NGA-Subduction research program

2021 ◽  
pp. 875529302110560
Author(s):  
Yousef Bozorgnia ◽  
Norman A Abrahamson ◽  
Sean K Ahdi ◽  
Timothy D Ancheta ◽  
Linda Al Atik ◽  
...  
Keyword(s):  
Ground Motion ◽  
Research Program ◽  
Epistemic Uncertainty ◽  
Specific Model ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Motion Data ◽  
Motion Models ◽  
Aleatory Variability ◽  
Adjustment Factors

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

NGA-subduction global ground motion models with regional adjustment factors

2021 ◽  
pp. 875529302110348
Author(s):  
Grace A Parker ◽  
Jonathan P Stewart ◽  
David M Boore ◽  
Gail M Atkinson ◽  
Behzad Hassani
Keyword(s):  
Ground Motion ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Peak Ground Velocity ◽  
Earthquake Ground Motion ◽  
Motion Models ◽  
Magnitude Scaling ◽  
Aleatory Variability ◽  
Single Station ◽  
Ground Motion Models

We develop semi-empirical ground motion models (GMMs) for peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral accelerations for periods from 0.01 to 10 s, for the median orientation-independent horizontal component of subduction earthquake ground motion. The GMMs are applicable to interface and intraslab subduction earthquakes in Japan, Taiwan, Mexico, Central America, South America, Alaska, the Aleutian Islands, and Cascadia. The GMMs are developed using a combination of data inspection, data regression with respect to physics-informed functions, ground-motion simulations, and geometrical constraints for certain model components. The GMMs capture observed differences in source and path effects for interface and intraslab events, conditioned on moment magnitude, rupture distance, and hypocentral depth. Site effect and aleatory variability models are shared between event types. Regionalized GMM components include the model constant (that controls ground motion amplitude), anelastic attenuation, magnitude-scaling break point, linear site response, and sediment depth terms. We develop models for the aleatory between-event variability [Formula: see text], within-event variability [Formula: see text], single-station within-event variability [Formula: see text], and site-to-site variability [Formula: see text]. Ergodic analyses should use the median GMM and aleatory variability computed using the between-event and within-event variability models. An analysis incorporating non-ergodic site response should use the median GMM at the reference shear-wave velocity condition, a site-specific site response model, and aleatory variability computed using the between-event and single-station within-event variability models. Epistemic uncertainty in the median model is represented by standard deviations on the regional model constants, which facilitates scaled-backbone representations of model uncertainty in hazard analyses.

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.

A Ground-Motion Prediction Model for Shallow Crustal Earthquakes in Greece

2020 ◽  
Author(s):  
David M. Boore ◽  
Jonathan P. Stewart ◽  
Andreas A. Skarlatoudis ◽  
Emel Seyhan ◽  
Basil Margaris ◽  
...  
Keyword(s):  
Prediction Model ◽  
Ground Motion ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Peak Ground Velocity ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Ground Acceleration ◽  
Magnitude Scaling ◽  
Nonlinear Site Response

ABSTRACT Using a recently completed database of uniformly processed strong-motion data recorded in Greece, we derive a ground-motion prediction model (GMPM) for horizontal-component peak ground velocity, peak ground acceleration, and 5% damped pseudoacceleration response spectra, at 105 periods ranging from 0.01 to 10 s. The equations were developed by modifying a global GMPM, to account for more rapid attenuation and weaker magnitude scaling in the Greek ground motions than in the global GMPM. Our GMPM is calibrated using the Greek data for distances up to 300 km, magnitudes from 4.0 to 7.0, and time-averaged 30 m shear-wave velocities from 150 to 1200  m/s. The GMPM has important attributes for hazard applications including magnitude scaling that extends the range of applicability to M 8.0 and nonlinear site response. These features are possible because they are well constrained by data in the global GMPM from which our model is derived. An interesting feature of the Greek data, also observed previously in studies of mid-magnitude events (6.1–6.5) in Italy, is that they are substantially overpredicted by the global GMPM, which may be a repeatable regional feature, but may also be influenced by soil–structure interaction. This bias is an important source of epistemic uncertainty that should be considered in hazard analysis.

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.

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.

Site-Condition Proxies, Ground Motion Variability, and Data-Driven GMPEs: Insights from the NGA-West2 and RESORCE Data Sets

2016 ◽  
Vol 32 (4) ◽  
pp. 2027-2056 ◽  
Author(s):  
Boumédiène Derras ◽  
Pierre-Yves Bard ◽  
Fabrice Cotton
Keyword(s):  
Ground Motion ◽  
Random Effect ◽  
Poor Performance ◽  
Data Driven ◽  
Peak Ground Velocity ◽  
Site Condition ◽  
Ground Acceleration ◽  
Data Set ◽  
Aleatory Variability ◽  
Ground Motion Variability

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%.

New Ground Motion to Intensity Conversion Equations (GMICEs) for New Zealand

2020 ◽  
Vol 92 (1) ◽  
pp. 448-459 ◽  
Author(s):  
Jose M. Moratalla ◽  
Tatiana Goded ◽  
David A. Rhoades ◽  
Silvia Canessa ◽  
Matthew C. Gerstenberger
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
Strong Motion ◽  
Peak Ground Velocity ◽  
Least Squares Regression ◽  
Data Types ◽  
Ground Acceleration ◽  
Hypocentral Distance ◽  
Motion Data ◽  
Recent Earthquakes

Abstract Macroseismic intensities play a key role in the engineering, seismological, and loss modeling communities. However, at present, there is an increasing demand for instrumental data-based loss estimations that require statistical relationships between intensities and strong-motion data. In New Zealand, there was an urgent need to update the ground motion to intensity conversion equation (GMICE) from 2007, developed prior to a large number of recent earthquakes including the 2010–2011 Canterbury and 2016 Kaikōura earthquake sequences. Two main factors now provide us with the opportunity to update New Zealand’s GMICE: (1) recent publication of New Zealand’s Strong-Motion Database, corresponding to 276 New Zealand earthquakes with magnitudes 3.5–7.8 and 4–185 km depths; and (2) recent generation of a community intensity database from GeoNet’s “Felt Classic” (2004–2016) and “Felt Detailed” (2016–2019) questionnaires, corresponding to around 930,000 individual reports. Ground-motion data types analyzed are peak ground velocity (PGV) and peak ground acceleration (PGA). The intensity database contains 67,572 felt reports from 917 earthquakes, with magnitudes 3.5–8.1, and 1797 recordings from 247 strong-motion stations (SMSs), with hypocentral distances of 5–345 km. Different regression analyses were tested, and the bilinear regression of binned mean strong-motion recordings for 0.5 modified Mercalli intensity bins was selected as the most appropriate. Total least squares regression was chosen for reversibility in the conversions. PGV provided the best-fitting results, with lower standard deviations. The influence of hypocentral distance, earthquake magnitude, and the site effects of local geology, represented by the mean shear-wave velocity in the first 30 m depth, on the residuals was also explored. A regional correction factor for New Zealand, suitable for adjustment of global relationships, has also been estimated.

Ground-motion models for Vrancea intermediate-depth earthquakes

2021 ◽  
pp. 875529302110329
Author(s):  
Elena Florinela Manea ◽  
Carmen Ortanza Cioflan ◽  
Laurentiu Danciu
Keyword(s):  
Ground Motion ◽  
Site Response ◽  
Seismic Station ◽  
Moment Magnitude ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Ground Shaking ◽  
Intermediate Depth ◽  
Motion Models ◽  
Ground Motion Models

A newly compiled high-quality ground-shaking dataset of 207 intermediate-depth earthquakes recorded in the Vrancea region of the south-eastern Carpathian mountains in Romania was used to develop region-specific empirical predictive equations for various intensity measures: peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration up to 10 s. Besides common predictor variables (e.g. moment magnitude, depth, hypocentral distance, and site conditions), additional distance scaling parameters were added to describe the specific attenuation pattern observed at the stations located not only on the back and fore but also along the Carpathian arc. In this model, we introduce a proxy measure for the site as the fundamental frequency of resonance to characterize the site response at each recording seismic station beside the soil classes. To additionally reduce the site-to-site variability, a non-ergodic methodology was considered, resulting in a lower standard deviation of about 25%. Statistical evaluation of the newly proposed ground-motion models indicates robust performance compared to regional observations. The model shows significant improvements in describing the spatial variability (at different spectral ordinates), particularly for the fore-arc area of the Carpathians where a deep sedimentary basin is located. Furthermore, the model presented herein improves estimates of ground shaking at longer spectral ordinates (>1 s) in agreement with the observations. The proposed ground-motion models are valid for hypocentral distances less than 500 km, depths over 70 km and within the moment magnitude range of 4.0–7.4.

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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