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Accelerometric data from the well-studied valley EUROSEISTEST are used to investigate ground motion uncertainty and variability. We define a simple local ground motion prediction equation (GMPE) and investigate changes in standard deviation (σ) and its components, the between-event variability (τ) and within-event variability (φ). Improving seismological metadata significantly reduces τ (30–50%), which in turn reduces the total σ. Improving site information reduces the systematic site-to-site variability, φ S2S (20–30%), in turn reducing φ, and ultimately, σ. Our values of standard deviations are lower than global values from literature, and closer to path-specific than site-specific values. However, our data have insufficient azimuthal coverage for single-path analysis. Certain stations have higher ground-motion variability, possibly due to topography, basin edge or downgoing wave effects. Sensitivity checks show that 3 recordings per event is a sufficient data selection criterion, however, one of the dataset’s advantages is the large number of recordings per station (9–90) that yields good site term estimates. We examine uncertainty components binning our data with magnitude from 0.01 to 2 s; at smaller magnitudes, τ decreases and φ SS increases, possibly due to κ and source-site trade-offs Finally, we investigate the alternative approach of computing φ SS using existing GMPEs instead of creating an ad hoc local GMPE. This is important where data are insufficient to create one, or when site-specific PSHA is performed. We show that global GMPEs may still capture φ SS , provided that: (1) the magnitude scaling errors are accommodated by the event terms; (2) there are no distance scaling errors (use of a regionally applicable model). Site terms (φ S2S ) computed by different global GMPEs (using different site-proxies) vary significantly, especially for hard-rock sites. This indicates that GMPEs may be poorly constrained where they are sometimes most needed, i.e., for hard rock.
Logic trees are widely used in probabilistic seismic hazard analysis as a tool to capture the epistemic uncertainty associated with the seismogenic sources and the ground-motion prediction models used in estimating the hazard. Combining two or more ground-motion relations within a logic tree will generally require several conversions to be made, because there are several definitions available for both the predicted ground-motion parameters and the explanatory parameters within the predictive ground-motion relations. Procedures for making conversions for each of these factors are presented, using a suite of predictive equations in current use for illustration. The sensitivity of the resulting ground-motion models to these conversions is shown to be pronounced for some of the parameters, especially the measure of source-to-site distance, highlighting the need to take into account any incompatibilities among the selected equations. Procedures are also presented for assigning weights to the branches in the ground-motion section of the logic tree in a transparent fashion, considering both intrinsic merits of the individual equations and their degree of applicability to the particular application
Volcanic tremor extraction and earthquake detection using music information retrieval algorithms
(2021)
Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contrib-ute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic-percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect tran-sient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time-frequency domain, we decompose the signal into two separate spec-trograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contribut-ing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of tran-sient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectro-gram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014-2015 eruption in Iceland with the bulletin presented in Agustsdottir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.
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.
With increasing amount of strong motion data, Ground Motion Prediction Equation (GMPE) developers are able to quantify empirical site amplification functions (delta S2S(s)) from GMPE residuals, for use in site-specific Probabilistic Seismic Hazard Assessment. In this study, we first derive a GMPE for 5% damped Pseudo Spectral Acceleration (g) of Active Shallow Crustal earthquakes in Japan with 3.4 <= M-w <= 7.3 and 0 <= R-JB <= 600km. Using k-mean spectral clustering technique, we then classify our estimated delta S2S(s)(T = 0.01 - 2s) of 588 wellcharacterized sites, into 8 site clusters with distinct mean site amplification functions, and within-cluster site-tosite variability similar to 50% smaller than the overall dataset variability (phi(S2S)). Following an evaluation of existing schemes, we propose a revised data-driven site classification characterized by kernel density distributions of V-s30, V-s10, H-800, and predominant period (T-G) of the site clusters.
The selection of earthquake focal mechanisms (FMs) for stress tensor inversion (STI) is commonly done on a spatial basis, that is, hypocentres. However, this selection approach may include data that are undesired, for example, by mixing events that are caused by different stress tensors when for the STI a single stress tensor is assumed. Due to the significant increase of FM data in the past decades, objective data-driven data selection is feasible, allowing more refined FM catalogues that avoid these issues and provide data weights for the STI routines. We present the application of angular classification with expectation-maximization (ACE) as a tool for data selection. ACE identifies clusters of FM without a priori information. The identified clusters can be used for the classification of the style-of-faulting and as weights of the FM data. We demonstrate that ACE effectively selects data that can be associated with a single stress tensor. Two application examples are given for weighted STI from South America. We use the resulting clusters and weights as a priori information for an STI for these regions and show that uncertainties of the stress tensor estimates are reduced significantly.
Detections of pP and sP phase arrivals (the so-called depth phases) at teleseismic distance provide one of the best ways to estimate earthquake focal depth, as the P-pP and the P-sP delays are strongly dependent on the depth. Based on a new processing workflow and using a single seismic array at teleseismic distance, we can estimate the depth of clusters of small events down to magnitude M-b 3.5. Our method provides a direct view of the relative variations of the seismicity depth from an active area. This study focuses on the application of this new methodology to study the lateral variations of the Guerrero subduction zone (Mexico) using the Eielson seismic array in Alaska (USA). After denoising the signals, 1232 M-b 3.5 + events were detected, with clear P, pP, sP and PcP arrivals. A high-resolution view of the lateral variations of the depth of the seismicity of the Guerero-Oaxaca area is thus obtained. The seismicity is shown to be mainly clustered along the interface, coherently following the geometry of the plate as constrained by the receiver-function analysis along the Meso America Subduction Experiment profile. From this study, the hypothesis of tears on the western part of Guerrero and the eastern part of Oaxaca are strongly confirmed by dramatic lateral changes in the depth of the earthquake clusters. The presence of these two tears might explain the observed lateral variations in seismicity, which is correlated with the boundaries of the slow slip events.
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.
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 Seismic Hazard Harmonization in Europe (SHARE) project, which began in June 2009, aims at establishing new standards for probabilistic seismic hazard assessment in the Euro-Mediterranean region. In this context, a logic tree for ground-motion prediction in Europe has been constructed. Ground-motion prediction equations (GMPEs) and weights have been determined so that the logic tree captures epistemic uncertainty in ground-motion prediction for six different tectonic regimes in Europe. Here we present the strategy that we adopted to build such a logic tree. This strategy has the particularity of combining two complementary and independent approaches: expert judgment and data testing. A set of six experts was asked to weight pre-selected GMPEs while the ability of these GMPEs to predict available data was evaluated with the method of Scherbaum et al. (Bull Seismol Soc Am 99:3234-3247, 2009). Results of both approaches were taken into account to commonly select the smallest set of GMPEs to capture the uncertainty in ground-motion prediction in Europe. For stable continental regions, two models, both from eastern North America, have been selected for shields, and three GMPEs from active shallow crustal regions have been added for continental crust. For subduction zones, four models, all non-European, have been chosen. Finally, for active shallow crustal regions, we selected four models, each of them from a different host region but only two of them were kept for long periods. In most cases, a common agreement has been also reached for the weights. In case of divergence, a sensitivity analysis of the weights on the seismic hazard has been conducted, showing that once the GMPEs have been selected, the associated set of weights has a smaller influence on the hazard.
The propagation of a seismic rupture on a fault introduces spatial variations in the seismic wave field surrounding the fault. This directivity effect results in larger shaking amplitudes in the rupture propagation direction. Its seismic radiation pattern also causes amplitude variations between the strike-normal and strike-parallel components of horizontal ground motion. We investigated the landslide response to these effects during the 2016 Kumamoto earthquake (M-w 7.1) in central Kyushu (Japan). Although the distribution of some 1500 earthquake-triggered landslides as a function of rupture distance is consistent with the observed Arias intensity, the landslides were more concentrated to the northeast of the southwest-northeast striking rupture. We examined several landslide susceptibility factors: hillslope inclination, the median amplification factor (MAF) of ground shaking, lithology, land cover, and topographic wetness. None of these factors sufficiently explains the landslide distribution or orientation (aspect), although the landslide head scarps have an elevated hillslope inclination and MAF. We propose a new physics-based ground-motion model (GMM) that accounts for the seismic rupture effects, and we demonstrate that the low-frequency seismic radiation pattern is consistent with the overall landslide distribution. Its spatial pattern is influenced by the rupture directivity effect, whereas landslide aspect is influenced by amplitude variations between the fault-normal and fault-parallel motion at frequencies < 2 Hz. This azimuth dependence implies that comparable landslide concentrations can occur at different distances from the rupture. This quantitative link between the prevalent landslide aspect and the low-frequency seismic radiation pattern can improve coseismic landslide hazard assessment.
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.
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.
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.
This article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan.
Seismic-hazard assessment is of great importance within the field of engineering seismology. Nowadays, it is common practice to define future seismic demands using probabilistic seismic-hazard analysis (PSHA). Often it is neither obvious nor transparent how PSHA responds to changes in its inputs. In addition, PSHA relies on many uncertain inputs. Sensitivity analysis (SA) is concerned with the assessment and quantification of how changes in the model inputs affect the model response and how input uncertainties influence the distribution of the model response. Sensitivity studies are challenging primarily for computational reasons; hence, the development of efficient methods is of major importance. Powerful local (deterministic) methods widely used in other fields can make SA feasible, even for complex models with a large number of inputs; for example, automatic/algorithmic differentiation (AD)-based adjoint methods. Recently developed derivative-based global sensitivity measures can combine the advantages of such local SA methods with efficient sampling strategies facilitating quantitative global sensitivity analysis (GSA) for complex models. In our study, we propose and implement exactly this combination. It allows an upper bounding of the sensitivities involved in PSHA globally and, therefore, an identification of the noninfluential and the most important uncertain inputs. To the best of our knowledge, it is the first time that derivative-based GSA measures are combined with AD in practice. In addition, we show that first-order uncertainty propagation using the delta method can give satisfactory approximations of global sensitivity measures and allow a rough characterization of the model output distribution in the case of PSHA. An illustrative example is shown for the suggested derivative-based GSA of a PSHA that uses stochastic ground-motion simulations.
In the Next Generation Attenuation West2 (NGA-West2) project, a 3D subsurface structure model (Japan Seismic Hazard Information Station [J-SHIS]) was queried to establish depths to 1.0 and 2.5 km/s velocity isosurfaces for sites without depth measurement in Japan. In this article, we evaluate the depth parameters in the J-SHIS velocity model by comparing them with their corresponding site-specific depth measurements derived from selected KiK-net velocity profiles. The comparison indicates that the J-SHIS model underestimates site depths at shallow sites and overestimates depths at deep sites. Similar issues were also identified in the southern California basin model. Our results also show that these underestimations and over-estimations have a potentially significant impact on ground-motion prediction using NGA-West2 ground-motion models (GMMs). Site resonant period may be considered as an alternative to depth parameter in the site term of a GMM.
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.
The one-dimensional (1-D) approach is still the dominant method to incorporate site effects in engineering applications. To bridge the 1-D to multidimensional site response analysis, we develop quantitative criteria and a reproducible method to identify KiK-net sites with significant deviations from 1-D behavior. We found that 158 out of 354 show two-dimensional (2-D) and three-dimensional (3-D) effects, extending the resonance toward shorter periods at which 2-D or 3-D site effects exceed those of the classic 1-D configurations and imposing an additional amplification to that caused by the impedance contrast alone. Such 2-D and 3-D effects go along with a large within-station ground motion variability. Remarkably, these effects are found to be more pronounced for small impedance contrasts. While it is hardly possible to identify common features in ground motion behavior for stations with similar topography typologies, it is not over-conservative to apply a safety factor to account for 2-D and 3-D site effects in ground motion modeling.
In this study, we analyzed 10 yrs of seismicity in central Italy from 2008 to 2017, a period witnessing more than 1400 earthquakes in the magnitude range 2.5≤Mw≤6.5. The data set includes the main sequences that have occurred in the area, including those associated with the 2009 Mw 6.3 L'Aquila earthquake and the 2016–2017 sequence (Mw 6.2 Amatrice, Mw 6.1 Visso, and Mw 6.5 Norcia earthquakes). We calibrated a local magnitude scale, investigating the impact of changing the reference distance at which the nonparametric attenuation is tied to the zero‐magnitude attenuation function for southern California. We also developed an attenuation model to compute the radiated seismic energy (Es) from the time integral of the squared ground‐motion velocity. Seismic moment (M0) and stress drop (Δσ) were estimated for each earthquake by fitting a ω‐square model to the source spectra obtained by applying a nonparametric spectral inversion. The Δσ‐values vary over three orders of magnitude from about 0.1 to 10 MPa, the larger values associated with the mainshocks. The Δσ‐values describe a lognormal distribution with mean and standard deviation equal to log(Δσ)=(−0.25±0.45) (i.e., the mean Δσ is 0.57 MPa, with a 95% confidence interval from 0.08 to 4.79 MPa). The Δσ variability introduces a spread in the distribution of seismic energy versus moment, with differences in energy up two orders of magnitudes for earthquakes with the same moment. The variability in the high‐frequency spectral levels is captured by the local magnitude (ML), which scales with radiated energy as ML=(−1.59+0.52logEs) for logEs≤10.26 and ML=(−1.38+0.50logEs) otherwise. As the peak ground velocity increases with increasing Δσ, local and energy magnitudes perform better than moment magnitude as predictors for the shaking potential. The availability of different magnitude scales and source parameters for a large earthquake population will help characterize the between‐event ground‐motion variability in central Italy.
We present the results of a consistency check performed over the flatfile extracted from the engineering strong motion (ESM) database. The flatfile includes 23,014 recordings from 2179 earthquakes in the magnitude range from 3.5 to 7.8 that occurred since the 1970s in Europe and Middle East, as presented in the companion article by Lanzano et al. (Bull Earthq Eng, 2018a). The consistency check is developed by analyzing different residual distributions obtained from ad-hoc ground motion prediction equations for the absolute spectral acceleration (SA), displacement and Fourier amplitude spectra (FAS). Only recordings from earthquakes shallower than 40 km are considered in the analysis. The between-event, between-station and event-and-station corrected residuals are computed by applying a mixed-effect regression. We identified those earthquakes, stations, and recordings showing the largest deviations from the GMPE median predictions, and also evaluated the statistical uncertainty on the median model to get insights on the applicable magnitude–distance ranges and the usable period (or frequency) range. We observed that robust median predictions are obtained up to 8 s for SA and up to 20 Hz for FAS, although median predictions for Mw ≥ 7 show significantly larger uncertainties with ‘bumps’ starting above 5 s for SA and below 0.3 Hz for FAS. The between-station variance dominates over the other residual variances, and the dependence of the between-station residuals on logarithm of Vs30 is well-described by a piece-wise linear function with period-dependent slopes and hinge velocity around 580 m/s. Finally, we compared the between-event residuals obtained by considering two different sources of moment magnitude. The results show that, at long periods, the between-event terms from the two regressions have a weak correlation and the overall between-event variability is dissimilar, highlighting the importance of magnitude source in the regression results.
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 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 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.
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.
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.
The basic seismic load parameters for the upcoming national design regulation for DIN EN 1998-1/NA result from the reassessment of the seismic hazard supported by the German Institution for Civil Engineering (DIBt). This 2016 version of the national seismic hazard assessment for Germany is based on a comprehensive involvement of all accessible uncertainties in models and parameters and includes the provision of a rational framework for integrating ranges of epistemic uncertainties and aleatory variabilities in a comprehensive and transparent way. The developed seismic hazard model incorporates significant improvements over previous versions. It is based on updated and extended databases, it includes robust methods to evolve sets of models representing epistemic uncertainties, and a selection of the latest generation of ground motion prediction equations. The new earthquake model is presented here, which consists of a logic tree with 4040 end branches and essential innovations employed for a realistic approach. The output specifications were designed according to the user oriented needs as suggested by two review teams supervising the entire project. Seismic load parameters, for rock conditions of nu(S30) = 800 m/s, are calculated for three hazard levels (10, 5 and 2% probability of occurrence or exceedance within 50 years) and delivered in the form of uniform hazard spectra, within the spectral period range 0.02-3 s, and seismic hazard maps for peak ground acceleration, spectral response accelerations and for macroseismic intensities. Results are supplied as the mean, the median and the 84th percentile. A broad analysis of resulting uncertainties of calculated seismic load parameters is included. The stability of the hazard maps with respect to previous versions and the cross-border comparison is emphasized.
One paragraph of the manuscript of the paper has been inadvertently omitted in the very final stage of its compilation due to a technical mistake. Since this paragraph discusses the declustering of the used earthquake catalogue and is therefore necessary for the understanding of the seismicity data preprocessing, the authors decided to provide this paragraph in form of a correction. The respective paragraph belongs to chapter 2 of the paper, where it was placed originally, and should be inserted into the published paper before the second to the last paragraph. The omitted text reads as follows:
The Seismic Hazard Inferred from Tectonics based on the Global Strain Rate Map (SHIFT_GSRM) earthquake forecast was designed to provide high-resolution estimates of global shallow seismicity to be used in seismic hazard assessment. This model combines geodetic strain rates with global earthquake parameters to characterize long-term rates of seismic moment and earthquake activity. Although SHIFT_GSRM properly computes seismicity rates in seismically active continental regions, it underestimates earthquake rates in subduction zones by an average factor of approximately 3. We present a complementary method to SHIFT_GSRM to more accurately forecast earthquake rates in 37 subduction segments, based on the conservation of moment principle and the use of regional interface seismicity parameters, such as subduction dip angles, corner magnitudes, and coupled seismogenic thicknesses. In seven progressive steps, we find that SHIFT_GSRM earthquake-rate underpredictions are mainly due to the utilization of a global probability function of seismic moment release that poorly captures the great variability among subduction megathrust interfaces. Retrospective test results show that the forecast is consistent with the observations during the 1 January 1977 to 31 December 2014 period. Moreover, successful pseudoprospective evaluations for the 1 January 2015 to 31 December 2018 period demonstrate the power of the regionalized earthquake model to properly estimate subduction-zone seismicity.
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.
Ground-motion prediction equations (GMPE) are essential in probabilistic seismic hazard studies for estimating the ground motions generated by the seismic sources. In low-seismicity regions, only weak motions are available during the lifetime of accelerometric networks, and the equations selected for the probabilistic studies are usually models established from foreign data. Although most GMPEs have been developed for magnitudes 5 and above, the minimum magnitude often used in probabilistic studies in low-seismicity regions is smaller. Disaggregations have shown that, at return periods of engineering interest, magnitudes less than 5 may be contributing to the hazard. This paper presents the testing of several GMPEs selected in current international and national probabilistic projects against weak motions recorded in France (191 recordings with source-site distances up to 300 km, 3:8 <= M-w <= 4:5). The method is based on the log-likelihood value proposed by Scherbaum et al. (2009). The best-fitting models (approximately 2:5 <= LLH <= 3:5) over the whole frequency range are the Cauzzi and Faccioli (2008), Akkar and Bommer (2010), and Abrahamson and Silva (2008) models. No significant regional variation of ground motions is highlighted, and the magnitude scaling could be the predominant factor in the control of ground-motion amplitudes. Furthermore, we take advantage of a rich Japanese dataset to run tests on randomly selected low-magnitude subsets, and confirm that a dataset of similar to 190 observations, the same size as the French dataset, is large enough to obtain stable LLH estimates. Additionally we perform the tests against larger magnitudes (5-7) from the Japanese dataset. The ranking of models is partially modified, indicating a magnitude scaling effect for some of the models, and showing that extrapolating testing results obtained from low-magnitude ranges to higher magnitude ranges is not straightforward.
This study aims to identify the best-performing site characterization proxy alternative and complementary to the conventional 30 m average shear-wave velocity V-S30, as well as the optimal combination of proxies in characterizing linear site response. Investigated proxies include T-0 (site fundamental period obtained from earthquake horizontal-to-vertical spectral ratios), V-Sz (measured average shear-wave velocities to depth z, z = 5, 10, 20 and 30 m), Z(0.8) and Z(1.0) (measured site depths to layers having shear-wave velocity 0.8 and 1.0 km/s, respectively), as well as Z(x-infer) (inferred site depths from a regional velocity model, x = 0.8 and 1.0, 1.5 and 2.5 km/s). To evaluate the performance of a site proxy or a combination, a total of 1840 surface-borehole recordings is selected from KiK-net database. Site amplifications are derived using surface-to-borehole response-, Fourier- and cross-spectral ratio techniques and then are compared across approaches. Next, the efficacies of 7 single-proxies and 11 proxy-pairs are quantified based on the site-to-site standard deviation of amplification residuals of observation about prediction using the proxy or the pair. Our results show that T-0 is the best-performing single-proxy among T-0, Z(0.8), Z(1.0) and V-Sz. Meanwhile, T-0 is also the best-performing proxy among T-0, Z(0.8), Z(1.0) and Z(x-infer) complementary to V-S30 in accounting for the residual amplification after V-S30-correction. Besides, T-0 alone can capture most of the site effects and should be utilized as the primary site indicator. Though (T-0, V-S30) is the best-performing proxy pair among (V-S30, T-0), (V-S30, Z(0.8)), (V-S30, Z(1.0)), (V-S30, Z(x-infer)) and (T-0, V-Sz), it is only slightly better than (T-0, V-S20). Considering both efficacy and engineering utility, the combination of T-0 (primary) and V-S20 (secondary) is recommended. Further study is needed to test the performances of various proxies on sites in deep sedimentary basins.
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.
The aim of this paper is to investigate the ability of various site-condition proxies (SCPs) to reduce ground-motion aleatory variability and evaluate how SCPs capture nonlinearity site effects. The SCPs used here are time-averaged shear-wave velocity in the top 30 m (V-S30), the topographical slope (slope), the fundamental resonance frequency (f(0)) and the depth beyond which V-s exceeds 800 m/s (H800). We considered first the performance of each SCP taken alone and then the combined performance of the 6 SCP pairs [V-S30-f(0)], [V-S30-H-800], [f(0)-slope], [H-800-slope], [V-S30-slope] and [f(0)-H-800]. This analysis is performed using a neural network approach including a random effect applied on a KiK-net subset for derivation of ground-motion prediction equations setting the relationship between various ground-motion parameters such as peak ground acceleration, peak ground velocity and pseudo-spectral acceleration PSA (T), and Mw, RJB, focal depth and SCPs. While the choice of SCP is found to have almost no impact on the median groundmotion prediction, it does impact the level of aleatory uncertainty. VS30 is found to perform the best of single proxies at short periods (T < 0.6 s), while f(0) and H-800 perform better at longer periods; considering SCP pairs leads to significant improvements, with particular emphasis on [V-S30-H-800] and [f(0)-slope] pairs. The results also indicate significant nonlinearity on the site terms for soft sites and that the most relevant loading parameter for characterising nonlinear site response is the "stiff" spectral ordinate at the considered period.
The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.
On April 29, 2017 at 0:56 UTC (2:56 local time), an M (W) = 2.8 earthquake struck the metropolitan area between Leipzig and Halle, Germany, near the small town of Markranstadt. The earthquake was felt within 50 km from the epicenter and reached a local intensity of I (0) = IV. Already in 2015 and only 15 km northwest of the epicenter, a M (W) = 3.2 earthquake struck the area with a similar large felt radius and I (0) = IV. More than 1.1 million people live in the region, and the unusual occurrence of the two earthquakes led to public attention, because the tectonic activity is unclear and induced earthquakes have occurred in neighboring regions. Historical earthquakes south of Leipzig had estimated magnitudes up to M (W) ae 5 and coincide with NW-SE striking crustal basement faults. We use different seismological methods to analyze the two recent earthquakes and discuss them in the context of the known tectonic structures and historical seismicity. Novel stochastic full waveform simulation and inversion approaches are adapted for the application to weak, local earthquakes, to analyze mechanisms and ground motions and their relation to observed intensities. We find NW-SE striking normal faulting mechanisms for both earthquakes and centroid depths of 26 and 29 km. The earthquakes are located where faults with large vertical offsets of several hundred meters and Hercynian strike have developed since the Mesozoic. We use a stochastic full waveform simulation to explain the local peak ground velocities and calibrate the method to simulate intensities. Since the area is densely populated and has sensitive infrastructure, we simulate scenarios assuming that a 12-km long fault segment between the two recent earthquakes is ruptured and study the impact of rupture parameters on ground motions and expected damage.
We study the rupture processes of Iquique earthquake M-w 8.1 (2014/04/01) and its largest aftershock M-w 7.7 (2014/04/03) that ruptured the North Chile subduction zone. High-rate Global Positioning System (GPS) recordings and strong motion data are used to reconstruct the evolution of the slip amplitude, rise time and rupture time of both earthquakes. A two-step inversion scheme is assumed, by first building prior models for both earthquakes from the inversion of the estimated static displacements and then, kinematic inversions in the frequency domain are carried out taken into account this prior information. The preferred model for the mainshock exhibits a seismic moment of 1.73 x 10(21) Nm (M-w 8.1) and maximum slip of similar to 9 m, while the aftershock model has a seismic moment of 3.88 x 10(20) (M-w 7.7) and a maximum slip of similar to 3 m. For both earthquakes, the final slip distributions show two asperities (a shallow one and a deep one) separated by an area with significant slip deficit. This suggests a segmentation along-dip which might be related to a change of the dipping angle of the subducting slab inferred from gravimetric data. Along-strike, the areas where the seismic ruptures stopped seem to be well correlated with geological features observed from geophysical information (high-resolution bathymetry, gravimetry and coupling maps) that are representative of the long-term segmentation of the subduction margin. Considering the spatially limited portions that were broken by these two earthquakes, our results support the idea that the seismic gap is not filled yet. (C) 2018 Elsevier B.V. All rights reserved.
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.
A transparent and data-driven global tectonic regionalization model for seismic hazard assessment
(2018)
A key concept that is common to many assumptions inherent within seismic hazard assessment is that of tectonic similarity. This recognizes that certain regions of the globe may display similar geophysical characteristics, such as in the attenuation of seismic waves, the magnitude scaling properties of seismogenic sources or the seismic coupling of the lithosphere. Previous attempts at tectonic regionalization, particularly within a seismic hazard assessment context, have often been based on expert judgements; in most of these cases, the process for delineating tectonic regions is neither reproducible nor consistent from location to location. In this work, the regionalization process is implemented in a scheme that is reproducible, comprehensible from a geophysical rationale, and revisable when new relevant data are published. A spatial classification-scheme is developed based on fuzzy logic, enabling the quantification of concepts that are approximate rather than precise. Using the proposed methodology, we obtain a transparent and data-driven global tectonic regionalization model for seismic hazard applications as well as the subjective probabilities (e.g. degree of being active/degree of being cratonic) that indicate the degree to which a site belongs in a tectonic category.
Python is at the forefront of scientific computation for seismologists and therefore should be introduced to students interested in becoming seismologists. On its own, Python is open source and well designed with extensive libraries. However, Python code can also be executed, visualized, and communicated to others with "Jupyter Notebooks". Thus, Jupyter Notebooks are ideal for teaching students Python and scientific computation. In this article, we designed an openly available Python library and collection of Jupyter Notebooks based on defined scientific computation learning goals for seismology students. The Notebooks cover topics from an introduction to Python to organizing data, earthquake catalog statistics, linear regression, and making maps. Our Python library and collection of Jupyter Notebooks are meant to be used as course materials for an upper-division data analysis course in an Earth Science Department, and the materials were tested in a Probabilistic Seismic Hazard course. However, seismologists or anyone else who is interested in Python for data analysis and map making can use these materials.
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.
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.
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.
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
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
The PEGASOS project was a major international seismic hazard study, one of the largest ever conducted anywhere in the world, to assess seismic hazard at four nuclear power plant sites in Switzerland. Before the report of this project has become publicly available, a paper attacking both methodology and results has appeared. Since the general scientific readership may have difficulty in assessing this attack in the absence of the report being attacked, we supply a response in the present paper. The bulk of the attack, besides some misconceived arguments about the role of uncertainties in seismic hazard analysis, is carried by some exercises that purport to be validation exercises. In practice, they are no such thing; they are merely independent sets of hazard calculations based on varying assumptions and procedures, often rather questionable, which come up with various different answers which have no particular significance. (C) 2005 Elsevier B.V. All rights reserved
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
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
We compare the ability of various site-condition proxies (SCPs) to reduce the aleatory variability of ground motion prediction equations (GMPEs). Three SCPs (measured V-S30, inferred V-S30, local topographic slope) and two accelerometric databases (RESORCE and NGA-West2) are considered. An artificial neural network (ANN) approach including a random-effect procedure is used to derive GMPEs setting the relationship between peak ground acceleration (PGA), peak ground velocity (PGV), pseudo-spectral acceleration [PSA(T)], and explanatory variables (M-w, R-JB, and V-S30 or Slope). The analysis is performed using both discrete site classes and continuous proxy values. All "non-measured" SCPs exhibit a rather poor performance in reducing aleatory variability, compared to the better performance of measured V-S30. A new, fully data-driven GMPE based on the NGA-West2 is then derived, with an aleatory variability value depending on the quality of the SCP. It proves very consistent with previous GMPEs built on the same data set. Measuring V-S30 allows for benefit from an aleatory variability reduction up to 15%.
We derive a set of regional ground-motion prediction equations (GMPEs) in the Fourier amplitude spectra (FAS-GMPE) and in the spectral acceleration (SA-GMPE) domains for the purpose of interpreting the between-event residuals in terms of source parameter variability. We analyze a dataset of about 65,000 recordings generated by 1400 earthquakes (moment magnitude 2: 5 <= M-w <= 6: 5, hypocentral distance R-hypo <= 150 km) that occurred in central Italy between January 2008 and October 2017. In a companion article (Bindi, Spallarossa, et al., 2018), the nonparametric acceleration source spectra were interpreted in terms of omega-square models modified to account for deviations from a high-frequency flat plateau through a parameter named k(source). Here, the GMPEs are derived considering the moment (M-w), the local (M-L), and the energy (M-e) magnitude scales, and the between-event residuals are computed as random effects. We show that the between-event residuals for the FAS-GMPE implementing M-w are correlated with stress drop, with correlation coefficients increasing with increasing frequency up to about 10 Hz. Contrariwise, the correlation is weak for the FAS-GMPEs implementing M-L and M-e, in particular between 2 and 5 Hz, where most of the corner frequencies lie. At higher frequencies, all models show a strong correlation with k(source). The correlation with the source parameters reflects in a different behavior of the standard deviation tau of the between-event residuals with frequency. Although tau is smaller for the FAS-GMPE using M-w below 1.5 Hz, at higher frequencies, the model implementing either M-L or M-e shows smaller values, with a reduction of about 30% at 3 Hz (i.e., from 0.3 for M-w to 0.1 for M-L). We conclude that considering magnitude scales informative for the stress-drop variability allows to reduce the between-event variability with a significant impact on the hazard assessment, in particular for studies in which the ergodic assumption on site is removed.
We study the rupture processes of Iquique earthquake 8.1 (2014/04/01) and its largest aftershock 7.7 (2014/04/03) that ruptured the North Chile subduction zone. High-rate Global Positioning System (GPS) recordings and strong motion data are used to reconstruct the evolution of the slip amplitude, rise time and rupture time of both earthquakes. A two-step inversion scheme is assumed, by first building prior models for both earthquakes from the inversion of the estimated static displacements and then, kinematic inversions in the frequency domain are carried out taken into account this prior information. The preferred model for the mainshock exhibits a seismic moment of 1.73 × 1021 Nm ( 8.1) and maximum slip of ∼9 m, while the aftershock model has a seismic moment of 3.88 × 1020 ( 7.7) and a maximum slip of ∼3 m. For both earthquakes, the final slip distributions show two asperities (a shallow one and a deep one) separated by an area with significant slip deficit. This suggests a segmentation along-dip which might be related to a change of the dipping angle of the subducting slab inferred from gravimetric data. Along-strike, the areas where the seismic ruptures stopped seem to be well correlated with geological features observed from geophysical information (high-resolution bathymetry, gravimetry and coupling maps) that are representative of the long-term segmentation of the subduction margin. Considering the spatially limited portions that were broken by these two earthquakes, our results support the idea that the seismic gap is not filled yet.
Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical to predict spatially accurate distribution of shear-wave amplitudes. Although decades’ old theory proposes a deterministic, highly anisotropic, four-lobed shear-wave radiation pattern, from lack of convincing evidence, most empirical ground-shaking prediction models settled for an oversimplified stochastic radiation pattern that is isotropic on average. Today, using the large datasets of uniformly processed seismograms from several strike, normal, reverse, and oblique-slip earthquakes across the globe, compiled specifically for engineering applications, we could reveal, quantify, and calibrate the frequency-, distance-, and style-of-faulting dependent transition of shear-wave radiation between a stochastic-isotropic and a deterministic-anisotropic phenomenon. Consequent recalibration of empirical ground-shaking models dramatically improved their predictions: with isodistant anisotropic variations of ±40%, and 8% reduction in uncertainty. The outcomes presented here can potentially trigger a reappraisal of several practical issues in engineering seismology, particularly in seismic ground-shaking studies and seismic hazard and risk assessment.
Adjustment of median ground motion prediction equations (GMPEs) from one region to another region is one of the major challenges within the current practice of seismic hazard analysis. In our approach of generating response spectra, we derive two separate empirical models for a) Fourier amplitude spectrum (FAS) and b) duration of ground motion. To calculate response spectra, the two models are combined within the random vibration theory (RVT) framework. The models are calibrated on recordings obtained from shallow crustal earthquakes in active tectonic regions. We use a subset of NGA-West2 database with M3.2-7.9 earthquakes at distances 0-300 km. The NGA-West2 database expanded over a wide magnitude and distance range facilitates a better constraint over derived models. A frequency-dependent duration model is derived to obtain adjustable response spectral ordinates. Excellent comparison of our approach with other NGA-West2 models implies that it can also be used as a stand-alone model.
Ground‐motion prediction equations (GMPEs) are calibrated to predict the intensity of ground shaking at any given location, based on earthquake magnitude, source‐to‐site distance, local soil amplifications, and other parameters. GMPEs are generally assumed to be independent of time; however, evidence is increasing that large earthquakes modify the shallow soil conditions and those of the fault zone for months or years. These changes may affect the intensity of shaking and result in time‐dependent effects that can potentially be resolved by analyzing between‐event residuals (residuals between observed and predicted ground motion for individual earthquakes averaged over all stations). Here, we analyze a data set of about 65,000 recordings for about 1400 earthquakes in the moment magnitude range 2.5–6.5 that occurred in central Italy from 2008 to 2017 to capture the temporal variability of the ground shaking at high frequency. We first compute between‐event residuals for each earthquake in the Fourier domain with respect to a GMPE developed ad hoc for the analyzed data set. The between‐events show large changes after the occurrence of mainshocks such as the 2009 Mw 6.3 L'Aquila, the 2016 Mw 6.2 Amatrice, and Mw 6.5 Norcia earthquakes. Within the time span of a few months after the mainshocks, the between‐event contribution to the ground shaking varies by a factor 7. In particular, we find a large drop in the between‐events in the aftermath of the L'Aquila earthquake, followed by a slow positive trend that leads to a recovery interrupted by a new drop at the beginning of 2014. We also quantify the frequency‐dependent correlation between the Brune stress drop Δσ and the between‐events. We find that the temporal changes of Δσ resemble those of the between‐event residuals; in particular, during the period when the between‐events show the positive trend, the average logarithm of Δσ increases with an annual rate of 0.19 (i.e., the amplification factor for Δσ is 1.56 per year). Breakpoint analysis located a change in the linear trend coefficients of Δσ versus time in February 2014, although no large earthquakes occurred at that time. Finally, the temporal variability of Δσ mirrors the relative seismic‐velocity variations observed in previous studies for the same area and period, suggesting that both crack healing along the main fault system and healing of microcracks distributed at shallow depths throughout the surrounding region might be necessary to explain the wider observations of postearthquake recovery.
We have analyzed the recently developed pan-European strong motion database, RESORCE-2012: spectral parameters, such as stress drop (stress parameter, Delta sigma), anelastic attenuation (Q), near surface attenuation (kappa(0)) and site amplification have been estimated from observed strong motion recordings. The selected dataset exhibits a bilinear distance-dependent Q model with average kappa(0) value 0.0308 s. Strong regional variations in inelastic attenuation were also observed: frequency-independent Q(0) of 1462 and 601 were estimated for Turkish and Italian data respectively. Due to the strong coupling between Q and kappa(0), the regional variations in Q have strong impact on the estimation of near surface attenuation kappa(0). kappa(0) was estimated as 0.0457 and 0.0261 s for Turkey and Italy respectively. Furthermore, a detailed analysis of the variability in estimated kappa(0) revealed significant within-station variability. The linear site amplification factors were constrained from residual analysis at each station and site-class type. Using the regional Q(0) model and a site-class specific kappa(0), seismic moments (M-0) and source corner frequencies f (c) were estimated from the site corrected empirical Fourier spectra. Delta sigma did not exhibit magnitude dependence. The median Delta sigma value was obtained as 5.75 and 5.65 MPa from inverted and database magnitudes respectively. A comparison of response spectra from the stochastic model (derived herein) with that from (regional) ground motion prediction equations (GMPEs) suggests that the presented seismological parameters can be used to represent the corresponding seismological attributes of the regional GMPEs in a host-to-target adjustment framework. The analysis presented herein can be considered as an update of that undertaken for the previous Euro-Mediterranean strong motion database presented by Edwards and Fah (Geophys J Int 194(2):1190-1202, 2013a).
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 propose an alternative procedure for the capture of the hard‐rock regional kappa (κ0ref). In our approach, we make use of a potential link between the well‐known κ parameter and the properties of coda waves. In our analysis, we consider near‐distance records of four crustal earthquakes of local magnitude 3.7–4.9 that occurred in four regions of France in different geological contexts: the crystalline axial chain of Pyrenees to the southwest, the large sedimentary basin to the southeast, the Alpine range to the east, and the extensional Rhine graben to the northeast. Each earthquake has been recorded at a pair of nearby soft‐ and hard‐rock station sites. The high‐frequency (16–32 Hz) spectral amplitudes of the coda window (carefully selected on the time series of the accelerograms) confirm an exponential decrease, which we quantify by κAHcoda and call “kappa of coda.” It is found that κAHcoda is independent of the soil type but shows significant regional variations. κ measurements (Anderson and Hough, 1984) over the coda window (κAHcoda) and full time series (κAH) show strong correlation at hard‐rock sites. This suggests that κAHcoda can provide a new proxy to estimate the regional hard rock κ0ref (Ktenidou et al., 2015). Theoretical analysis is also presented to relate the regional κAHcoda and coda quality factor Qc, which quantifies the average attenuation properties of the crust (both scattering and absorption). It allows interpreting κAHcoda as the time spent by the waves in the medium, weighted by its attenuation properties. This theoretical analysis also shows that the classical κ measurement should be frequency dependent; this was confirmed by the spectra of the observed records.
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.
A new view of Ecuador's complex geodynamics has been developed in the course of modeling seismic source zones for probabilistic seismic hazard analysis. This study focuses on two aspects of the plates' interaction at a continental scale: (a) age-related differences in rheology between Farallon and Nazca plates—marked by the Grijalva rifted margin and its inland projection—as they subduct underneath central Ecuador, and (b) the rapidly changing convergence obliquity resulting from the convex shape of the South American northwestern continental margin. Both conditions satisfactorily explain several characteristics of the observed seismicity and of the interseismic coupling. Intermediate-depth seismicity reveals a severe flexure in the Farallon slab as it dips and contorts at depth, originating the El Puyo seismic cluster. The two slabs position and geometry below continental Ecuador also correlate with surface expressions observable in the local and regional geology and tectonics. The interseismic coupling is weak and shallow south of the Grijalva rifted margin and increases northward, with a heterogeneous pattern locally associated to the Carnegie ridge subduction. High convergence obliquity is responsible for the North Andean Block northeastward movement along localized fault systems. The Cosanga and Pallatanga fault segments of the North Andean Block-South American boundary concentrate most of the seismic moment release in continental Ecuador. Other inner block faults located along the western border of the inter-Andean Depression also show a high rate of moderate-size earthquake production. Finally, a total of 19 seismic source zones were modeled in accordance with the proposed geodynamic and neotectonic scheme.
The increasing numbers of recordings at individual sites allows quantification of empirical linear site-response adjustment factors (delta S2S(s)) from the ground motion prediction equation (GMPE) residuals. The delta S2S(s) are then used to linearly scale the ergodic GMPE predictions to obtain site-specific ground motion predictions in a partially non-ergodic Probabilistic Seismic Hazard Assessment (PSHA). To address key statistical and conceptual issues in the current practice, we introduce a novel empirical region-and site-specific PSHA methodology wherein, (1) site-to-site variability (phi(S2S)) is first estimated as a random-variance in a mixed-effects GMPE regression, (2) delta S2S(s) at new sites with strong motion are estimated using the a priori phi(S2S), and (3) the GMPE site-specific single-site aleatory variability sigma(ss,s) is replaced with a generic site-corrected aleatory variability sigma(0). Comparison of region- and site-specific hazard curves from our method against the traditional ergodic estimates at 225 sites in Europe and Middle East shows an approximate 50% difference in predicted ground motions over a range of hazard levels-a strong motivation to increase seismological monitoring of critical facilities and enrich regional ground motion data sets.
Site-Corrected Magnitude- and Region-Dependent Correlations of Horizontal Peak Spectral Amplitudes
(2017)
Empirical correlations of horizontal peak spectral amplitudes (PSA) are modeled using the total-residuals obtained in a ground motion prediction equation (GMPE) regression. Recent GMPEs moved toward partially non-ergodic region-and site-specific predictions, while the residual correlation models remained largely ergodic. Using mixed-effects regression, we decompose the total-residuals of a pan-European GMPE into between-event, between-site, and event-and-site corrected residuals to investigate the ergodicity in empirical PSA correlations. We first observed that the between-event correlations are magnitude-dependent, partially due to the differences in source spectra, and influence of stress-drop parameter on small and large events. Next, removing the between-site residuals from within-event residuals yields the event-and-site corrected residuals which are found to be region-dependent, possibly due to the regional differences in distance-decay of short period PSAs. Using our site-corrected magnitude- and region-dependent correlations, and the between-site residuals as empirical site-specific ground motion adjustments, we compute partially non-ergodic conditional mean spectra at four well-recorded sites in Europe and Middle Eastern regions.
The Engineering Strong-Motion (ESM) flatfile is a parametric table which contains verified and reliable metadata and intensity measures of manually processed waveforms included in the ESM database. The flatfile has been developed within the Seismology Thematic Core Service of EPOS-IP (European Plate Observing System Implementation Phase) and it is disseminated throughout a web portal for research and technical purposes. The adopted criteria for flatfile compilation aim to collect strong motion data and related metadata in a uniform, updated, traceable and quality-checked way to develop Ground Motion Models (GMMs) for Probabilistic Seismic Hazard Assessment (PSHA) and engineering applications. In this paper, we present the characteristics of ESM flatfile in terms of recording, event and station distributions, and we discuss the most relevant features of the Intensity Measures (IMs) of engineering interest included in the table. The dataset for flatfile compilation includes 23,014 recordings from 2179 earthquakes and 2080 stations from Europe and Middle-East. The events are characterized by magnitudes in the range 3.5-8.0 and refer to different tectonics regimes, such as shallow active crustal and subduction zones. Intensity measures include peak and integral parameters and duration of each waveform. The spectral amplitudes of the (5% damping) acceleration and displacement response are provided for 36 periods, in the interval 0.01-10 s, as well as the 103 amplitudes of the Fourier spectrum for the frequency range 0.04-50 Hz. Several statistics are shown with reference to the most significant metadata for GMMs calibrations, such as moment magnitude, focal depth, several distance metrics, style of faulting and parameters for site characterization. Furthermore, we also compare and explain the most relevant differences between the metadata of ESM flatfile with those provided by the previous flatfile derived in RESORCE (Reference Database for Seismic Ground Motion in Europe) project.
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.