Estimation of rupture propagation direction and strong motion generation area from azimuth and distance dependence of source amplitude spectra

2001 ◽  
Vol 28 (14) ◽  
pp. 2727-2730 ◽  
Author(s):  
Hiroe Miyake ◽  
Tomotaka Iwata ◽  
Kojiro Irikura
Keyword(s):  
Strong Motion ◽  
Propagation Direction ◽  
Rupture Propagation ◽  
Motion Generation ◽  
Strong Motion Generation Area ◽  
Distance Dependence ◽  
Amplitude Spectra

Modeling of strong motion generation area of the Uttarkashi earthquake using modified semiempirical approach

2014 ◽  
Vol 73 (3) ◽  
pp. 2041-2066 ◽  
Author(s):  
Sandeep ◽  
A. Joshi ◽  
Kamal ◽  
Parveen Kumar ◽  
Pushpa Kumari
Keyword(s):  
Strong Motion ◽  
Motion Generation ◽  
Strong Motion Generation Area ◽  
Semiempirical Approach

scholarly journals Strong motion generation area modelling of the 2008 Iwate earthquake, Japan using modified semi-empirical technique

2019 ◽  
Vol 128 (8) ◽  
Author(s):  
Sandeep ◽  
A Joshi ◽  
Sonia Devi ◽  
Parveen Kumar ◽  
S K Sah ◽  
...  
Keyword(s):  
Strong Motion ◽  
Motion Generation ◽  
Strong Motion Generation Area ◽  
Semi Empirical

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.

Approximate method to estimate the short-period strong motion spectra on bedrock

1981 ◽  
Vol 71 (2) ◽  
pp. 491-505
Author(s):  
Katsuhiko Ishida
Keyword(s):  
Earthquake Swarm ◽  
Strong Motion ◽  
Fourier Amplitude ◽  
Period Range ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Short Period ◽  
Amplitude Spectra ◽  
Low Pass ◽  
Parkfield Earthquake

abstract The methodology to estimate the strong motion Fourier amplitude spectra in a short-period range (T ≦ 1 to 2 sec) on a bedrock level is discussed in this paper. The basic idea is that the synthetic strong motion Fourier spectrum F˜A(ω) calculated from smoothed rupture velocity model (Savage, 1972) is approximately similar to that of low-pass-filtered strong earthquake ground motion at a site in a period range T ≧ 1 to 2 sec: F˜A(ω)=B˜(ω)·A(ω). B˜(ω) is an observed Fourier spectrum on a bedrock level and A(ω) is a low-pass filter. As a low-pass filter, the following relation, A ( T ) = · a · T n a T n + 1 , ( T = 2 π / ω ) , is assumed. In order to estimate the characteristic coefficients {n} and {a}, the Tokachi-Oki earthquake (1968), the Parkfield earthquake (1966), and the Matsushiro earthquake swarm (1966) were analyzed. The results obtained indicate that: (1) the coefficient {n} is nearly two for three earthquakes, and {a} is nearly one for the Tokachi-Oki earthquake, eight for the Parkfield earthquake, and four for the Matsushiro earthquake swarm, respectively; (2) the coefficient {a} is related with stress drop Δσ as (a = 0.07.Δσ). Using this relationship between {a} and Δσ, the coefficients {a} of past large earthquakes were estimated. The Fourier amplitude spectra on a bedrock level are also estimated using an inverse filtering method of A ( T ) = a T 2 a T 2 + 1 .

Peak horizontal acceleration and velocity from strong-motion records including records from the 1979 imperial valley, California, earthquake

1981 ◽  
Vol 71 (6) ◽  
pp. 2011-2038 ◽  
Author(s):  
William B. Joyner ◽  
David M. Boore
Keyword(s):  
Strong Motion ◽  
Fault Rupture ◽  
Imperial Valley ◽  
Attenuation Relations ◽  
Motion Data ◽  
Horizontal Acceleration ◽  
Anelastic Attenuation ◽  
Distance Dependence ◽  
Peak Horizontal Acceleration

Abstract We have taken advantage of the recent increase in strong-motion data at close distances to derive new attenuation relations for peak horizontal acceleration and velocity. This new analysis uses a magnitude-independent shape, based on geometrical spreading and anelastic attenuation, for the attenuation curve. An innovation in technique is introduced that decouples the determination of the distance dependence of the data from the magnitude dependence. The resulting equations are log A = − 1.02 + 0.249 M − log r − 0.00255 r + 0.26 P r = ( d 2 + 7.3 2 ) 1 / 2 5.0 ≦ M ≦ 7.7 log V = − 0.67 + 0.489 M − log r − 0.00256 r + 0.17 S + 0.22 P r = ( d 2 + 4.0 2 ) 1 / 2 5.3 ≦ M ≦ 7.4 where A is peak horizontal acceleration in g, V is peak horizontal velocity in cm/ sec, M is moment magnitude, d is the closest distance to the surface projection of the fault rupture in km, S takes on the value of zero at rock sites and one at soil sites, and P is zero for 50 percentile values and one for 84 percentile values. We considered a magnitude-dependent shape, but we find no basis for it in the data; we have adopted the magnitude-independent shape because it requires fewer parameters.

Sign in / Sign up

Export Citation Format

Share Document