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Ground-motion models (GMMs) are often used to predict the random distribution of Spectral accelerations (SAs) at a site due to a nearby earthquake. In probabilistic seismic hazard and risk assessment, large earthquakes occurring close to a site are considered as critical scenarios. GMMs are expected to predict realistic SAs with low within-model uncertainty (sigma(mu)) for such rare scenarios. However, the datasets used to regress GMMs are usually deficient of data from critical scenarios. The (Kotha et al., A Regionally Adaptable Ground-Motion Model for Shallow Crustal Earthquakes in Europe Bulletin of Earthquake Engineering 18:4091-4125, 2020) GMM developed from the Engineering strong motion (ESM) dataset was found to predict decreasing short-period SAs with increasing M-W >= M-h = 6.2, and with large sigma(mu) at near-source distances <= 30km. In this study, we updated the parametrisation of the GMM based on analyses of ESM and the Near source strong motion (NESS) datasets. With M-h = 5.7, we could rectify the M-W scaling issue, while also reducing sigma(mu). at M-W >= M-h. We then evaluated the GMM against NESS data, and found that the SAs from a few large, thrust-faulting events in California, New Zealand, Japan, and Mexico are significantly higher than GMM median predictions. However, recordings from these events were mostly made on soft-soil geology, and contain anisotropic pulse-like effects. A more thorough non-ergodic treatment of NESS was not possible because most sites sampled unique events in very diverse tectonic environments. We provide an updated set of GMM coefficients,sigma(mu), and heteroscedastic variance models; while also cautioning against its application for M-W <= 4 in low-moderate seismicity regions without evaluating the homogeneity of M-W estimates between pan-European ESM and regional datasets.
The within-site variability in site response is the randomness in site response at a given site from different earthquakes and is treated as aleatory variability in current seismic hazard/risk analyses.
In this study, we investigate the single-station variability in linear site response at K-NET and KiK-net stations in Japan using a large number of earthquake recordings.
We found that the standard deviation of the horizontal-to-vertical Fourier spectral ratio at individual sites, that is single-station horizontal-to-vertical spectral ratio (HVSR) sigma sigma(HV,s), approximates the within-site variability in site response quantified using surface-to-borehole spectral ratios (for oscillator frequencies higher than the site fundamental frequency) or empirical ground-motion models.
Based on this finding, we then utilize the single-station HVSR sigma as a convenient tool to study the site-response variability at 697 KiK-net and 1169 K-NET sites.
Our results show that at certain frequencies, stiff, rough and shallow sites, as well as small and local events tend to have a higher sigma(HV,s).
However, when being averaged over different sites, the single-station HVSR sigma, that is sigma(HV), increases gradually with decreasing frequency. In the frequency range of 0.25-25 Hz, sigma(HV) is centred at 0.23-0.43 in ln scales (a linear scale factor of 1.26-1.54) with one standard deviation of less than 0.1. sigma(HV) is quite stable across different tectonic regions, and we present a constant, as well as earthquake magnitude- and distance-dependent sigma(HV) models.
Megathrust earthquakes impose changes of differential stress and pore pressure in the lithosphere-asthenosphere system that are transiently relaxed during the postseismic period primarily due to afterslip, viscoelastic and poroelastic processes.
Especially during the early postseismic phase, however, the relative contribution of these processes to the observed surface deformation is unclear.
To investigate this, we use geodetic data collected in the first 48 days following the 2010 Maule earthquake and a poro-viscoelastic forward model combined with an afterslip inversion.
This model approach fits the geodetic data 14% better than a pure elastic model. Particularly near the region of maximum coseismic slip, the predicted surface poroelastic uplift pattern explains well the observations.
If poroelasticity is neglected, the spatial afterslip distribution is locally altered by up to +/- 40%.
Moreover, we find that shallow crustal aftershocks mostly occur in regions of increased postseismic pore-pressure changes, indicating that both processes might be mechanically coupled.
In seismic risk assessment, the sources of uncertainty associated with building exposure modelling have not received as much attention as other components related to hazard and vulnerability. Conventional practices such as assuming absolute portfolio compositions (i.e., proportions per building class) from expert-based assumptions over aggregated data crudely disregard the contribution of uncertainty of the exposure upon earthquake loss models. In this work, we introduce the concept that the degree of knowledge of a building stock can be described within a Bayesian probabilistic approach that integrates both expert-based prior distributions and data collection on individual buildings. We investigate the impact of the epistemic uncertainty in the portfolio composition on scenario-based earthquake loss models through an exposure-oriented logic tree arrangement based on synthetic building portfolios. For illustrative purposes, we consider the residential building stock of Valparaiso (Chile) subjected to seismic ground-shaking from one subduction earthquake. We have found that building class reconnaissance, either from prior assumptions by desktop studies with aggregated data (top-down approach), or from building-by-building data collection (bottom-up approach), plays a fundamental role in the statistical modelling of exposure. To model the vulnerability of such a heterogeneous building stock, we require that their associated set of structural fragility functions handle multiple spectral periods. Thereby, we also discuss the relevance and specific uncertainty upon generating either uncorrelated or spatially cross-correlated ground motion fields within this framework. We successively show how various epistemic uncertainties embedded within these probabilistic exposure models are differently propagated throughout the computed direct financial losses. This work calls for further efforts to redesign desktop exposure studies, while also highlighting the importance of exposure data collection with standardized and iterative approaches.
Ground motion with strong-velocity pulses can cause significant damage to buildings and structures at certain periods; hence, knowing the period and velocity amplitude of such pulses is critical for earthquake structural engineering.
However, the physical factors relating the scaling of pulse periods with magnitude are poorly understood.
In this study, we investigate moderate but damaging earthquakes (M-w 6-7) and characterize ground- motion pulses using the method of Shahi and Baker (2014) while considering the potential static-offset effects.
We confirm that the within-event variability of the pulses is large. The identified pulses in this study are mostly from strike-slip-like earthquakes. We further perform simulations using the freq uency-wavenumber algorithm to investigate the causes of the variability of the pulse periods within and between events for moderate strike-slip earthquakes.
We test the effect of fault dips, and the impact of the asperity locations and sizes. The simulations reveal that the asperity properties have a high impact on the pulse periods and amplitudes at nearby stations.
Our results emphasize the importance of asperity characteristics, in addition to earthquake magnitudes for the occurrence and properties of pulses produced by the forward directivity effect.
We finally quantify and discuss within- and between-event variabilities of pulse properties at short distances.
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 similar to 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.
The simulation of broad-band (0.1 to 10 + Hz) ground-shaking over deep and spatially extended sedimentary basins at regional scales is challenging. We evaluate the ground-shaking of a potential M 6.5 earthquake in the southern Lower Rhine Embayment, one of the most important areas of earthquake recurrence north of the Alps, close to the city of Cologne in Germany. In a first step, information from geological investigations, seismic experiments and boreholes is combined for deriving a harmonized 3D velocity and attenuation model of the sedimentary layers. Three alternative approaches are then applied and compared to evaluate the impact of the sedimentary cover on ground-motion amplification. The first approach builds on existing response spectra ground-motion models whose amplification factors empirically take into account the influence of the sedimentary layers through a standard parameterization. In the second approach, site-specific 1D amplification functions are computed from the 3D basin model. Using a random vibration theory approach, we adjust the empirical response spectra predicted for soft rock conditions by local site amplification factors: amplifications and associated ground-motions are predicted both in the Fourier and in the response spectra domain. In the third approach, hybrid physics-based ground-motion simulations are used to predict time histories for soft rock conditions which are subsequently modified using the 1D site-specific amplification functions computed in method 2. For large distances and at short periods, the differences between the three approaches become less notable due to the significant attenuation of the sedimentary layers. At intermediate and long periods, generic empirical ground-motion models provide lower levels of amplification from sedimentary soils compared to methods taking into account site-specific 1D amplification functions. In the near-source region, hybrid physics-based ground-motions models illustrate the potentially large variability of ground-motion due to finite source effects.
We construct and examine the prototype of a deep learning-based ground-motion model (GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling as an image processing task, in which a specific type of neural network, the U-Net, relates continuous, horizontal maps of earthquake predictive parameters to sparse observations of a ground-motion intensity measure (IM). The processing of map-shaped data allows the natural incorporation of absolute earthquake source and observation site coordinates, and is, therefore, well suited to include site-, source-, and path-specific amplification effects in a nonergodic GMM. Data-driven interpolation of the IM between observation points is an inherent feature of the U-Net and requires no a priori assumptions. We evaluate our model using both a synthetic dataset and a subset of observations from the KiK-net strong motion network in the Kanto basin in Japan. We find that the U-Net model is capable of learning the magnitude???distance scaling, as well as site-, source-, and path-specific amplification effects from a strong motion dataset. The interpolation scheme is evaluated using a fivefold cross validation and is found to provide on average unbiased predictions. The magnitude???distance scaling as well as the site amplification of response spectral acceleration at a period of 1 s obtained for the Kanto basin are comparable to previous regional studies.
In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.
The main Marmara fault (MMF) extends for 150 km through the Sea of Marmara and forms the only portion of the North Anatolian fault zone that has not ruptured in a large event (Mw >7) for the last 250 yr. Accordingly, this portion is potentially a major source contributing to the seismic hazard of the Istanbul region. On 26 September 2019, a sequence of moderate-sized events started along the MMF only 20 km south of Istanbul and were widely felt by the population. The largest three events, 26 September Mw 5.8 (10:59 UTC), 26 September 2019 Mw 4.1 (11:26 UTC), and 20 January 2020 Mw 4.7 were recorded by numerous strong-motion seismic stations and the resulting ground motions were compared to the predicted means resulting from a set of the most recent ground-motion prediction equations (GMPEs). The estimated residuals were used to investigate the spatial variation of ground motion across the Marmara region. Our results show a strong azimuthal trend in ground-motion residuals, which might indicate systematically repeating directivity effects toward the eastern Marmara region.