Quality assessment for site characterization at seismic stations

Giulio, Giuseppe Di; Cultrera, Giovanna; Cornou, Cécile; Bard, Pierre‐Yves; Tfaily, Bilal Al

doi:10.1007/s10518-021-01137-6

Cited by 12 publications

(4 citation statements)

References 89 publications

(95 reference statements)

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“…Each report contains a general description on site location and references of specific studies at the site, the contact information of the compiler, together with the final value for the indicators and the associated Quality index according to the procedure explained in the companion paper (Di Giulio et al 2021) (Fig. 6).…”

Section: Summary Reportmentioning

confidence: 99%

Indicators for site characterization at seismic station: recommendation from a dedicated survey

Cultrera

Cornou

Giulio

et al. 2021

Bull Earthquake Eng

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In recent years, the permanent seismic networks worldwide have largely increased, raising the amount of earthquake signals and the applications using seismic records. Although characterization of the soil properties at recording stations has a large impact on hazard estimates, it has not been implemented so far in a standardized way for reaching high-level metadata. To address this issue, we built an online questionnaire for the identification of the indicators useful for a reliable site characterization at a seismic station. We analysed the answers of a large number of experts in different fields, which allowed us to rank 24 different indicators and to identify the most relevant ones: fundamental frequency (f0), shear-wave velocity profile (VS), time-averaged Vs over 30 m (VS30), depth of seismological and engineering bedrock (Hseis_bed and Heng_bed), surface geology and soil class. Moreover, the questionnaire proposed two additional indices in terms of cost and difficulty to obtain a reliable value of each indicator, showing that the selection of the most relevant indicators results from a complex balance between physical relevancy, average cost and reliability. For each indicator we propose a summary report, provided as editable pdf, containing the background information of data acquisition and processing details, with the aim to homogenize site metadata information at European level and to define the quality of the site characterization (see companion paper Di Giulio et al. 2021). The selected indicators and the summary reports have been shared within European and worldwide scientific community and discussed in a dedicated international workshop. They represent a first attempt to reach a homogeneous set of high-level metadata for site characterization.

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Section: Summary Reportmentioning

confidence: 99%

Indicators for site characterization at seismic station: recommendation from a dedicated survey

Cultrera

Cornou

Giulio

et al. 2021

Bull Earthquake Eng

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“…(b) The estimated average shear waves velocities to a depth of 30 m are relatively high and the correlation lacks low velocity stations with V S30 < 500 m/s. However, most high-quality seismic networks will face the same problem, as this station positioning is essential to create a quality seismic network that reliably detects weak seismic events (Di Giulio et al 2021). Nevertheless, due to a high quality and instrumentally homogeneous seismic network such as WEBNET (Institute of Geophysics AS CR 1991) differences in the amplification of incoming earthquake signals caused by local conditions, can be observed.…”

Section: Discussionmentioning

confidence: 99%

Does VS30 reflect seismic amplification? Observations from the West Bohemia Seismic Network

Mazanec,

Valenta,

Málek

2024

Nat Hazards

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The present study addresses verification of average seismic shear-wave velocity from the surface to a depth of 30 m (VS30) as a suitable proxy for a seismic amplification. For this purpose, we used instrumentally homogeneous and spatially dense seismic network WEBNET (West Bohemia Seismic Network), designed to monitor an intraplate earthquake swarm activity in the West Bohemia/Vogtland region (Czech Republic/Germany). Using a Multichannel analysis of surface waves (MASW) shear-wave velocity models and parameters VS30 and H800 (depth of Vs > 800 m/s) were obtained at 17 WEBNET sites. VS30 were compared with (i) H800 and (ii) determined bedrock densities. To understand the relation between VS30 and site amplification, VS30 values were correlated with amplitudes of two earthquakes: (1) Mw 6.4 Petrinja, Croatia (12/2020) and (2) Mw 4.2 SE of Vienna, Austria (03/2021) both recorded by the WEBNET. The correlation analysis examined four categories of seismic waves in nine frequency windows and described the relation between amplification and VS30 using newly defined regression model. The results show that for the regression model, the frequency window with the highest correlation is in the 1–3 Hz range, and this dependence is statistically best observed in the full wave record. The amplification generally decreases with increasing VS30. However, a large scatter in amplification within Eurocode 8 category B is observed. Based on the observations a new general approach is put forward to finely indicate the relation between amplification and VS30 and the use of other site proxies is discussed.

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“…Eurocode 8, 2004), and thus tremendous efforts have been devoted to deriving this parameter at many sites (e.g. Cultrera et al, 2021; Di Giulio et al, 2021). f p can also be reliably obtained from mHVSR (e.g.…”

Section: Model Developmentmentioning

confidence: 99%

How well can we predict earthquake site response so far? Machine learning vs physics-based modeling

et al. 2022

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In site-specific site-response assessments, observation-based site-specific approaches requiring a target–reference recording pair or a regional recording network cannot be implemented at many sites of interest. Thus, various estimation techniques have to be used. How effective are these techniques in predicting site-specific site responses (average over many earthquakes)? To address this question, we conduct a systematic comparison using a large data set which consists of detailed site metadata and Fourier outcrop linear site responses based on observations at 1725 K-NET and KiK-net sites. We first develop machine learning (i.e. random forest ( RF)) amplification models on a training data set (1580 sites). Then we test and compare their predictive powers at 145 independent testing sites with that of the one-dimensional (1D) ground response analysis (GRA). The standard deviation of residuals between observations and predictions, that is, between-site (site-to-site or inter-site) variability, is used as the benchmark. Results show that the machine learning model using a few predictor variables, surface roughness, peak frequency fP, HV, VS30, and depth Z2.5 achieves better performance than the physics-based modeling (GRA) using detailed 1D velocity profiles. This implies that machine learning can be more effective in using existing site information than 1D GRA which is inflicted by a high level of parametric and modeling uncertainties. This finding warrants the further exploration of machine learning in site effect characterization, especially on model transferability across different regions.

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Quality assessment for site characterization at seismic stations

Cited by 12 publications

References 89 publications

Indicators for site characterization at seismic station: recommendation from a dedicated survey

Indicators for site characterization at seismic station: recommendation from a dedicated survey

Does VS30 reflect seismic amplification? Observations from the West Bohemia Seismic Network

How well can we predict earthquake site response so far? Machine learning vs physics-based modeling

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