Are Near-Fault Fling Effects Captured in the New NGA West2 Ground Motion Models?

2015 ◽  
Vol 31 (3) ◽  
pp. 1629-1645 ◽  
Author(s):  
Ronnie Kamai ◽  
Norman Abrahamson
Keyword(s):  
Ground Motion ◽  
Vertical Component ◽  
Strong Motion ◽  
Motion Processing ◽  
Large Set ◽  
Pass Filter ◽  
Motion Models ◽  
Finite Fault ◽  
Maximum Period ◽  
Ground Motion Models

We evaluate how much of the fling effect is removed from the NGA database and accompanying GMPEs due to standard strong motion processing. The analysis uses a large set of finite-fault simulations, processed with four different high-pass filter corners, representing the distribution within the PEER ground motion database. The effects of processing on the average horizontal component, the vertical component, and peak ground motion values are evaluated by taking the ratio between unprocessed and processed values. The results show that PGA, PGV, and other spectral values are not significantly affected by processing, partly thanks to the maximum period constraint used when developing the NGA GMPEs, but that the bias in peak ground displacement should not be ignored.

A Suite of Alternative Ground-Motion Models (GMMs) for Israel

2021 ◽  
Author(s):  
Soumya Kanti Maiti ◽  
Gony Yagoda-Biran ◽  
Ronnie Kamai
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Empirical Data ◽  
Epistemic Uncertainty ◽  
Ground Motions ◽  
Plate Boundary ◽  
Strong Motion ◽  
Engineering Applications ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT Models for estimating earthquake ground motions are a key component in seismic hazard analysis. In data-rich regions, these models are mostly empirical, relying on the ever-increasing ground-motion databases. However, in areas in which strong-motion data are scarce, other approaches for ground-motion estimates are sought, including, but not limited to, the use of simulations to replace empirical data. In Israel, despite a clear seismic hazard posed by the active plate boundary on its eastern border, the instrumental record is sparse and poor, leading to the use of global models for hazard estimation in the building code and all other engineering applications. In this study, we develop a suite of alternative ground-motion models for Israel, based on an empirical database from Israel as well as on four data-calibrated synthetic databases. Two host models are used to constrain model behavior, such that the epistemic uncertainty is captured and characterized. Despite the lack of empirical data at large magnitudes and short distances, constraints based on the host models or on the physical grounds provided by simulations ensure these models are appropriate for engineering applications. The models presented herein are cast in terms of the Fourier amplitude spectra, which is a linear, physical representation of ground motions. The models are suitable for shallow crustal earthquakes; they include an estimate of the median and the aleatory variability, and are applicable in the magnitude range of 3–8 and distance range of 1–300 km.

scholarly journals Seismic hazard evaluation

2000 ◽  
Vol 33 (3) ◽  
pp. 371-386 ◽  
Author(s):  
Paul Somerville
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Earthquake Source ◽  
Hazard Evaluation ◽  
Data Set ◽  
Motion Models ◽  
The Past ◽  
Ground Motion Models

This paper reviews concepts and trends in seismic hazard characterization that have emerged in the past decade, and identifies trends and concepts that are anticipated during the coming decade. New methods have been developed for characterizing potential earthquake sources that use geological and geodetic data in conjunction with historical seismicity data. Scaling relationships among earthquake source parameters have been developed to provide a more detailed representation of the earthquake source for ground motion prediction. Improved empirical ground motion models have been derived from a strong motion data set that has grown markedly over the past decade. However, these empirical models have a large degree of uncertainty because the magnitude - distance - soil category parameterization of these models often oversimplifies reality. This reflects the fact that other conditions that are known to have an important influence on strong ground motions, such as near- fault rupture directivity effects, crustal waveguide effects, and basin response effects, are not treated as parameters of these simple models. Numerical ground motion models based on seismological theory that include these additional effects have been developed and extensively validated against recorded ground motions, and used to estimate the ground motions of past earthquakes and predict the ground motions of future scenario earthquakes. The probabilistic approach to characterizing the ground motion that a given site will experience in the future is very compatible with current trends in earthquake engineering and the development of building codes. Performance based design requires a more comprehensive representation of ground motions than has conventionally been used. Ground motions estimates are needed at multiple annual probability levels, and may need to be specified not only by response spectra but also by suites of strong motion time histories for input into time-domain non-linear analyses of structures.

Residual analysis of large strong-motion flatfiles as a tool for detecting data error and anomalies

2021 ◽  
Author(s):  
Claudia Mascandola ◽  
Giovanni Lanzano ◽  
Francesca Pacor
Keyword(s):  
Ground Motion ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Strong Motion ◽  
Consistency Check ◽  
Mixed Effect ◽  
Motion Models ◽  
Tree Classifier ◽  
The Common ◽  
Ground Motion Models

<p>The rapid increase of seismic waveforms, due to the increment of seismic stations and continuous real-time streaming to data centres, leads to the need for automatic procedures aimed at supporting data processing and data quality control. In this study, we propose a semi-automatic procedure for the consistency check of large strong-motion datasets, classifying the anomalies observed on the residuals analysis and identifying the possible causes.</p><p>The data collected in the strong-motion databases are usually arranged as parametric tables (called flatfiles), used to disseminate the Intensity Measures (IMs) and the associated metadata of the processed waveforms. This is the current practice for the ITalian ACcelerometric Archive (ITACA, D’Amico et al., 2020) and Engineering Strong Motion (ESM; Lanzano et al. 2019a) databases. The adopted criteria for flatfile compilation are designed to collect IMs and related metadata in a uniform, updated, and traceable way, with the aim of providing datasets useful to develop Ground Motion Models (GMMs) for Probabilistic Seismic Hazard Assessment (PSHA) and engineering applications. Therefore, the consistency check of the flatfiles is a crucial task to improve the quality of the products provided by the waveform services.</p><p>The proposed procedure is based on the residual distributions obtained from ad-hoc ground motion prediction equations for the ordinates of the 5% damped acceleration response spectra. In this study, we focus on the active shallow crust events in ITACA, considering the ITA18 ground motion model (Lanzano et al., 2019b) as a reference for Italy. The total residuals, computed as logarithm difference between observations and predictions, are decomposed in between-event, between-station and event-and-station corrected residuals by applying a mixed-effect regression (Bates et al., 2015). This is the common practice for the (partial) removal of the ergodic assumption in empirical GMMs (e.g., Stafford 2014), where the contribution of the systematic corrective effects of event and station on aleatory variability are identified and shifted to the epistemic uncertainty. Afterward, the proposed procedure is applied to raise a warning in case of anomalous residual values. Warnings are provided when the normalized residuals exceed a certain threshold, in three ranges of periods (i.e., 0.01-0.15 s, 0.15-1 s, 1-5 s). The causes of warnings may be several and may concern the event, the site, the waveform, or a combination of them. Among the possible sources of anomalous trends, the more common are: preliminary or inaccurate event localization or magnitude, wrong soil category assigned based on proxies, misleading tectonic regime assigned to the earthquake, and fault directivity that may cause strong-ground motion amplification in certain directions. Warnings may also raise for peculiarities in the site-response (e.g., large amplifications/de-amplifications at certain frequency-bands) and to the occurrence of near-source effects in the waveforms (see Pacor et al., 2018). Based on the raised warnings, a decision tree classifier is developed to identify the common anomaly sources and to support the consistency check of the semi-automatic procedure.</p><p>This study may help to enhance the waveform services and related products, besides reducing the variability of ground motion models and guiding decisions for site characterization studies and network maintenance.</p>

An Overview of the NGA Project

2008 ◽  
Vol 24 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Maurice Power ◽  
Brian Chiou ◽  
Norman Abrahamson ◽  
Yousef Bozorgnia ◽  
Thomas Shantz ◽  
...  
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Research Program ◽  
Development Process ◽  
Strong Motion ◽  
Engineering Research ◽  
Motion Models ◽  
Ground Motion Attenuation ◽  
Attenuation Models ◽  
Ground Motion Models

The “Next Generation of Ground-Motion Attenuation Models” (NGA) project is a multidisciplinary research program coordinated by the Lifelines Program of the Pacific Earthquake Engineering Research Center (PEER), in partnership with the U.S. Geological Survey and the Southern California Earthquake Center. The objective of the project is to develop new ground-motion prediction relations through a comprehensive and highly interactive research program. Five sets of ground-motion models were developed by teams working independently but interacting with one another throughout the development process. The development of ground-motion models was supported by other project components, which included (1) developing an updated and expanded PEER database of recorded ground motions, including supporting information on the strong-motion record processing, earthquake sources, travel path, and recording station site conditions; (2) conducting supporting research projects to provide guidance on the selected functional forms of the ground-motion models; and (3) conducting a program of interactions throughout the development process to provide input and reviews from both the scientific research and engineering user communities. An overview of the NGA project components, process, and products is presented in this paper.

Ground Motion Prediction Equations for the Vertical Ground Motion Component Based on the NGA-W2 Database

2017 ◽  
Vol 33 (2) ◽  
pp. 499-528 ◽  
Author(s):  
Zeynep Gülerce ◽  
Ronnie Kamai ◽  
Norman A. Abrahamson ◽  
Walter J. Silva
Keyword(s):  
Ground Motion ◽  
Site Response ◽  
Vertical Component ◽  
Weighted Average ◽  
Motion Models ◽  
Nonlinear Site Response ◽  
Vertical Ground Motion ◽  
Vertical Ground ◽  
Standard Deviations ◽  
Ground Motion Models

Empirical ground motion models for the vertical component from shallow crustal earthquakes in active tectonic regions are derived using the PEER NGA-West2 database. The model is applicable to magnitudes 3.0–8.0, distances of 0–300 km, and spectral periods of 0–10 s. The model input parameters are the same as used by Abrahamson et al. (2014) except that the nonlinear site response and depth to bedrock effects are evaluated but found to be insignificant. Regional differences in large distance attenuation and site amplification scaling between California, Japan, China, Taiwan, Italy, and the Middle East are included. Scaling for the hanging-wall effect is incorporated using the constraints from numerical simulations by Donahue and Abrahamson (2014) . The standard deviation is magnitude dependent with smaller magnitudes leading to larger standard deviations at short periods but smaller standard deviations at long periods. The vertical ground motion model developed in this study can be paired with the horizontal component model proposed by Abrahamson et al. (2014) to produce a V/H ratio. For applications where the horizontal spectrum is derived from the weighted average of several horizontal ground motion models, a V/H model derived directly from the V/H data (such as Gülerce and Abrahamson 2011 ) should be preferred.

Ranking and calibration of ground-motion models using the stochastic area metric. 

2021 ◽  
Author(s):  
Jaleena Sunny ◽  
Marco De Angelis ◽  
Ben Edwards
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Strong Motion ◽  
Statistical Testing ◽  
Testing Methods ◽  
Motion Models ◽  
Area Metric ◽  
Scenario Earthquakes ◽  
Ground Motion Variability ◽  
Ground Motion Models

<p>The selection and ranking of  Ground Motion Models (GMMs) for scenario earthquakes is a crucial element in seismic hazard analysis. Typically model testing and ranking do not appropriately account for uncertainties, thus leading to improper ranking. We introduce the stochastic area metric (AM) as a scoring metric for GMMs, which not only informs the analyst of the degree to which observed or test data fit the model but also considers the uncertainties without the assumption of how data are distributed. The AM can be used as a scoring metric or cost function, whose minimum value identifies the model that best fits a given dataset. We apply this metric along with existing testing methods to recent and commonly used European ground motion prediction equations: Bindi et al. (2014, B014), Akkar et al. (2014, A014) and Cauzzi et al. (2015, C015). The GMMs are ranked and their performance analysed against the European Engineering Strong Motion (ESM) dataset. We focus on the ranking of models for ranges of magnitude and distance with sparse data, which pose a specific problem with other statistical testing methods. The performance of models over different ranges of magnitude and distance were analysed using AM, revealing the importance of considering different models for specific applications (e.g., tectonic, induced). We find the A014 model displays good performance with complete dataset while B014 appears to be best for small magnitudes and distances. In addition, we calibrated GMMs derived from a compendium of data and generated a suite of models for the given region through an optimisation technique utilising the concept of AM and ground motion variability. This novel framework for ranking and calibration guides the informed selection of models and helps develop regionally adjusted and application-specific GMMs for better prediction. </p><p> </p>

Conditional Ground-Motion Models for Horizontal Peak Ground Displacement for Active Crustal Regions

2021 ◽  
Author(s):  
Chih-Hsuan Sung ◽  
Norman A. Abrahamson ◽  
Jyun-Yan Huang
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Input Parameter ◽  
Strong Motion ◽  
Tectonic Deformation ◽  
Ground Displacement ◽  
Strong Motion Data ◽  
Motion Data ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT Ground-motion models (GMMs) are developed for peak ground displacement (PGD) and for bandlimited PGD based on strong-motion data that has been filtered as part of standard processing and the total PGD that includes the tectonic deformation as well as the vibratory ground motion. For the bandlimited PGD, we develop conditional ground-motion models (CGMMs) using subsets of the Pacific Earthquake Engineering Research Center Next Generation Attenuation-West2 Project (NGA-W2) database and the National Center for Research on Earthquake Engineering Taiwan Senior Seismic Hazard Analysis Committee level 3 project database. The CGMM approach includes the observed pseudospectral acceleration (PSA(T)) as an input parameter in addition to magnitude and distance. The period of the PSA(T) is used as an input parameter; it is magnitude dependent and is based on the period for which there is the highest correlation between the ln(PGD) and ln(PSA(T)). Two CGMMs are developed: a global model based on the NGA-W2 data and a region-specific model for Taiwan. The conditional PGD models are combined with traditional GMMs for PSA(T) values to develop GMMs for both the median and standard deviation of PGD without the dependence on PSA. A second set of PGD GMMs are developed to correct for two factors: the effect of the high-pass filtering from standard record processing and the stronger large magnitude (M>6.5) scaling due to tectonic deformation. For magnitudes greater than 7, the PGD values from the total PGD GMMs are 2–5 times larger than the bandlimited PGD values based on the strong-motion data sets, but the increase is at very long periods. The appropriate PGD model to use, bandlimited PGD or total PGD, depends on the period range of interest for the specific engineering application.

Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs

2019 ◽  
Vol 47 (1) ◽  
pp. 361-388 ◽  
Author(s):  
Richard M. Allen ◽  
Diego Melgar
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
The United States ◽  
Earthquake Early Warning ◽  
Ground Shaking ◽  
Motion Models ◽  
Societal Needs ◽  
Finite Fault ◽  
User Groups ◽  
Ground Motion Models

Earthquake early warning (EEW) is the delivery of ground shaking alerts or warnings. It is distinguished from earthquake prediction in that the earthquake has nucleated to provide detectable ground motion when an EEW is issued. Here we review progress in the field in the last 10 years. We begin with EEW users, synthesizing what we now know about who uses EEW and what information they need and can digest. We summarize the approaches to EEW and gather information about currently existing EEW systems implemented in various countries while providing the context and stimulus for their creation and development. We survey important advances in methods, instrumentation, and algorithms that improve the quality and timeliness of EEW alerts. We also discuss the development of new, potentially transformative ideas and methodologies that could change how we provide alerts in the future. ▪ Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken. ▪ EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States. ▪ EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches. ▪ The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.

scholarly journals A Regionally Adaptable Ground-Motion Model for Fourier Amplitude Spectra of Shallow Crustal Earthquakes in Europe

2021 ◽  
Author(s):  
Sreeram Reddy Kotha ◽  
Dino Bindi ◽  
Fabrice Cotton
Keyword(s):  
Spatial Variability ◽  
Ground Motion ◽  
High Frequency ◽  
Prediction Models ◽  
Activity Index ◽  
Site Response ◽  
Strong Motion ◽  
Response Spectra ◽  
Motion Models ◽  
Ground Motion Models

Abstract Typical ground-motion models predict the response spectral ordinates (GMM-SA), which are the damped responses of a suite of single-degree-of-freedom oscillators. Response spectra represent the response of an idealized structure to input ground-motion, but not the physics of the actual ground-motion. To complement the regionally adaptable GMM-SA of Kotha et al. (2020), we introduce here a model capable of predicting Fourier amplitudes (GMM-FA); developed from the same Engineering Strong Motion (ESM) dataset for pan-Europe. This GMM-FA reveals the very high variability of high frequency ground-motions, which are completely masked in a GMM-SA. By maintaining the development strategies of GMM-FA identical to that of the GMM-SA, we are able to evaluate the physical meaning of the spatial variability of anelastic attenuation and source characteristics. We find that a fully data-driven geospatial index, Activity Index (AIx), correlates well with the spatial variability of these physical effects. AIx is a fuzzy combination of seismicity and crustal parameters, and can be used to adapt the attenuation and source non-ergodicity of the GMM-FA to regions and tectonic localities sparsely sampled in ESM. While AIx, and a few other parameters we touch upon, may help understand the spatial variability of high frequency attenuation and source effects, the high frequency site-response variability - dominating the overall aleatory variance - is yet unresolvable. With the rapid increase in quantity and quality of ground-motion datasets, we call for an upgrade of regionalization techniques, site-characterisation, and a paradigm shift to Fourier ground-motion models to complement the traditional response spectra prediction models.

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