Correlation of Spectral Acceleration Values from NGA Ground Motion Models

2008 ◽  
Vol 24 (1) ◽  
pp. 299-317 ◽  
Author(s):  
Jack W. Baker ◽  
Nirmal Jayaram
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Single Equation ◽  
Spectral Acceleration ◽  
Ground Motion Selection ◽  
Motion Models ◽  
Attenuation Relationships ◽  
Ground Motion Model ◽  
Analysis Application ◽  
Ground Motion Models

Ground motion models (or “attenuation relationships”) describe the probability distribution of spectral acceleration at an individual period, given a set of predictor variables such as magnitude and distance, but they do not address the correlations between spectral acceleration values at multiple periods or orientations. Those correlations are needed for several calculations related to seismic hazard analysis and ground motion selection. Four NGA models and the NGA ground motion database are used here to measure these correlations, and predictive equations are fit to the results. The equations are valid for periods from 0.01 seconds to 10 seconds, versus similar previous equations that were valid only between 0.05 and 5 seconds and produced unreasonable results if extrapolated. Use of the new NGA ground motion database also facilitates a first study of correlations from intra- and inter-event residuals. Observed correlations are not sensitive to the choice of accompanying ground motion model, and intra-event, inter-event, and total residuals all exhibit similar correlation structure. A single equation is thus applicable for a variety of correlation predictions. A simple example illustrates the use of the proposed equations for one hazard analysis application.

Relational database used for ground-motion model development in the NGA-sub project

2021 ◽  
pp. 875529302110552
Author(s):  
Silvia Mazzoni ◽  
Tadahiro Kishida ◽  
Jonathan P Stewart ◽  
Victor Contreras ◽  
Robert B Darragh ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Data Storage ◽  
Relational Database ◽  
Model Development ◽  
Intensity Measures ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models ◽  
Pseudo Spectral

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

Ranking of Ground-Motion Models (GMMs) for Use in Probabilistic Seismic Hazard Analysis for Iran Based on an Independent Data Set

2020 ◽  
Author(s):  
Zoya Farajpour ◽  
Milad Kowsari ◽  
Shahram Pezeshk ◽  
Benedikt Halldorsson
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Selection Process ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT We apply three data-driven selection methods, log-likelihood (LLH), Euclidean distance-based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground-motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong-motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol-e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4.5≤Mw≤7.3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground-motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation-West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T=0.2  s). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable.

Exploring the Proximity of Ground-Motion Models Using High-Dimensional Visualization Techniques

2010 ◽  
Vol 26 (4) ◽  
pp. 1117-1138 ◽  
Author(s):  
Frank Scherbaum ◽  
Nicolas M. Kuehn ◽  
Matthias Ohrnberger ◽  
Andreas Koehler
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
High Dimensional ◽  
Logic Tree ◽  
Motion Models ◽  
Metric Set ◽  
Motion Characteristics ◽  
Visualization Techniques ◽  
Ground Motion Models

Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analysis. They are commonly used by assigning weights to models on a purely descriptive basis (nominal scale). This invites the creation of unintended inconsistencies regarding the weights on the corresponding hazard curves. On the other hand, for human experts it is difficult to confidently express degrees-of-beliefs in particular numerical values. Here we demonstrate for ground-motion models how the model and the value-based perspectives can be partially reconciled by using high-dimensional information-visualization techniques. For this purpose we use Sammon's (1969) mapping and self-organizing mapping to project ground-motion models onto a two-dimensional map (an ordered metric set). Here they can be evaluated jointly according to their proximity in predicting similar ground motions, potentially making the assignment of logic tree weights consistent with their ground motion characteristics without having to abandon the model-based perspective.

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>

Comparison of Near-Fault Velocity Pulse-Like Ground Motions from the 2018 Mw 6.4 Hualien, Taiwan, Earthquake with the Next Generation Attenuation (NGA)-West2 Ground-Motion Models and Directivity Models

2021 ◽  
Author(s):  
Xiaofen Zhao ◽  
Zengping Wen ◽  
Junju Xie ◽  
Quancai Xie ◽  
Kuo-En Ching
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
Spectral Shape ◽  
Next Generation ◽  
Motion Models ◽  
Near Fault ◽  
Short Period ◽  
Shape Curve ◽  
Ground Motion Models

ABSTRACT Pulse-like ground motions cause severe damage in structures at certain periods. Hence, pulse effects need to be considered during probabilistic seismic hazard analysis and seismic design in the near-fault region. Traditional ground-motion models used to quantify the hazard posed by pulse-like ground motions may underestimate them, but they are relatively suitable for describing the residual ground motions after extracting pulses. Nevertheless, the applicability of Next Generation Attenuation-West2 Project (NGA-West2) models to pulse and residual ground motions has not been evaluated. Moreover, the applicability of recently developed directivity models, including the Shahi and Baker (2011; hereafter, SB2011), Chang et al. (2018; hereafter, Chang2018), and Rupakhety et al. (2011; hereafter, Rupakhety2011) models, has not been investigated for this event. Here, based on the abundance of pulse-like ground motions recorded during the Mw 6.4 Hualien earthquake, the applicability of NGA-West2 models and directivity models was quantitatively evaluated. In summary, (1) The applicability of NGA-West2 models to the observed original and residual ground motions varies significantly at different periods. The suggests that NGA-West2 models overestimate the original and residual ground motions for short periods (T<1.0  s), but are suitable for describing the residual ground motions yet underestimate the original ground motions for long periods (T≥1.0  s). (2) Pulse periods and amplification bands due to pulses are unusually larger than previous events. Similar to the Chang2018 model, the plateau of this event starts and ends at the periods of 0.70 and 1.1 times the pulse period. However, the Chang2018 and SB2011 models underestimate the constant ordinate of this plateau. Spectral ordinates of the spectral shape curve due to pulses for the short period (∼Tn<1.3  s) are smaller than the predictions from the Rupakhety2011 model. The trend was reversed for long periods (∼Tn>3.0  s). Compared with the Rupakhety2011 model, the peak location of the spectral shape curve is shifted to the long period. These results will be helpful for updating these models in the near future.

Localizing Ground-Motion Models in Volcanic Terranes: Shallow Events at Mt. Etna, Italy, Revisited

2020 ◽  
Vol 110 (6) ◽  
pp. 2843-2861
Author(s):  
Giuseppina Tusa ◽  
Horst Langer ◽  
Raffaele Azzaro
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Total Error ◽  
Scaling Law ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Hypocentral Distance ◽  
Motion Models ◽  
Mt Etna ◽  
Ground Motion Models

ABSTRACT We present a set of revised ground-motion models (GMMs) for shallow events at Mt. Etna Volcano. The recent occurrence of damaging events, in particular two of the strongest earthquakes ever instrumentally recorded in the area, has required revising previous GMMs, as these failed to match the observations made for events with local magnitude ML>4.3, above all for sites situated close to the epicenter. The dataset now includes 49 seismic events, with a total of 1600 time histories recorded at distances of up to 100 km, and ML ranging from 3.0 to 4.8. The model gives estimates of peak ground acceleration (both horizontal and vertical), peak ground velocity (both horizontal and vertical), and 5% damped horizontal pseudoacceleration response spectral ordinates up to a period of 4 s. GMMs were developed using the functional form proposed by Boore and Atkinson (2008). Furthermore, with a slightly modified approach, we also considered a regression model using a pseudodepth (h) depending on magnitude according to the scaling law by Azzaro et al. (2017). Both models were applied to hypocentral distance ranges of up to 60 km and up to 100 km, respectively. From the statistical analysis, we found that reducing the maximum distance from the event up to 60 km and introducing a magnitude-dependent pseudodepth improved the model in terms of total error. We compared our results with those derived using the GMMs for shallow events at Mt. Etna found by Tusa and Langer (2016) and for volcanic areas by Lanzano and Luzi (2019). The main differences are observed at short epicentral distances and for higher magnitude events. The use of variable pseudodepth avoids sharp peaks of predicted ground-motion parameters around the epicenter, preventing instabilities when using a GMM in probabilistic seismic hazard analysis.

Arias Intensity Conditional Scaling Ground‐Motion Models for Subduction Zones

2019 ◽  
Vol 109 (4) ◽  
pp. 1343-1357 ◽  
Author(s):  
Jorge Macedo ◽  
Norman Abrahamson ◽  
Jonathan D. Bray
Keyword(s):  
Ground Motion ◽  
Subduction Zones ◽  
Spectral Acceleration ◽  
Scaling Effects ◽  
Arias Intensity ◽  
Ground Acceleration ◽  
Earthquake Scenario ◽  
Motion Models ◽  
Conditional Models ◽  
Ground Motion Models

Abstract Conditional ground‐motion models (CGMMs) for estimating Arias intensity (IA) for earthquakes in subduction zones are developed. The estimate of IA is conditioned in these models on the estimated peak ground acceleration (PGA), the spectral acceleration at T=1  s (SA1), time‐averaged shear‐wave velocity in the top 30 m (VS30), and magnitude (Mw). Random‐effects regressions are used to develop CGMMs for Japan, Taiwan, South America, and New Zealand. By combining the conditional models of IA with the ground‐motion models (GMMs) for PGA and SA1, the conditional models are converted to scenario‐based GMMs that can be used to estimate the median IA and its standard deviation directly for a given earthquake scenario and site conditions. The conditional scaling approach ensures the estimated IA values are consistent with a design spectrum that may correspond to above‐average spectral values for the controlling scenario. In addition, this approach captures the complex ground‐motion scaling effects found in GMMs for spectral acceleration, such as sediment‐depth effects, soil nonlinearity effects, and regionalization effects, in the developed scenario‐based models for IA. Estimates from the new scenario‐based IA models are compared to those from traditional GMMs for IA in subduction zones.

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