scholarly journals Rupture model of the 1999 Düzce, Turkey, earthquake deduced from high and low frequency strong motion data

2004 ◽  
Vol 31 (5) ◽  
pp. n/a-n/a ◽  
Author(s):  
Gülüm Birgören ◽  
H. Sekiguchi ◽  
K. Irikura
Keyword(s):  
Low Frequency ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Rupture Model

Stable baseline correction of digital strong-motion data

1997 ◽  
Vol 87 (4) ◽  
pp. 932-944 ◽  
Author(s):  
Hung-Chie Chiu
Keyword(s):  
Least Squares ◽  
Linear Trend ◽  
Low Frequency ◽  
Strong Motion ◽  
Digital Data ◽  
Pass Filter ◽  
Strong Motion Data ◽  
Motion Data ◽  
Initial Values ◽  
Least Squares Fit

Abstract Most baseline errors of analog strong-motion data still exist in highresolution data. In this study, we identify the major baseline errors of digital strong-motion data and propose a three-step algorithm to correct these errors. The major baseline errors found in these digital data consist of constant drift in the acceleration, low-frequency instrument noise, low-frequency background noise, the small initial values for acceleration and velocity, and manipulation errors. This threestep algorithm includes fitting the baseline of acceleration by the least squares, applying a high-pass filter in acceleration, and subtracting the initial values in velocity. A least-squares fit of a straight line before filtering can effectively remove the baseline drift in acceleration. Then, the filtering removes the linear trend and other low-frequency errors that exist in the acceleration. Finally, the subtracting of the initial velocity removes the linear trend of displacement. Among these three steps, only the filtering in the second step may introduce a side effect. Compared to the Volume II routine developed by Trifunac and Lee (1973), this three-step processing significantly reduces computational efforts and side effects resulting from unnecessary manipulation of data. This algorithm has been successfully tested on several types of digital strong-motion data. Several independent validations show that the proposed algorithm is stable.

Automatic Inversion of Rupture Processes of the Foreshock and Mainshock and Correlation of the Seismicity during the 2019 Ridgecrest Earthquake Sequence

2020 ◽  
Vol 91 (3) ◽  
pp. 1556-1566 ◽  
Author(s):  
Yijun Zhang ◽  
Xujun Zheng ◽  
Qiang Chen ◽  
Xianwen Liu ◽  
Xiaomei Huang ◽  
...  
Keyword(s):  
High Reliability ◽  
Strong Motion ◽  
Temporal Correlation ◽  
Earthquake Sequence ◽  
Coseismic Slip ◽  
Aftershock Activity ◽  
Strong Motion Data ◽  
Motion Data ◽  
Coulomb Failure Stress Change ◽  
Rupture Model

Abstract The 2019 Ridgecrest, California, earthquake sequence included an Mw 6.4 foreshock on 4 July, followed by an Mw 7.1 mainshock about 32 hr later. We determined the rupture patterns of the foreshock and mainshock by applying the automatic iterative deconvolution and stacking method to strong-motion records. The foreshock was characterized by a unilateral rupture toward the southwest, and the shallow portion had a relatively large slip with the maximum value of ∼1.4  m. The mainshock presents an asymmetrical bilateral rupture with an average rupture velocity of 2.0  km/s. More than 80% of the seismic moment was released on the northwest segment of the fault, producing a maximum slip of ∼5.2  m. With the two inferred slip models, we calculated the Coulomb failure stress change (ΔCFS) to analyze the spatial–temporal correlation of the seismicity activity in this sequence. The result shows that the epicenter of the Mw 7.1 mainshock was brought 0.4 bars closer to failure by the Mw 6.4 foreshock, and the stress-increased zone has a good spatial consistence with the coseismic slip distribution of the mainshock and the aftershock distribution of the foreshock. Besides, the positive ΔCFS induced by the mainshock also enhanced its aftershock activity, especially at depths of 4–10 km where the major rupture occurred, inferring that the mainshock-induced ΔCFS may be responsible for the occurrence of aftershocks. In addition, we test the effects of different cutoff frequencies and crust velocity structures on the inversion results. The result reveals that the main source rupture characteristics are almost independent of these factors, implying a high reliability of automation inversion of strong-motion data. Overall, this work indicates that automatic inversion of strong-motion data can provide reliable and rapid rupture model, which is essential for earthquake emergency responses and tsunami early warnings.

Processed strong-motion data for the El Salvador earthquake of June 19, 1982

1988 ◽  
Author(s):  
Kenneth W. Campbell ◽  
Sylvester Theodore Algermissen
Keyword(s):  
El Salvador ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data

Duration prediction of Chilean strong motion data using machine learning

2021 ◽  
Vol 109 ◽  
pp. 103253
Author(s):  
Sarit Chanda ◽  
M.C. Raghucharan ◽  
K.S.K. Karthik Reddy ◽  
Vasudeo Chaudhari ◽  
Surendra Nadh Somala
Keyword(s):  
Machine Learning ◽  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data ◽  
Duration Prediction

Re-digitization of Strong Motion Data Recorded by SMAC-M Accelerographs

2021 ◽  
Vol 21 (1) ◽  
pp. 1_25-1_45
Author(s):  
Toshihide KASHIMA ◽  
Shin KOYAMA ◽  
Hiroto NAKAGAWA
Keyword(s):  
Strong Motion ◽  
Strong Motion Data ◽  
Motion Data

A comparison of two methods for earthquake source inversion using strong motion seismograms

1994 ◽  
Vol 37 (6) ◽  
Author(s):  
B. P. Cohee ◽  
G. C. Beroza
Keyword(s):  
Rise Time ◽  
Seismic Moment ◽  
Strong Motion ◽  
Rupture Time ◽  
Rupture Propagation ◽  
Rupture Velocity ◽  
Strong Motion Data ◽  
Motion Data ◽  
Inversion Methods ◽  
Window Method

In this paper we compare two time-domain inversion methods that have been widely applied to the problem of modeling earthquake rupture using strong-motion seismograms. In the multi-window method, each point on the fault is allowed to rupture multiple times. This allows flexibility in the rupture time and hence the rupture velocity. Variations in the slip-velocity function are accommodated by variations in the slip amplitude in each time-window. The single-window method assumes that each point on the fault ruptures only once, when the rupture front passes. Variations in slip amplitude are allowed and variations in rupture velocity are accommodated by allowing the rupture time to vary. Because the multi-window method allows greater flexibility, it has the potential to describe a wider range of faulting behavior; however, with this increased flexibility comes an increase in the degrees of freedom and the solutions are comparatively less stable. We demonstrate this effect using synthetic data for a test model of the Mw 7.3 1992 Landers, California earthquake, and then apply both inversion methods to the actual recordings. The two approaches yield similar fits to the strong-motion data with different seismic moments indicating that the moment is not well constrained by strong-motion data alone. The slip amplitude distribution is similar using either approach, but important differences exist in the rupture propagation models. The single-window method does a better job of recovering the true seismic moment and the average rupture velocity. The multi-window method is preferable when rise time is strongly variable, but tends to overestimate the seismic moment. Both methods work well when the rise time is constant or short compared to the periods modeled. Neither approach can recover the temporal details of rupture propagation unless the distribution of slip amplitude is constrained by independent data.

scholarly journals The INGV real time strong motion data sharing during the 2016 Amatrice (central Italy) seismic sequence

2016 ◽  
Vol 59 ◽  
Author(s):  
Marco Massa ◽  
Ezio D'Alema ◽  
Chiara Mascandola ◽  
Sara Lovati ◽  
Davide Scafidi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Sharing ◽  
Strong Motion ◽  
Main Task ◽  
Seismic Sequence ◽  
Web Portal ◽  
Epicentral Area ◽  
Strong Motion Data ◽  
Motion Data ◽  
The Web

<p><em>ISMD is the real time INGV Strong Motion database. During the recent August-September 2016 Amatrice, Mw 6.0, seismic sequence, ISMD represented the main tool for the INGV real time strong motion data sharing.  Starting from August 24<sup>th</sup>,  the main task of the web portal was to archive, process and distribute the strong-motion waveforms recorded  by the permanent and temporary INGV accelerometric stations, in the case of earthquakes with magnitude </em><em>≥</em><em> 3.0, occurring  in the Amatrice area and surroundings.  At present (i.e. September 30<sup>th</sup>, 2016), ISMD provides more than 21.000 strong motion waveforms freely available to all users. In particular, about 2.200 strong motion waveforms were recorded by the temporary network installed for emergency in the epicentral area by SISMIKO and EMERSITO working groups. Moreover, for each permanent and temporary recording site, the web portal provide a complete description of the necessary information to properly use the strong motion data.</em></p>

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