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.

scholarly journals Ground motion models for Arias intensity, cumulative absolute velocity, peak incremental ground velocity, and significant duration in New Zealand

2019 ◽  
Vol 52 (4) ◽  
pp. 193-207 ◽  
Author(s):  
Zach Bullock
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
Absolute Velocity ◽  
Taupo Volcanic Zone ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Motion Models ◽  
Cumulative Absolute Velocity ◽  
Functional Forms ◽  
Ground Motion Models

This study proposes empirical ground motion models for a variety of non-spectral intensity measures and significant durations in New Zealand. Equations are presented for the prediction of the median and maximum rotated components of Arias intensity, cumulative absolute velocity, cumulative absolute velocity above a 5 cm/s2 acceleration threshold, peak incremental ground velocity, and the 5% to 75% and 5% to 95% significant durations. Recent research has highlighted the usefulness of these parameters in both structural and geotechnical engineering. The New Zealand Strong Motion Database provides the database for regression and includes many earthquakes from all regions of New Zealand with the exceptions of Auckland and Northland, Otago and Southland, and Taranaki. The functional forms for the proposed models are selected using cross validation. The possible influence of effects not typically included in ground motion models for these intensity measures is considered, such as hanging wall effects and basin depth effects, as well as altered attenuation in the Taupo Volcanic Zone. The selected functional forms include magnitude and rupture depth scaling, attenuation with distance, and shallow site effects. Finally, the spatial autocorrelation of the models’ within-event residuals is considered and recommendations are made for developing correlated maps of intensity predictions stochastically.

Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

2021 ◽  
Author(s):  
Tomohisa Okazaki ◽  
Nobuyuki Morikawa ◽  
Asako Iwaki ◽  
Hiroyuki Fujiwara ◽  
Tomoharu Iwata ◽  
...  
Keyword(s):  
Ground Motion ◽  
Ground Acceleration ◽  
Site Specific ◽  
Motion Data ◽  
Motion Models ◽  
Proposed Model ◽  
Single Station ◽  
Input Variables ◽  
Ground Condition ◽  
Ground Motion Models

ABSTRACT Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.

Ground Motion Models for the Horizontal Components of Arias Intensity (AI) and Cumulative Absolute Velocity (CAV) Using the NGA-West2 Database

2019 ◽  
Vol 35 (3) ◽  
pp. 1289-1310 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Yousef Bozorgnia
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Goodness Of Fit ◽  
Performance Metrics ◽  
Absolute Velocity ◽  
Arias Intensity ◽  
Intensity Measures ◽  
Motion Models ◽  
Cumulative Absolute Velocity ◽  
Ground Motion Models

We updated our Next Generation Attenuation (NGA)-West1 ground motion models (GMMs) for the horizontal components of Arias intensity (AI) and cumulative absolute velocity (CAV) using the functional form and NGA-West2 database we used to develop GMMs for peak-amplitude and peak-spectral ground motion intensity measures (GMIMs). Our results show that CAV has the best goodness-of-fit statistics of all the GMIMs we have evaluated up to this time. Its relatively small between- and within-event standard deviations confirm its superior predictability. On the other hand, AI has the highest standard deviation of any GMIM we have studied thus far, which is approximately double that of CAV. Although either CAV or AI or a combination of both have been shown to meet various performance metrics proposed in the context of performance-based earthquake engineering (PBEE), CAV's high level of predictability makes it superior to AI for use in engineering applications, such as PBEE, that involve probabilistic inference.

scholarly journals Characteristics of strong ground motion attenuation in the 2019 Mw 5.8 Changning earthquake, Sichuan, China

2021 ◽  
Author(s):  
J. J. Hu ◽  
H. Zhang ◽  
J. B. Zhu ◽  
G. H. Liu
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Response Spectra ◽  
Peak Ground Velocity ◽  
Arias Intensity ◽  
Ground Acceleration ◽  
Motion Models ◽  
Ground Motion Attenuation ◽  
Short Period ◽  
Strong Ground

AbstractA moderate magnitude earthquake with Mw 5.8 occurred on June 17, 2019, in Changning County, Sichuan Province, China, causing 13 deaths, 226 injuries, and serious engineering damage. This earthquake induced heavier damage than earthquakes of similar magnitude. To explain this phenomenon in terms of ground motion characteristics, based on 58 sets of strong ground motions in this earthquake, the peak ground acceleration (PGA), peak ground velocity (PGV), acceleration response spectra (Sa), duration, and Arias intensity are analyzed. The results show that the PGA, PGV, and Sa are larger than the predicted values from some global ground motion models. The between-event residuals reveal that the source effects on the intermediate-period and long-period ground motions are stronger than those on short-period ground motions. Comparison of Arias intensity attenuation with the global models indicates that the energy of ground motions of the Changning earthquake is larger than those of earthquakes with the same magnitude.

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.

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.

Ground motion models for the new seismic hazard model of Italy (MPS19): selection for active shallow crustal regions and subduction zones

2020 ◽  
Vol 18 (8) ◽  
pp. 3487-3516
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi ◽  
Vera D’Amico ◽  
Francesca Pacor ◽  
Carlo Meletti ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Subduction Zones ◽  
Hazard Model ◽  
Motion Models ◽  
Selection For ◽  
Ground Motion Models

scholarly journals An Evaluation of the Applicability of Current Ground-Motion Models to the South and Central American Subduction Zones

2012 ◽  
Vol 102 (1) ◽  
pp. 143-168 ◽  
Author(s):  
M. C. Arango ◽  
F. O. Strasser ◽  
J. J. Bommer ◽  
J. M. Cepeda ◽  
R. Boroschek ◽  
...  
Keyword(s):  
Ground Motion ◽  
Subduction Zones ◽  
Central American ◽  
The South ◽  
Motion Models ◽  
Ground Motion Models

scholarly journals The new Italian seismic hazard model (MPS19)

2021 ◽  
Vol 64 (1) ◽  
Author(s):  
Carlo Meletti ◽  
Warner Marzocchi ◽  
Vera D'Amico ◽  
Giovanni Lanzano ◽  
Lucia Luzi ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Model ◽  
Ground Acceleration ◽  
Motion Models ◽  
Novel Approach ◽  
Wide Range ◽  
Macroseismic Intensities ◽  
Ground Motion Models

We describe the main structure and outcomes of the new probabilistic seismic hazard model for Italy, MPS19 [Modello di Pericolosità Sismica, 2019]. Besides to outline the probabilistic framework adopted, the multitude of new data that have been made available after the preparation of the previous MPS04, and the set of earthquake rate and ground motion models used, we give particular emphasis to the main novelties of the modeling and the MPS19 outcomes. Specifically, we (i) introduce a novel approach to estimate and to visualize the epistemic uncertainty over the whole country; (ii) assign weights to each model components (earthquake rate and ground motion models) according to a quantitative testing phase and structured experts’ elicitation sessions; (iii) test (retrospectively) the MPS19 outcomes with the horizontal peak ground acceleration observed in the last decades, and the macroseismic intensities of the last centuries; (iv) introduce a pioneering approach to build MPS19_cluster, which accounts for the effect of earthquakes that have been removed by declustering. Finally, to make the interpretation of MPS19 outcomes easier for a wide range of possible stakeholders, we represent the final result also in terms of probability to exceed 0.15 g in 50 years.

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