Within-site variability in earthquake site response

Zhu, Chuanbin; Cotton, Fabrice; Kwak, Dongmin; Ji, Kun; Kawase, Hiroshi; Pilz, Marco

doi:10.1093/gji/ggab481

Cited by 13 publications

(12 citation statements)

References 42 publications

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“…Similarly, to reduce the degree of modeling uncertainty, prior investigations can be carried out to determine whether a target site has subsurface irregularities. This can be achieved using, for instance, the directionality of HVSR (Matsushima et al, 2017), variability of HVSR from different events (HVSR sigma σ HV,s , Zhu et al, 2021a), or a combination of indicators (Pilz et al, 2021). Then decisions can be made about the suitability of 1D analyses or the necessity of multi-dimensional models.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, to mitigate both types of errors simultaneously, Zhu et al (2021a) suggested a dual-parameter framework: f 0, TTF / f 0, HV (the ratio of f 0, TTF to f 0, HV ) and σ HV,s , to constrain parametric and modeling uncertainty, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, we only focus on the repeatable site response at a given site, that is, a + δ S 2 S s 0 , and the event-specific residual and the randomness in site response are investigated by Zhu et al (2021a). Currently, the repeatable site response at a target site can be estimated through various methods ( m ).…”

Section: Evaluation Frameworkmentioning

confidence: 99%

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How well can we predict earthquake site response so far? Site-specific approaches

et al. 2022

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Earthquake site responses or site effects are the modifications of surface geology to seismic waves. How well can we predict the site effects (average over many earthquakes) at individual sites so far? To address this question, we tested and compared the effectiveness of different estimation techniques in predicting the outcrop Fourier site responses separated using the general inversion technique (GIT) from recordings. Techniques being evaluated are (a) the empirical correction to the horizontal-to-vertical spectral ratio of earthquakes (c-HVSR), (b) one-dimensional ground response analysis (GRA), and (c) the square-root-impedance (SRI) method (also called the quarter-wavelength approach). Our results show that c-HVSR can capture significantly more site-specific features in site responses than both GRA and SRI in the aggregate, especially at relatively high frequencies. c-HVSR achieves a “good match” in spectral shape at ∼80%–90% of 145 testing sites, whereas GRA and SRI fail at most sites. GRA and SRI results have a high level of parametric and/or modeling errors which can be constrained, to some extent, by collecting on-site recordings.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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How well can we predict earthquake site response so far? Site-specific approaches

et al. 2022

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“…), geomorphological category (Geomorp), V S30 , predominant frequency on HVSR curve (f P, HV or f p for brevity), depth to SH-wave velocity 2.5 km/s horizon from the Japan Seismic Hazard Information Station (J-SHIS) 3D velocity model (Z 2.5 , Fujiwara et al, 2012), the mean eHVSR curve over multiple events (A HV (f)), and the high-frequency decay parameter kappa (k 0 ). Although some of the site information is not typically considered as site metadata, for example, A HV (f), rather we regard them as ''predictor variables'' of site response ) uncaptured by GMMnative site-response models (after Zhu et al, 2021b). These results are compiled from studies on Japan (Al Atik, 2015;Hassani and Atkinson, 2020;Kotha et al, 2018;Kwak and Seyhan, 2020;Rodriguez-Marek et al, 2011;Weatherill et al, 2020).…”

Section: Datamentioning

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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“…Site effects are caused, among others, by the seismic impedance between rock and sediments, the 1D, 2D and 3D resonances, and the edge-generated surface waves. In turn, the site response can vary significantly from one site to another (siteto-site variability, e.g., Bindi et al, 2009;Hollender et al, 2015;Bindi et al, 2017;Imtiaz et al, 2018;Perron et al, 2018) and from one earthquake to another (within-site variability, e.g., Thompson et al, 2012;Ktenidou et al, 2016;Ktenidou et al, 2017;Maufroy et al, 2017;Perron, 2017;Zhu et al, 2018;Zhu et al, 2022). At large ground motion levels, non-linear effects in specific soils will increase the site response uncertainty as well (Régnier et al, 2013;Régnier et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Minimum Number of Earthquakes in Empirical Site Response Assessment: Input for New Requirements for Microzonation in the Swiss Building Codes

Perron¹,

Bergamo²,

Fäh³

2022

Front. Earth Sci.

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Site-specific hazard analyses and microzonation are important products for densely populated areas and facilities of special risk. The empirical amplification function is classically estimated using the standard spectral ratio (SSR) approach. The SSR simply consists in comparing earthquake recordings on soil sites with the recording of the same earthquake on a close-by rock reference. Recording a statistically significant number of earthquakes to apply the SSR can however be difficult, especially in low seismicity areas and noisy urban environments. On the contrary, computing the SSR from too few earthquakes can lead to an uncertain evaluation of the mean amplification function. Defining the minimum number of earthquake recordings in empirical site response assessment is thus important. We compute empirical amplification functions at 60 KiKnet sites in Japan from several hundred earthquakes and three Swiss sites from several tens of earthquakes. We performed statistical analysis on the amplification functions to estimate the geometric mean and standard deviation and more importantly to determine the distribution law of the amplification factors as a function of the number of recordings. Independent to the site and to the frequency, we find that the log-normal distribution is a very good approximation for the site response. Based on that, we develop a strategy to estimate the minimum number of earthquakes from the confidence interval definition. We find that 10 samples are the best compromise between minimizing the number of recordings and having a good statistical significance of the results. As a general rule, a minimum of 10 uncorrelated earthquakes should be considered, but the higher the number of earthquakes, the lower the uncertainty on the geometric mean of the site amplification function. Moreover, the linear site response is observed to be independent to the intensity of the ground motion level for the analyzed dataset.

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Within-site variability in earthquake site response

Cited by 13 publications

References 42 publications

How well can we predict earthquake site response so far? Site-specific approaches

How well can we predict earthquake site response so far? Site-specific approaches

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

Evaluating the Minimum Number of Earthquakes in Empirical Site Response Assessment: Input for New Requirements for Microzonation in the Swiss Building Codes

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