Comparison of Manual and Automated Ground Motion Processing for Small-to-Moderate-Magnitude Earthquakes in Japan

2017 ◽  
Vol 33 (3) ◽  
pp. 875-894 ◽  
Author(s):  
Tadahiro Kishida ◽  
Danilo Di Giacinto ◽  
Giuseppe Iaccarino
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Earthquake Engineering ◽  
Corner Frequency ◽  
Motion Processing ◽  
Ground Motion Parameters ◽  
Acceleration Time ◽  
Motion Parameters ◽  
Filter Noise ◽  
Moderate Magnitude

Numerous time series for small-to-moderate-magnitude (SMM) earthquakes have been recorded in many regions. A uniformly-processed ground-motion database is essential in the development of regional ground-motion models. An automated processing protocol is useful in developing the database for these earthquakes especially when the number of recordings is substantial. This study compares a manual and an automated ground-motion processing methods using SMM earthquakes. The manual method was developed by the Pacific Earthquake Engineering Research Center to build the database of time series and associated ground-motion parameters. The automated protocol was developed to build a database of pseudo-spectral acceleration for the Kiban-Kyoshin network recordings. Two significant differences were observed when the two methods were applied to identical acceleration time series. First, the two methods differed in the criteria for the acceptance or rejection of the time series in the database. Second, they differed in the high-pass corner frequency used to filter noise from the acceleration time series. The influences of these differences were investigated on ground-motion parameters to elucidate the quality of ground-motion database for SMM earthquakes.

NGA Project Strong-Motion Database

2008 ◽  
Vol 24 (1) ◽  
pp. 23-44 ◽  
Author(s):  
Brian Chiou ◽  
Robert Darragh ◽  
Nick Gregor ◽  
Walter Silva
Keyword(s):  
Time Series ◽  
Ground Motion ◽  
Model Development ◽  
Strong Motion ◽  
Corner Frequency ◽  
Engineering Practice ◽  
Data Set ◽  
Acceleration Time ◽  
Crustal Earthquakes ◽  
Strong Motion Database

A key component of the NGA research project was the development of a strong-motion database with improved quality and content that could be used for ground-motion research as well as for engineering practice. Development of the NGA database was executed through the Lifelines program of the PEER Center with contributions from several research organizations and many individuals in the engineering and seismological communities. Currently, the data set consists of 3551 publicly available multi-component records from 173 shallow crustal earthquakes, ranging in magnitude from 4.2 to 7.9. Each acceleration time series has been corrected and filtered, and pseudo absolute spectral acceleration at multiple damping levels has been computed for each of the 3 components of the acceleration time series. The lowest limit of usable spectral frequency was determined based on the type of filter and the filter corner frequency. For NGA model development, the two horizontal acceleration components were further rotated to form the orientation-independent measure of horizontal ground motion (GMRotI50). In addition to the ground-motion parameters, a large and comprehensive list of metadata characterizing the recording conditions of each record was also developed. NGA data have been systematically checked and reviewed by experts and NGA developers.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

2021 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Arthur J. Rodgers ◽  
Monica D. Kohler ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Ground Motion ◽  
San Francisco ◽  
Earthquake Engineering ◽  
Seismic Analysis ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Motion Time

ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

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