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.

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 A Non-Ergodic Effective Amplitude Ground-Motion Model for California

2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson ◽  
Nicolas M. Kuehn
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Amplitude Spectrum ◽  
Time History ◽  
Motion Model ◽  
Small Magnitude ◽  
Varying Coefficients ◽  
Ground Motion Model ◽  
Systematic Effects

Abstract A new non-ergodic ground-motion model (GMM) for effective amplitude spectral (EAS) values for California is presented in this study. EAS, which is defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic EAS GMM, rather than a spectral acceleration GMM, is that the scaling of EAS does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic EAS GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately 30 to 40% smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past event.

A ground motion model for volcanic areas in Italy

2019 ◽  
Vol 18 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi
Keyword(s):  
Ground Motion ◽  
Motion Model ◽  
Ground Motion Model

Conditional Ground-Motion Model for Damaging Characteristics of Near-Fault Ground Motions Based on Instantaneous Power

2020 ◽  
Vol 110 (6) ◽  
pp. 2828-2842
Author(s):  
Esra Zengin ◽  
Norman Abrahamson
Keyword(s):  
Ground Motion ◽  
Ground Motions ◽  
Accurate Estimation ◽  
Spectral Acceleration ◽  
Motion Model ◽  
Velocity Pulse ◽  
Near Fault ◽  
Instantaneous Power ◽  
Ground Motion Model ◽  
Short Time

ABSTRACT The velocity pulse in near-fault ground motions has been used as a key characteristic of damaging ground motions. Characterization of the velocity pulse involves three parameters: presence of the pulse, period of the pulse, and amplitude of the pulse. The basic concept behind the velocity pulse is that a large amount of seismic energy is packed into a short time, leading to larger demands on the structure. An intensity measure for near-fault ground motions, which is a direct measure of the amount of energy arriving in short time, called instantaneous power (IP (T1)), is defined as the maximum power of the bandpass-filtered velocity time series measured over a time interval of 0.5T1, in which T1 is the fundamental period of the structure. The records are bandpass filtered in the period band (0.2T1−3T1) to remove the frequencies that are not expected to excite the structure. Zengin and Abrahamson (2020) showed that the drift is better correlated with the IP (T1) than with the velocity pulse parameters for records scaled to the same spectral acceleration at T1. A conditional ground-motion model (GMM) for the IP is developed based on the 5%-damped spectral acceleration at T1, the earthquake magnitude, and the rupture distance. This conditional GMM can be used for record selection for near-fault ground motions that captures the key features of velocity pulses and can lead to a better representation of the median and variability of the maximum interstory drift. The conditional GMM can also be used in a vector hazard analysis for spectral acceleration (T1) and IP (T1) that can be used for more accurate estimation of drift hazard and seismic risk.

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