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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.
Probabilistic assessment of seismic hazard and risk over a geographical region presents the modeler with challenges in the characterization of the site amplification that are not present in site-specific assessment. Using site-to-site residuals from a ground motion model fit to observations from the Japanese KiK-net database, correlations between measured local amplifications and mappable proxies such as topographic slope and geology are explored. These are used subsequently to develop empirical models describing amplification as a direct function of slope, conditional upon geological period. These correlations also demonstrate the limitations of inferring 30-m shearwave velocity from slope and applying them directly into ground motion models. Instead, they illustrate the feasibility of deriving spectral acceleration amplification factors directly from sets of observed records, which are calibrated to parameters that can be mapped uniformly on a regional scale. The result is a geologically calibrated amplification model that can be incorporated into national and regional seismic hazard and risk assessment, ensuring that the corresponding total aleatory variability reflects the predictive capability of the mapped site proxy.
A ground motion logic tree for seismic hazard analysis in the stable cratonic region of Europe
(2020)
Regions of low seismicity present a particular challenge for probabilistic seismic hazard analysis when identifying suitable ground motion models (GMMs) and quantifying their epistemic uncertainty. The 2020 European Seismic Hazard Model adopts a scaled backbone approach to characterise this uncertainty for shallow seismicity in Europe, incorporating region-to-region source and attenuation variability based on European strong motion data. This approach, however, may not be suited to stable cratonic region of northeastern Europe (encompassing Finland, Sweden and the Baltic countries), where exploration of various global geophysical datasets reveals that its crustal properties are distinctly different from the rest of Europe, and are instead more closely represented by those of the Central and Eastern United States. Building upon the suite of models developed by the recent NGA East project, we construct a new scaled backbone ground motion model and calibrate its corresponding epistemic uncertainties. The resulting logic tree is shown to provide comparable hazard outcomes to the epistemic uncertainty modelling strategy adopted for the Eastern United States, despite the different approaches taken. Comparison with previous GMM selections for northeastern Europe, however, highlights key differences in short period accelerations resulting from new assumptions regarding the characteristics of the reference rock and its influence on site amplification.
Evaluation of a novel application of earthquake HVSR in site-specific amplification estimation
(2020)
Ground response analyses (GRA) model the vertical propagations of SH waves through flat-layered media (1DSH) and are widely carried out to evaluate local site effects in practice. Horizontal-to-vertical spectral ratio (HVSR) technique is a cost-effective approach to extract certain site-specific information, e.g., site fundamental frequency (f(0)), but HVSR values cannot be directly used to approximate the levels of S-wave amplifications. Motivated by the work of Kawase et al. (2019), we propose a procedure to correct earthquake HVSR amplitudes for direct amplification estimations. The empirical correction compensates HVSR by generic vertical amplification spectra categorized by the vertical fundamental frequency (f(0v)) via kappa-means clustering. In this investigation, we evaluate the effectiveness of the corrected HVSR in approximating observed linear amplifications in comparison with 1DSH modellings. We select a total of 90 KiK-net (Kiban Kyoshin network) surface-downhole sites which are found to have no velocity contrasts below their boreholes and thus of which surface-to-borehole spectral ratios (SBSRs) can be taken as their empirical transfer functions (ETFs). 1DSH-based theoretical transfer functions (TTFs) are computed in the linear domain considering uncertainties in Vs profiles through randomizations. Five goodness-of-fit metrics are adopted to gauge the closeness between observed (ETF) and predicted (i.e., TTF and corrected HVSR) amplifications in both amplitude and spectral shape over frequencies from f(0) to 25 Hz. We find that the empirical correction to HVSR is highly effective and achieves a "good match" in both spectral shape and amplitude at the majority of the 90 KiK-net sites, as opposed to less than one-third for the 1DSH modelling. In addition, the empirical correction does not require a velocity model, which GRAs require, and thus has great potentials in seismic hazard assessments.
In this investigation, we examine the uncertainties using the horizontal-to-vertical spectral ratio (HVSR) technique on earthquake recordings to detect site resonant frequencies at 207 KiK-net sites. Our results show that the scenario dependence of response (pseudospectral acceleration) spectral ratio could bias the estimates of resonant frequencies for sites having multiple significant peaks with comparable amplitudes. Thus, the Fourier amplitude spectrum (FAS) should be preferred in computing HVSR. For more than 80% of the investigated sites, the first peak (in the frequency domain) on the average HVSR curve over multiple sites coincides with the highest peak. However, for sites with multiple peaks, the highest peak frequency (f(p)) is less susceptible to the selection criteria of significant peaks and the extent of smoothing to spectrum than the first peak frequency (f(0)). Meanwhile, in comparison to the surface-to-borehole spectral ratio, f(0) tends to underestimate the predominant frequency (at which the largest amplification occurs) more than f(p). In addition, in terms of characterizing linear site response, f(p) shows a better overall performance than f(0). Based on these findings, we thus recommend that seismic network operators provide f(p) on the average HVSRFAS curve as a priority, ideally together with the average HVSRFAS curve in site characterization.
We analyze the spatiotemporal evolution of seismicity during a sequence of moderate (an M-w 4.7 foreshock and M-w 5.8 mainshock) earthquakes occurring in September 2019 at the transition between a creeping and a locked segment of the North Anatolian fault in the central Sea of Marmara, northwest Turkey. To investigate in detail the seismicity evolution, we apply a matched-filter technique to continuous waveforms, thus reducing the magnitude threshold for detection. Sequences of foreshocks preceding the two largest events are clearly seen, exhibiting two different behaviors: a long-term activation of the seismicity along the entire fault segment and a short-term concentration around the epicenters of the large events. We suggest a two-scale preparation phase, with aseismic slip preparing the mainshock final rupture a few days before, and a cascade mechanism leading to the nucleation of the mainshock. Thus, our study shows a combination of seismic and aseismic slip during the foreshock sequence changing the strength of the fault, bringing it closer to failure.
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.
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.
Applying conservation of energy to estimate earthquake frequencies from strain rates and stresses
(2020)
Estimating earthquake occurrence rates from the accumulation rate of seismic moment is an established tool of seismic hazard analysis. We propose an alternative, fault-agnostic approach based on the conservation of energy: the Energy-Conserving Seismicity Framework (ENCOS). Working in energy space has the advantage that the radiated energy is a better predictor of the damage potential of earthquake waves than the seismic moment release. In a region, ENCOS balances the stationary power available to cause earthquakes with the long-term seismic energy release represented by the energy-frequency distribution's first moment. Accumulation and release are connected through the average seismic efficiency, by which we mean the fraction of released energy that is converted into seismic waves. Besides measuring earthquakes in energy, ENCOS differs from moment balance essentially in that the energy accumulation rate depends on the total stress in addition to the strain rate tensor. To validate ENCOS, we exemplarily model the energy-frequency distribution around Southern California. We estimate the energy accumulation rate due to tectonic loading assuming poroelasticity and hydrostasis. Using data from the World Stress Map and assuming the frictional limit to estimate the stress tensor, we obtain a power of 0.8 GW. The uncertainty range, 0.3-2.0GW, originates mainly from the thickness of the seismogenic crust, the friction coefficient on preexisting faults, and models of Global Positioning System (GPS) derived strain rates. Based on a Gutenberg-Richter magnitude-frequency distribution, this power can be distributed over a range of energies consistent with historical earthquake rates and reasonable bounds on the seismic efficiency.
The task of downloading comprehensive datasets of event-based seismic waveforms has been made easier through the development of standardized webservices but is still highly nontrivial because the likelihood of temporary network failures or subtle data errors naturally increases when the amount of requested data is in the order of millions of relatively short segments. This is even more challenging because the typical workflow is not restricted to a single massive download but consists of fetching all possible available input data (e.g., with several repeated download executions) for a processing stage producing any desired user-defined output. Here, we present stream2segment, a highly customizable Python 2+3 package helping the user in the entire workflow of downloading, inspecting, and processing event-based seismic data by means of a relational database management system as archiving storage, which has clear performance and usability advantages, and an integrated processing subroutine requiring a configuration file and a single Python function to produce user-defined output. Stream2segment can also produce diagnostic maps or user-defined plots, which, unlike existing tools, do not require external software dependencies and are not static images but instead are interactive browser-based applications ideally suited for data inspection or annotation tasks and subsequent training of classifiers in foreseen supervised machine-learning applications. Stream2segment has already been used as a data quality tool for datasets within the European Integrated Data Archive and to create a weak-motion database (in the form of a so-called flat file) for the stable continental region of Europe in the context of the European Ground Shaking Intensity Model service, in turn an important building block for seismic hazard studies.