Network analysis of earthquake ground motion spatial correlation: A case study with the san jacinto seismic nodal array

2021 ◽  
Author(s):  
Yixiao Sheng ◽  
Qingkai Kong ◽  
Gregory C Beroza
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Strong Motion ◽  
Earthquake Ground Motion ◽  
Graph Analytics ◽  
Motion Data ◽  
Large N ◽  
Aftershock Sequences ◽  
Weak Constraints

Summary The spatial correlation of earthquake ground motion intensity can be measured from strong motion data; however, the data used in past studies is sparsely sampled in space, and only the inter-station distance was considered as a correlation variable. These limitations mean that we have only weak constraints on the true correlation structure of ground motion and that potentially important aspects of spatial correlation are unconstrained. In this study, we combine a large-N seismic array and graph analytics to explore this issue at a local scale using small local and regional earthquakes. Our result suggests site conditions, and how they interact with the incident seismic wavefield, strongly condition the spatial correlation of ground motion. Future progress in characterizing ground motion spatial variability will require dense wavefield measurements, either through nodal deployments, or perhaps distributed acoustic sensing (DAS) measurements, of seismic wavefields. Aftershock sequences of major earthquakes would provide particularly data-rich targets of opportunity.

Seismic response and retrofit of industrial brick masonry chimneys

1992 ◽  
Vol 19 (1) ◽  
pp. 117-128 ◽  
Author(s):  
A. Ghobarah ◽  
T. Baumber
Keyword(s):  
Seismic Response ◽  
Ground Motion ◽  
Strong Motion ◽  
Brick Masonry ◽  
Wind Loading ◽  
Earthquake Ground Motion ◽  
Lumped Mass ◽  
Mass System ◽  
Higher Modes ◽  
Recent Earthquakes

During recent earthquakes, the documented cases of collapsed unreinforced brick masonry industrial chimneys are numerous. Observed modes of structural failure are either total collapse or sometimes collapse or damage of the top third of the structure. The objective of this study is to analyze and explain the modes of observed failure of masonry chimneys during earthquake events, and to evaluate two retrofit systems for existing chimneys in areas of high seismicity. The behaviour of the masonry chimney, when subjected to earthquake ground motion, was modelled using a lumped mass system. Several actual strong motion records were used as input to the model. The shear, moment, and displacement responses to the earthquake ground motion were evaluated for various chimney configurations. It was found that the failure of the chimney at its base is the result of the fundamental mode of vibration. Failure at the top third of the structure due to the higher modes of vibration is possible when the chimney is subjected to high frequency content earthquakes. Higher modes, which are normally not of concern under wind loading, were shown to be critical in seismic design. Post-tensioning and the reinforcing steel cage were found to be effective retrofit systems. Key words: masonry, chimneys, behaviour, analysis, design, retrofit, dynamic, earthquakes, seismic response.

Implementing horizontal-to-vertical Fourier spectral ratios and spatial correlation in a ground-motion prediction equation to predict site effects

2020 ◽  
pp. 875529302095244
Author(s):  
Shu-Hsien Chao ◽  
Che-Min Lin ◽  
Chun-Hsiang Kuo ◽  
Jyun-Yan Huang ◽  
Kuo-Liang Wen ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Ground Motion ◽  
Prediction Accuracy ◽  
Site Effects ◽  
Strong Motion ◽  
Prediction Equation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Ground Motion Prediction ◽  
Spectral Ratios

We propose a methodology to implement horizontal-to-vertical Fourier spectral ratios (HVRs) evaluated from strong ground motion induced by earthquake (EHVRs) or ambient ground motion observed from microtremor (MHVRs) individually and simultaneously with the spatial correlation (SC) in a ground-motion prediction equation (GMPE) to improve the prediction accuracy of site effects. We illustrated the methodology by developing an EHVRs-SC-based model which supplements Vs30 and Z1.0 with the SC and EHVRs collected at strong motion stations, and a MHVRs-SC-based model that supplements Vs30 and Z1.0 with the SC and MHVRs observed from microtremors at sites which were collocated with strong motion stations. The standard deviation of the station-specific residuals can be reduced by up to 90% when the proposed models are used to predict site effects. In the proposed models, the spatial distribution of the predicted station terms for peak ground acceleration (PGA) from MHVRs at 3699 sites is consistent with that of the predicted station terms for PGA from EHVRs at 721 strong motion stations. Prediction accuracy for stations with inferred Vs30 is similar to that of stations with measured Vs30 with the proposed models. This study provides a methodology to simultaneously implement SC and EHVRs, or SC and MHVRs in a GMPE to improve the prediction accuracy of site effects for a target site with available EHVRs or MHVRs information.

Improvement of the objective function in the velocity structure inversion based on horizontal-to-vertical spectral ratio of earthquake ground motions

2020 ◽  
Vol 224 (1) ◽  
pp. 1-16
Author(s):  
Mianshui Rong ◽  
Xiaojun Li ◽  
Lei Fu
Keyword(s):  
Objective Function ◽  
Ground Motion ◽  
Velocity Structure ◽  
Strong Motion ◽  
Earthquake Ground Motion ◽  
Inversion Method ◽  
Observation Data ◽  
S Wave ◽  
Inversion Methods ◽  
Underground Velocity Structures

SUMMARY Given the improvements that have been made in the forward calculations of seismic noise horizontal-to-vertical spectral ratios (NHVSRs) or earthquake ground motion HVSRs (EHVSRs), a number of HVSR inversion methods have been proposed to identify underground velocity structures. Compared with the studies on NHVSR inversion, the research on the EHVSR-based inversion methods is relatively rare. In this paper, to make full use of the widely available and constantly accumulating strong-motion observation data, we propose an S-wave HVSR inversion method based on diffuse-field approximation. Herein, the S-wave components of earthquake ground motion recordings are considered as data source. Improvements to the objective function has been achieved in this work. An objective function with the slope term is introduced. The new objective function can mitigate the multisolution phenomenon encountered when working with HVSR curves with multipeaks. Then, a synthetic case is used to show the verification of the proposed method and this method has been applied to invert underground velocity structures for six KiK-net stations based on earthquake observations. The results show that the proposed S-wave EHVSR inversion method is effective for identifying underground velocity structures.

Exploring the dimensionality of ground motion data

2020 ◽  
Author(s):  
Reza Dokht Dolatabadi Esfahani ◽  
Kristin Vogel ◽  
Fabrice Cotton ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
...  
Keyword(s):  
Ground Motion ◽  
Predictive Power ◽  
Epistemic Uncertainty ◽  
Dimensional Space ◽  
Amplitude Spectrum ◽  
Strong Motion ◽  
Motion Prediction ◽  
Dimensional Manifold ◽  
Ground Motion Prediction ◽  
Motion Data

<p>For years, engineering seismologists aim to reduce the epistemic uncertainty related to ground motion prediction. Assuming that simple models with few variables are not sufficient to describe the complex phenomena, there is a trend in present-day science to increase complexity of ground motion models. Therefore, some of the most recent ground motion prediction equations use more than 20 variables to improve the predictive power of the model. However, the legitimate question to ask is whether the inclusion of additional variables leads to an improved predictive power of the model. In other words, what is the smallest number of predictive variables needed to reconstruct the distribution of ground motion induced shaking observed in data? In this study, by taking advantage of the exponential growth of ground motion data and new machine learning methods, we present a data-driven approach to derive the dimensionality of ground motion data in the Fourier amplitude spectrum (FAS) metric. We apply an autoencoder architecture, which is commonly used for mapping high dimensional data to a lower dimensional space (bottleneck) and search for the lowest dimensionality (minimum number of nodes in the bottleneck) required to reconstruct the FAS input data. The approach is tested on synthetic ground motion data with known dimensionality (2D and 4D) and finally applied to the FAS of recorded ground motion data. A simple autoencoder with variable nodes in the bottleneck is used to explore the dimensionality of the ground motion data. We use the relation between the total residual of the network with the number of codes in the bottleneck as an indicator of dimensionality. Its numerical value is estimated based on the reduction of residuals by increasing the number of codes in the bottleneck layer. In addition, we use the low dimensional manifold of the ground motion data to predict the ground motion shaking for a given scenario. The residual analyses between observed and reconstructed data and observed and predicted data are used to validate the training and prediction steps. We applied the method on different scenarios in two synthetic data sets which are simulated by a stochastic simulation method and secondly the Pan-European engineering strong motion data (EMS) to show the performance of the proposed method. The results show that the statistical properties of ground motion data can be captured by using a limited number of three to five parameters. Especially for low frequency data the most dominant features are already captured by two parameters (codes), which roughly correspond to magnitude and distance. For higher frequencies additional parameters, e.g. corresponding to stress drop and kappa, become more relevant. The standard deviation of the residuals can be reduced to its lower bound in comparison with the standard deviations of conventional methods. Finally, we use a two-dimensional manifold to predict the FAS for given magnitude and distance values.</p>

Book Review of “On Strong Motion Seismology” (in Japanese) written and edited by Hiroaki Yamanaka, published by University of Tokyo Press

2006 ◽  
Vol 1 (3) ◽  
pp. 449-451
Author(s):  
Editorial Office
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Earthquake Ground Motion ◽  
Large Fires ◽  
Hyogo Prefecture ◽  
Narrow Belt ◽  
Oil Tanks ◽  
Second Period ◽  
Collapsed Building

This book is a work for general readers, straight-forwardly treating the theme of "strong earthquake ground motion" directly causing disaster and explaining how to cope. Eartquake ground motion is generally said to cause earthquake disasters and the degree of ground motion is determined both by the magnitude of the earthquake and the distance from its epicenter. In reality, however, things are not so simple. In the 2003 Tokachi Offshore Earthquake, for example, shaking at a relatively long 10-second period resonated at the characteristic frequency of oil tanks, triggering sloshing and causing large fires. In the 1995 Southern Hyogo Prefecture Earthquake (the Great Hanshin-Awaji Earthquake Disaster), for another example, a long narrow belt of disaster confirmed where damage to collapsed building was especially significant because only ground within this belt quaked more intensely than elsewhere.

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 Seismological and Engineering Characteristics of Strong Motion Data from 24 and 26 September 2019 Marmara Sea Earthquakes

2021 ◽  
Author(s):  
Fatma Sevil Malcıoğlu ◽  
Hakan Süleyman ◽  
Eser Çaktı
Keyword(s):  
Ground Motion ◽  
Prediction Models ◽  
Strong Motion ◽  
P Wave ◽  
Corner Frequency ◽  
Marmara Region ◽  
Marmara Sea ◽  
S Wave ◽  
Data Set ◽  
Motion Data

Abstract An MW 4.5 earthquake took place on September 24, 2019 in the Marmara Sea. Two days after, on September 26, 2019, Marmara region was rattled by an MW5.7 earthquake. With the intention of compiling an ample strong ground motion data set of recordings, we have utilized the stations of Istanbul Earthquake Rapid Response and Early Warning System operated by the Department of Earthquake Engineering of Boğaziçi University and of the National Strong Motion Network operated by AFAD. All together 438 individual records are used to calculate the source parameters of events; namely, corner frequency, radius, rupture area, average source dislocation, source duration and stress drop. Some of these parameters are compared with empirical relationships and discussed extensively. Duration characteristics are analyzed in two steps; first, by making use of the time difference between P-wave and S-wave onsets and then, by considering S-wave durations and significant durations. It is observed that they yield similar trends with global models. PGA, PGV and SA values are compared with three commonly used ground motion prediction models. At distances closer than about 60 km observed intensity measures mostly conform with the GMPE predictions. Beyond 60 km their attenuation is clearly faster than those of GMPEs. Frequency-dependent Q models are developed for both events. Their consistency with existing regional models are confirmed.

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