Moment-magnitude relations based on strong-motion records in Greece

1999 ◽  
Vol 89 (2) ◽  
pp. 442-455 ◽  
Author(s):  
B. N. Margaris ◽  
C. B. Papazachos
Keyword(s):  
Strong Motion ◽  
Soil Classification ◽  
Moment Magnitude ◽  
Systematic Bias ◽  
Local Magnitude ◽  
Strong Motion Data ◽  
Strong Motion Records ◽  
Motion Data ◽  
Input Acceleration ◽  
The Difference

Abstract In this work, the variation of the local magnitude, MLSM, derived from strong-motion records at short distances is examined, in terms of moment magnitude, MW. Strong-motion data from Greek earthquakes are used to determine the strong-motion local magnitude, MLSM, by performing an integration of the equation of motion of the Wood-Anderson (WA) seismograph subjected to an input acceleration. The most reliable strong-motion data are utilized for earthquakes with seismic moments log M0 ≧ 22.0 dyne · cm and calculated local magnitudes, MLSM ≧ 3.7. The correlation between the seismic moments, log M0, and the calculated local magnitudes, MLSM, using strong-motion records is given by log M0 = 1.5*MLSM + 16.07, which is very similar to that proposed by Hanks and Kanamori (1979). Moreover, it is shown that MLSM is equal to moment magnitude, MW, for a large MLSM range (3.9 to 6.6). Comparison of the strong-motion local magnitude and the ML magnitude estimated in Greece (MLGR) and surrounding area shows a systematic bias of 0.4 to 0.5, similar to the difference that has been found between MW and MLGR for the same area. The contribution of the local site effects in the calculation of the local magnitude, MLSM, is also considered by taking into account two indices of soil classification, namely, rock and alluvium or the shear-wave velocity, v30s, of the first 30 m, based on NEHRP (1994) and UBC (1997). An increase of MLSM by 0.16 is observed for alluvium sites. Alternative relations showing the MLSM variation with, v30s are also presented. Finally, examination of the WA amplitude attenuation, −log A0, with distance shows that the Jennings and Kanamori (1983) relation for Δ < 100 km is appropriate for Greece. The same results confirm earlier suggestions that the 0.4 to 0.5 bias between MLGR and MW (also MLSM) should be attributed to a low static magnification (∼800) of the Athens WA instrument on which all other ML relations in Greece have been calibrated.

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

Empirical equations for the prediction of PGA and pseudo spectral accelerations using Iranian strong-motion data

2017 ◽  
Vol 22 (1) ◽  
pp. 263-285 ◽  
Author(s):  
H. Zafarani ◽  
Lucia Luzi ◽  
Giovanni Lanzano ◽  
M. R. Soghrat
Keyword(s):  
Strong Motion ◽  
Empirical Equations ◽  
Strong Motion Data ◽  
Motion Data ◽  
Spectral Accelerations ◽  
Pseudo Spectral

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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