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- Institut für Geowissenschaften (53) (remove)
The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation-maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastias and Montalva (2016) with similar to 2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.
The mechanisms leading to large earthquakes are poorly understood and documented. Here we characterize the long-term precursory phase of the 1 April 2014 M(w)8.1 North Chile megathrust. We show that a group of coastal GPS stations accelerated westward 8months before the main shock, corresponding to a M(w)6.5 slow slip event on the subduction interface, 80% of which was aseismic. Concurrent interface foreshocks underwent a diminution of their radiation at high frequency, as shown by the temporal evolution of Fourier spectra and residuals with respect to ground motions predicted by recent subduction models. Such ground motions change suggests that in response to the slow sliding of the subduction interface, seismic ruptures are progressively becoming smoother and/or slower. The gradual propagation of seismic ruptures beyond seismic asperities into surrounding metastable areas could explain these observations and might be the precursory mechanism eventually leading to the main shock.
One of the major challenges in engineering seismology is the reliable prediction of site-specific ground motion for particular earthquakes, observed at specific distances. For larger events, a special problem arises, at short distances, with the source-to-site distance measure, because distance metrics based on a point-source model are no longer appropriate. As a consequence, different attenuation relations differ in the distance metric that they use. In addition to being a source of confusion, this causes problems to quantitatively compare or combine different ground- motion models; for example, in the context of Probabilistic Seismic Hazard Assessment, in cases where ground-motion models with different distance metrics occupy neighboring branches of a logic tree. In such a situation, very crude assumptions about source sizes and orientations often have to be used to be able to derive an estimate of the particular metric required. Even if this solves the problem of providing a number to put into the attenuation relation, a serious problem remains. When converting distance measures, the corresponding uncertainties map onto the estimated ground motions according to the laws of error propagation. To make matters worse, conversion of distance metrics can cause the uncertainties of the adapted ground-motion model to become magnitude and distance dependent, even if they are not in the original relation. To be able to treat this problem quantitatively, the variability increase caused by the distance metric conversion has to be quantified. For this purpose, we have used well established scaling laws to determine explicit distance conversion relations using regression analysis on simulated data. We demonstrate that, for all practical purposes, most popular distance metrics can be related to the Joyner-Boore distance using models based on gamma distributions to express the shape of some "residual function." The functional forms are magnitude and distance dependent and are expressed as polynomials. We compare the performance of these relations with manually derived individual distance estimates for the Landers, the Imperial Valley, and the Chi-Chi earthquakes
The estimation of minimum-misfit stochastic models from empirical ground-motion prediction equations
(2006)
In areas of moderate to low seismic activity there is commonly a lack of recorded strong ground motion. As a consequence, the prediction of ground motion expected for hypothetical future earthquakes is often performed by employing empirical models from other regions. In this context, Campbell's hybrid empirical approach (Campbell, 2003, 2004) provides a methodological framework to adapt ground-motion prediction equations to arbitrary target regions by using response spectral host-to-target-region-conversion filters. For this purpose, the empirical ground-motion prediction equation has to be quantified in terms of a stochastic model. The problem we address here is how to do this in a systematic way and how to assess the corresponding uncertainties. For the determination of the model parameters we use a genetic algorithm search. The stochastic model spectra were calculated by using a speed-optimized version of SMSIM (Boore, 2000). For most of the empirical ground-motion models, we obtain sets of stochastic models that match the empirical models within the full magnitude and distance ranges of their generating data sets fairly well. The overall quality of fit and the resulting model parameter sets strongly depend on the particular choice of the distance metric used for the stochastic model. We suggest the use of the hypocentral distance metric for the stochastic Simulation of strong ground motion because it provides the lowest-misfit stochastic models for most empirical equations. This is in agreement with the results of two recent studies of hypocenter locations in finite-source models which indicate that hypocenters are often located close to regions of large slip (Mai et al., 2005; Manighetti et al., 2005). Because essentially all empirical ground-motion prediction equations contain data from different geographical regions, the model parameters corresponding to the lowest-misfit stochastic models cannot necessarily be expected to represent single, physically realizable host regions but to model the generating data sets in an average way. In addition, the differences between the lowest-misfit stochastic models and the empirical ground-motion prediction equation are strongly distance, magnitude, and frequency dependent, which, according to the laws of uncertainty propagation, will increase the variance of the corresponding hybrid empirical model predictions (Scherbaum et al., 2005). As a consequence, the selection of empirical ground-motion models for host-to-target-region conversions requires considerable judgment of the ground-motion analyst
The use of ground-motion-prediction equations to estimate ground shaking has become a very popular approach for seismic-hazard assessment, especially in the framework of a logic-tree approach. Owing to the large number of existing published ground-motion models, however, the selection and ranking of appropriate models for a particular target area often pose serious practical problems. Here we show how observed around-motion records can help to guide this process in a systematic and comprehensible way. A key element in this context is a new, likelihood based, goodness-of-fit measure that has the property not only to quantify the model fit but also to measure in some degree how well the underlying statistical model assumptions are met. By design, this measure naturally scales between 0 and 1, with a value of 0.5 for a situation in which the model perfectly matches the sample distribution both in terms of mean and standard deviation. We have used it in combination with other goodness-of-fit measures to derive a simple classification scheme to quantify how well a candidate ground-rnotion-prediction equation models a particular set of observed-response spectra. This scheme is demonstrated to perform well in recognizing a number of popular ground-motion models from their rock-site- recording, subsets. This indicates its potential for aiding the assignment of logic-tree weights in a consistent and reproducible way. We have applied our scheme to the border region of France, Germany, and Switzerland where the M-w 4.8 St. Die earthquake of 22 February 2003 in eastern France recently provided a small set of observed-response spectra. These records are best modeled by the ground-motion-prediction equation of Berge-Thierry et al. (2003), which is based on the analysis of predominantly European data. The fact that the Swiss model of Bay et al. (2003) is not able to model the observed records in an acceptable way may indicate general problems arising from the use of weak-motion data for strong-motion prediction
Composite ground-motion models and logic trees: Methodology, sensitivities, and uncertainties
(2005)
Logic trees have become a popular tool in seismic hazard studies. Commonly, the models corresponding to the end branches of the complete logic tree in a probabalistic seismic hazard analysis (PSHA) are treated separately until the final calculation of the set of hazard curves. This comes at the price that information regarding sensitivities and uncertainties in the ground-motion sections of the logic tree are only obtainable after disaggregation. Furthermore, from this end-branch model perspective even the designers of the logic tree cannot directly tell what ground-motion scenarios most likely would result from their logic trees for a given earthquake at a particular distance, nor how uncertain these scenarios might be or how they would be affected by the choices of the hazard analyst. On the other hand, all this information is already implicitly present in the logic tree. Therefore, with the ground-motion perspective that we propose in the present article, we treat the ground-motion sections of a complete logic tree for seismic hazard as a single composite model representing the complete state-of-knowledge-and-belief of a particular analyst on ground motion in a particular target region. We implement this view by resampling the ground-motion models represented in the ground-motion sections of the logic tree by Monte Carlo simulation (separately for the median values and the sigma values) and then recombining the sets of simulated values in proportion to their logic-tree branch weights. The quantiles of this resampled composite model provide the hazard analyst and the decision maker with a simple, clear, and quantitative representation of the overall physical meaning of the ground-motion section of a logic tree and the accompanying epistemic uncertainty. Quantiles of the composite model also provide an easy way to analyze the sensitivities and uncertainties related to a given logic-tree model. We illustrate this for a composite ground- motion model for central Europe. Further potential fields of applications are seen wherever individual best estimates of ground motion have to be derived from a set of candidate models, for example, for hazard rnaps, sensitivity studies, or for modeling scenario earthquakes
To evaluate the spatiotemporal variations of ground motions in northern Chile, we built a high-quality rock seismic acceleration database and an interface earthquakes catalog. Two ground-motion prediction equation (GMPE) models for subduction zones have been tested and validated for the area. They were then used as backbone models to describe the time-space variations of earthquake frequency content (Fourier and response spectra). Consistent with previous studies of large subduction earthquakes, moderate interface earthquakes in northern Chile show an increase of the high-frequency energy released with depth. A regional variability of earthquake frequency content is also observed, which may be related to a lateral segmentation of the mechanical properties of the subduction interface. Finally, interface earthquakes show a temporal evolution of their frequency content in the earthquake sequence associated with the 2014 Iquique M-w 8.1 megathrust earthquake. Surprisingly, the change does not occur with the mainshock but is associated with an 8 month slow slip preceding the megathrust. Electronic Supplement: Strong-motion database.
Two ground motion prediction equation models for subduction zones have been tested using a public ground motion database of the KiK-net records obtained by automated processing protocols (Dawood et al., 2016, https://doi.org/10.1193/071214EQS106). The database contains records of more than 700 interface earthquakes that occurred on the Japan subduction between 1998 and 2012. The Zhao et al. (2006, https://doi.org/10.1785/0120050122) ground motion prediction equation was shown to be the best suited model for the region. It was then used as backbone to analyze the variability of ground motion records. The residuals between observed and predicted ground motions have been analyzed to study the spatial variation of the earthquakes' ground motion frequency content on the Japan megathrust. This analysis revealed a depth dependency of generated ground motions consistent with the downdip segmentation proposed for subduction interfaces (Lay et al., 2012, https://doi.org/10.1029/2011JB009133), a regional ground motion dependency that may be related with lateral variations of the mechanical properties of the subduction interface and a high-frequency radiations drop in the earthquake sequence that preceded the Tohoku-Oki earthquake Mw 9.0. The regional ground motion dependency suggests the existence of different domains along trench of the Japan subduction megathrust that control the ground motions and the wave radiation patterns of interface earthquakes. The location of their boundaries is consistent with the extension of the rupture of the 2011 Tohoku-Oki earthquake, with pre-Tohoku interseismic coupling, and with the free air gravity anomalies.
1-D site response analysis dominates earthquake engineering practice, while local 2-D/3-D models are often required at sites where the site response is complex. For such sites, the 1-D representation of the soil column can account neither for topographic effects or dipping layers nor for locally generated horizontally propagating surface waves. It then remains a crucial task to identify whether the site response can be modelled sufficiently precisely by 1-D analysis. In this study we develop a method to classify sites according to their 1-D or 2-D/3-D nature. This classification scheme is based on the analysis of surface earthquake recordings and the evaluation of the variability and similarity of the horizontal Fourier spectra. The taxonomy is focused on capturing significant directional dependencies and interevent variabilities indicating a more probable 2-D/3-D structure around the site causing the ground motion to be more variable. While no significant correlation of the 1-D/3-D site index with environmental parameters and site proxies seems to exist, a reduction in the within-site (single-station) variability is found. The reduction is largest (up to 20 per cent) for purely 1-D sites. Although the taxonomy system is developed using surface stations of the KiK-net network in Japan as considerable additional information is available, it can also be applied to any (non-downhole array) site.
A proper assessment of seismic reference site conditions has important applications as they represent the basis on which ground motions and amplifications are generally computed. Besides accounting for the average S-wave velocity over the uppermost 30 m (V-S30), the parameterization of high-frequency ground motions beyond source-corner frequency received significant attention. kappa, an empirical parameter introduced by Anderson and Hough (1984), is often used to represent the spectral decay of the acceleration spectrum at high frequencies. The lack of hard-rock records and the poor understanding of the physics of kappa introduced significant epistemic uncertainty in the final seismic hazard of recent projects. Thus, determining precise and accurate regional hard-rock kappa(0) values is critical. We propose an alternative procedure for capturing the reference kappa(0) on regional scales by linking thewell-known high-frequency attenuation parameter kappa and the properties of multiple-scattered coda waves. Using geological and geophysical data around more than 1300 stations for separating reference and soft soil sites and based on more than 10,000 crustal earthquake recordings, we observe that kappa(0) from multiple-scattered coda waves seems to be independent of the soil type but correlated with the hard-rock kappa(0), showing significant regional variations across Europe. The values range between 0.004 s for northern Europe and 0.020 s for the southern and southeastern parts. On the other hand, measuring kappa (and correspondingly kappa(0)) on the S-wave window (as classically proposed), the results are strongly affected by transmitted (reflected, refracted, and scattered) waves included in the analyzed window biasing the proper assessment of kappa(0). This effect is more pronounced for soft soil sites. In this way, kappa(coda)(0) can serve as a proxy for the regional hard-rock kappa(0) at the reference sites.