Ground-Motion Model for Deep Intraslab Subduction Zone Earthquakes of Northeastern India (NEI) and Adjacent Regions

2020 ◽  
Author(s):  
Ricky L. Chhangte ◽  
Tauhidur Rahman ◽  
Ivan G. Wong
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Strong Motion ◽  
Sensitivity Analyses ◽  
Motion Model ◽  
Strong Motion Data ◽  
Northeastern India ◽  
Motion Data ◽  
Ground Motion Model ◽  
Subduction Zone Earthquakes

ABSTRACT In this study, a ground-motion model (GMM) for deep intraslab subduction zone earthquakes in northeastern India (NEI) and adjacent regions, including portions of Bangladesh, Bhutan, China, Myanmar, and Nepal, is developed. Strong-motion data for deep intraslab earthquakes in NEI are very sparse, so it is not possible to develop a robust empirical GMM; hence, we used the stochastic point-source model to develop a new GMM. The model is based on ground-motion simulations of 36,500 Mw 5–8 earthquakes and epicentral distances of 50–300 km. We used region-specific key seismic parameters, for example, stress parameter, quality factor, and path duration in ground-motion simulation. Sensitivity analyses were also performed to evaluate the bias of each key seismic input parameter. We compared our GMM with the existing strong-motion data and compared our model with those of Lin and Lee (2008), Abrahamson et al. (2016), and Idini et al. (2017), which were developed for intraslab earthquakes based on VS30 and hypocentral depth. Our model gives higher values compared with their GMMs. Both peak ground acceleration and spectral acceleration values are estimated for NEI and adjacent regions intraslab earthquakes.

scholarly journals Simulation of non-stationary stochastic ground motions based on recent Italian earthquakes

2021 ◽  
Author(s):  
Fabio Sabetta ◽  
Antonio Pugliese ◽  
Gabriele Fiorentino ◽  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Stochastic Simulations ◽  
Model Parameters ◽  
Motion Model ◽  
Arias Intensity ◽  
Time Frequency ◽  
Ground Motion Model ◽  
Ground Motion Records

AbstractThis work presents an up-to-date model for the simulation of non-stationary ground motions, including several novelties compared to the original study of Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996). The selection of the input motion in the framework of earthquake engineering has become progressively more important with the growing use of nonlinear dynamic analyses. Regardless of the increasing availability of large strong motion databases, ground motion records are not always available for a given earthquake scenario and site condition, requiring the adoption of simulated time series. Among the different techniques for the generation of ground motion records, we focused on the methods based on stochastic simulations, considering the time- frequency decomposition of the seismic ground motion. We updated the non-stationary stochastic model initially developed in Sabetta and Pugliese (Bull Seism Soc Am 86:337–352, 1996) and later modified by Pousse et al. (Bull Seism Soc Am 96:2103–2117, 2006) and Laurendeau et al. (Nonstationary stochastic simulation of strong ground-motion time histories: application to the Japanese database. 15 WCEE Lisbon, 2012). The model is based on the S-transform that implicitly considers both the amplitude and frequency modulation. The four model parameters required for the simulation are: Arias intensity, significant duration, central frequency, and frequency bandwidth. They were obtained from an empirical ground motion model calibrated using the accelerometric records included in the updated Italian strong-motion database ITACA. The simulated accelerograms show a good match with the ground motion model prediction of several amplitude and frequency measures, such as Arias intensity, peak acceleration, peak velocity, Fourier spectra, and response spectra.

scholarly journals Ground motion and rupture process of the 2003 Tokachi-oki earthquake obtained from strong motion data of K-NET and KiK-net

2004 ◽  
Vol 56 (3) ◽  
pp. 317-322 ◽  
Author(s):  
Ryou Honda ◽  
Shin Aoi ◽  
Nobuyuki Morikawa ◽  
Haruko Sekiguchi ◽  
Takashi Kunugi ◽  
...  
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Rupture Process ◽  
Strong Motion Data ◽  
Motion Data

scholarly journals Towards preparation of design spectra for Serbian National Annex to Eurocode 8: Part I: spectral shapes and regional empirical equations for scaling pseudo-acceleration spectra

2012 ◽  
Vol 10 (2) ◽  
pp. 131-154
Author(s):  
Borko Bulajic ◽  
Miodrag Manic ◽  
Djordje Ladjinovic
Keyword(s):  
Ground Motion ◽  
Response Spectrum ◽  
Strong Motion ◽  
Soil Conditions ◽  
Eurocode 8 ◽  
Empirical Equations ◽  
Strong Motion Data ◽  
Motion Data ◽  
Local Soil ◽  
Spectral Shapes

Eurocode 8 allows that any country can use its own shape of the elastic response spectrum after it defines it in the National Annex. Having in mind that such country-specific spectra are to be derived through analysis of the strong motion data recorded in the considered seismo-tectonic region, in this Paper we discuss the existing and a set of new empirical equations for scaling pseudo-acceleration spectra in Serbia and the whole region of north-western Balkans. We then compare the presented spectra to those proposed by Eurocode 8. Results show that the indiscriminate use of the strong motion data from different seismo-tectonic regions, improper classification of the local soil conditions, and neglect of the effects of deep geology, may all lead to unreliable scaling equations and to extremely biased ground motion estimates. Moreover, only two spectral shapes that are defined for wide magnitude ranges and scaled by a single PGA value, are not able to adequately represent all important features of real strong ground motion, and instead of using such normalized spectra one should rather employ the direct scaling of spectral amplitudes that is based on the analysis of regionally gathered and processed strong motion data.

Exploring the Dimensionality of Ground-Motion Data by Applying Autoencoder Techniques

2021 ◽  
Author(s):  
Reza Dokht Dolatabadi Esfahani ◽  
Kristin Vogel ◽  
Fabrice Cotton ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
...  
Keyword(s):  
Ground Motion ◽  
Reconstruction Error ◽  
Variable Number ◽  
Data Driven ◽  
Data Reconstruction ◽  
Motion Model ◽  
Distance Information ◽  
Motion Data ◽  
Ground Motion Model ◽  
Minimum Number

ABSTRACT 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.

A Ground Motion Prediction Model for Deep Earthquakes beneath the Island of Hawaii

2015 ◽  
Vol 31 (3) ◽  
pp. 1763-1788 ◽  
Author(s):  
Ivan G. Wong ◽  
Walter J. Silva ◽  
Robert Darragh ◽  
Nick Gregor ◽  
Mark Dober
Keyword(s):  
Prediction Model ◽  
Ground Motion ◽  
Strong Motion ◽  
Nonlinear Least Squares ◽  
Source Model ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Motion Data ◽  
Ground Motion Model ◽  
Stress Drops

Until recently, no ground motion prediction model was available for deep ( >20 km) Hawaiian earthquakes, including the 2006 M6.7 Kiholo Bay earthquake. We developed such a model based on the stochastic point-source model. Strong motion data from the 2006 event and 15 other deep Hawaiian earthquakes of M3.3 to M6.2 were inverted using a nonlinear least-squares inversion of Fourier amplitude spectra to estimate stress drops for input into the stochastic modeling and for the few larger events (M ≥ 5.0), to calibrate the ground motion prediction model. The ground motion model is valid for M3.5 to M7.5 over the Joyner-Boore ( RJB) distance range of 20 km to 400 km and are for 5%-damped horizontal spectral acceleration at 27 periods from PGA (0.01 s) to 10.0 s. The shallow site condition assumed for the model is soil and weathered basalt with a mean VS30 of 428 m/s.

scholarly journals Accessing European Strong-Motion Data: An Update on ORFEUS Coordinated Services

2021 ◽  
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi ◽  
Carlo Cauzzi ◽  
Jarek Bienkowski ◽  
Dino Bindi ◽  
...  
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Strong Motion ◽  
Automated System ◽  
Data Repositories ◽  
Waveform Data ◽  
Strong Motion Data ◽  
Web Interfaces ◽  
Motion Data ◽  
Major Interest

Abstract Strong ground motion records and free open access to strong-motion data repositories are fundamental inputs to seismology, engineering seismology, soil dynamics, and earthquake engineering science and practice. This article presents the current status and outlook of the Observatories and Research Facilities for European Seismology (ORFEUS) coordinated strong-motion seismology services, namely the rapid raw strong-motion (RRSM) and the engineering strong-motion (ESM) databases and associated web interfaces and webservices. We compare and discuss the role and use of these two systems using the Mw 6.5 Norcia (Central Italy) earthquake that occurred on 30 October 2016 as an example of a well-recorded earthquake that triggered major interest in the seismological and earthquake engineering communities. The RRSM is a fully automated system for rapid dissemination of earthquake shaking information, whereas the ESM provides quality-checked, manually processed waveforms and reviewed earthquake information. The RRSM uses only data from the European Integrated Waveform Data Archive, whereas the ESM also includes offline data from other sources, such as the ITalian ACcelerometric Archive (ITACA). Advanced software tools are also included in the ESM to allow users to process strong-motion data and to select ground-motion waveform sets for seismic structural analyses. The RRSM and ESM are complementary services designed for a variety of possible stakeholders, ranging from scientists to the educated general public. The RRSM and ESM are developed, organized, and reviewed by selected members of the seismological community in Europe, including strong-motion data providers and expert users. Global access and usage of the data is encouraged. The ESM is presently the reference database for harmonized seismic hazard and risk studies in Europe. ORFEUS strong-motion data are open, “Findable, Accessible, Interoperable, and Reusable,” and accompanied by licensing information. The users are encouraged to properly cite the data providers, using the digital object identifiers of the seismic networks.

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.

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