rather poor performance in reducing aleatory variability, compared to the better performance of measured V-S30. A new, fully data-driven GMPE based on the NGA-West2 is then derived, with an aleatory variability value depending on the quality of the SCP. It proves very consistent with previous GMPEs built on the same data set. Measuring V-S30 allows for benefit from an aleatory variability reduction up to 15%.
VS30, slope, H800 and f0
(2017)
The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (VS30), the topographical slope (slope), the fundamental resonance frequency (f0) and the depth beyond which Vs exceeds 800 m/s (H800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [VS30–f0], [VS30–H800], [f0–slope], [H800–slope], [VS30–slope] and [f0–H800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and Mw, RJB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median groundmotion prediction, it does impact the level of aleatory uncertainty. VS30 is found to perform the best of single proxies
at short periods (T < 0.6 s), while f0 and H800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [VS30–H800] and [f0–slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the “stiff” spectral ordinate at the considered period.
This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan.
The inversion of surface-wave dispersion curve to derive shear-wave velocity profile is a very delicate process dealing with a nonunique problem, which is strongly dependent on the model space parameterization. When independent and reliable information is not available, the selection of most representative models within the ensemble produced. by the inversion is often difficult. We implemented a strategy in the inversion of dispersion curves able to investigate the influence of the parameterization of the model space and to select a "best" class of models. We analyzed surface-wave dispersion curves measured at 14 European strong..-motion sites within the NERIES EC-Project. We focused on the inversion task exploring the model space by means of four distinct pararneterization classes composed of layers progressively added over a half-space. The classes differ in the definition of the shear-wave velocity profile; we considered models with uniform velocity as well as models with increasing velocity with depth. At each site and for each model parameterization, we performed an extensive surface-wave inversion (200,100 models for five seeds) using the conditional neighborhood algorithm. We addressed the model evaluation following the corrected Akaike's information criterion (AlCc) that combines the concept of misfit to the number of degrees of freedom of the system. The misfit was computed as least-squares estimation between theoretical and observed dispersion curve. The model complexity was accounted in a penalty term by AlCc. By applying such inversion strategy on 14 strong-motion sites, we found that the best parameterization of the model space is mostly three to four layers over a half-space: where the shear-wave velocity of the uppermost layers can follow uniform or power-law dependence with depth. The shear-wave velocity profiles derived by inversion agree with shear-wave velocity profiles provided by borehole surveys at approximately 80% of the sites.
The knowledge of the local soil structure is important for the assessment of seismic hazards. A widespread, but time-consuming technique to retrieve the parameters of the local underground is the drilling of boreholes. Another way to obtain the shear wave velocity profile at a given location is the inversion of surface wave dispersion curves. To ensure a good resolution for both superficial and deeper layers, the used dispersion curves need to cover a wide frequency range. This wide frequency range can be obtained using several arrays of seismic sensors or a single array comprising a large number of sensors. Consequently, these measurements are time-consuming. A simpler alternative is provided by the use of the ellipticity of Rayleigh waves. The frequency dependence of the ellipticity is tightly linked to the shear wave velocity profile. Furthermore, it can be measured using a single seismic sensor. As soil structures obtained by scaling of a given model exhibit the same ellipticity curve, any inversion of the ellipticity curve alone will be ambiguous. Therefore, additional measurements which fix the absolute value of the shear wave velocity profile at some points have to be included in the inversion process. Small-scale spatial autocorrelation measurements or MASW measurements can provide the needed data. Using a theoretical soil structure, we show which parts of the ellipticity curve have to be included in the inversion process to get a reliable result and which parts can be omitted. Furthermore, the use of autocorrelation or high-frequency dispersion curves will be highlighted. The resulting guidelines for inversions including ellipticity data are then applied to real data measurements collected at 14 different sites during the European NERIES project. It is found that the results are in good agreement with dispersion curve measurements. Furthermore, the method can help in identifying the mode of Rayleigh waves in dispersion curve measurements.