Frequency dependent attenuation of strong earthquake ground motion

Trifunac, Mihailo D.; Lee, V. W.

doi:10.1016/s0267-7261(09)90004-5

Cited by 63 publications

(27 citation statements)

References 9 publications

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“…To implement the approach described, a dedicated code (SSK) has been developed (Gusev & Petukhin 1995, 1996) and a brief description of it is given in the Appendix. In a number of aspects, our method of data analysis follows the approaches of Trifunac (1976) and Trifunac & Lee (1990).…”

Section: Simple Theory Versus Empirical Formulae In Strong‐motion Damentioning

confidence: 99%

“…For practical use, we replace R s by the effective source radius R eff , defined as ( S / π ) 0.5 , where S = LW is the source area. The formula was successfully tested against real earthquake data by Trifunac & Lee (1990), who compared it to three other formulae and found it superior in describing the near‐source effects contained in the western USA accelerograms set, and by Ohno et al . (1993), who performed a more detailed check against near‐source data of two well‐recorded earthquakes.…”

Section: Appendix A:short Description Of the Algorithm Of The Ssk Codmentioning

confidence: 99%

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Preliminary determination of the interdependence among strong-motion amplitude, earthquake magnitude and hypocentral distance for the Himalayan region

et al. 2001

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SUMMAR YSince the installation of three limited-aperture strong-motion networks in the Himalayan region in 1986, six earthquakes with M w =5.2±7.2 have been recorded up to 1991. The data set of horizontal peak accelerations and velocities consists of 182-component data for the hypocentral distance range 10±400 km. This data set is limited in volume and coverage and, worst of all, it is highly inhomogeneous. Thus, we could not determine regional trends for amplitudes by means of the traditional approach of empirical multiple regression. Instead, we perform the reduction of the observations to a ®xed distance and magnitude using independently de®ned distance and magnitude trends. To determine an appropriate magnitude-dependent distance attenuation law, we use the spectral energy propagation/random function approach of Gusev (1983) and adjust its parameters based on the residual variance. In doing so we con®rm the known, rather gradual mode of decay of amplitudes with distance in the Himalayas; this seems to be caused by the combination of high Qs and crustal waveguide effects for high frequencies.The data are then reduced with respect to magnitude. The trend of peak acceleration versus magnitude cannot be determined from observations, and we assume that it coincides with that of abundant Japanese data. For the resulting set of reduced log 10 (peak acceleration) data, the residual variance is 0.37 2 , much above commonly found values. However, dividing the data into two geographical groups, western with two events and eastern with four events, reduces the residual variance to a more usual level of 0.27 2 (a station/site component of 0.22 2 and an event component of 0.16 2 ). This kind of data description is considered acceptable. A similar analysis is performed with velocity data, and again we have to split the data into two subregional groups. With our theoretically grounded attenuation laws we attempt a tentative extrapolation of our results to small distances and large magnitudes. Our minimum estimates of peak acceleration for the epicentral zone of M w =7.5±8.5 events is A peak =0.25±0.4 g for the western Himalayas, and as large as A peak =1±1.6 g for the eastern Himalayas. Similarly, the expected minimum epicentral values of V peak for M w =8 are 35 cm s x1 for the western and 112 cm s x1 for the eastern Himalayas. To understand whether our results re¯ect the properties of the subregions and not of a small data set, we check them against macroseismic intensity data for the same subregion. The presence of unusually high levels of epicentral amplitudes for the eastern subregion agrees well with the macroseismic evidence such as the epicentral intensity levels of X±XII for the Great Assam

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Section: Simple Theory Versus Empirical Formulae In Strong‐motion Damentioning

confidence: 99%

Section: Appendix A:short Description Of the Algorithm Of The Ssk Codmentioning

confidence: 99%

Preliminary determination of the interdependence among strong-motion amplitude, earthquake magnitude and hypocentral distance for the Himalayan region

et al. 2001

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“…Use of the formula after GUSEV (1983) to allow for this effect seems justified for the present study. Its applicability for a strong-motion FSA data analysis has been verified by TRIFUNAC and LEE (1990) in a special study.…”

Section: Data Analysis: General Approach and Distance Reductionmentioning

confidence: 83%

Preliminary Model for Scaling of Fourier Spectra of Strong Ground Motion Recorded on Kamchatka

Petukhin

Gusev

Guseva

et al. 1999

Pure and Applied Geophysics

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Abstract-To determine the average relationship among the Fourier spectrum of horizontal acceleration FSA(f ), moment magnitude M W and hypocentral distance R for Kamchatka earthquakes, we analyzed 44 analog strong-motion records recorded here in 1969 -1993. The records of acceleration and velocity meters were obtained at 11 rock to medium-ground sites from 36 earthquakes with M W = 4.5-7.8, at distances R =30-250 km and depths 0 -80 km. Amplitude spectra FSA(f ) were calculated from digitized, baseline corrected records of 81 horizontal components, and then divided by instrumental transfer function. After smoothing the values were picked at a set of fixed frequencies. With the scarce amount of data at hand it was impossible to determine reliably the entire FSA(M W , R f ) average trend surface. Hence we first performed distance equalization with distance corrections calculated on a theoretical basis, and thus reduced the observed data to the reference distance of R 0 =100 km. The model of distance attenuation applied included point source decay terms (1/R plus attenuation specified by Q(f )= 250 f 0.8 ) and finite source correction (using the formula for a disc-shaped incoherent source, its size depending on M W ); its general applicability was later checked by analysis of residuals. After reduction we determined the FSA(M W , R 0 f ) vs. M W trends. To do this we employed a multiple regression procedure with ground type and station dummy variables. The M W dependence was assumed to consist of two linear branches intersecting at M W =6.5. The result of multiple regression represents the first systematic description of spectral properties of destructive ground motion for Kamchatka earthquakes. The empirical FSA vs. M W trend flattens as frequency increases. This flattening persists even between 3 and 16 Hz, suggesting the decrease of source-related f max with increasing magnitude.

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“…Within the recordable range, the spectrum is given by empirical scaling models defined by regression analyses of strong motion data. All of the models use a frequency-dependent attenuation function for strong ground motion and representative source to station distance (Trifunac and Lee 1990), and they differ in the set of parameters describing the effects of the local site conditions (e.g., some models use simultaneously the geologic site characterization and the local soil characterization, whereas others use only the geologic site characterization; the models also differ in whether the geologic site classification is in terms of the categorical variable s or in terms of thickness of sediments h). Outside the recordable frequency band, the spectrum is defined based on the behavior of theoretical Fourier spectrum, and it is calibrated by data on the average fault slip and highfrequency attenuation characteristics of the medium, measured independently.…”

Section: Energy Methods and ''En'' Modelmentioning

confidence: 99%

Liquefaction Opportunity Mapping via Seismic Wave Energy

Todorovska

Trifunac

1999

J. Geotech. Geoenviron. Eng.

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An empirical, energy-based methodology for liquefaction hazard assessment and microzonation mapping is presented. The approach is probabilistic, considers the uncertainty in the liquefaction criterion, and is applicable to most earthquake-induced liquefaction analyses. The examples illustrated are for water-saturated sands at level ground. The energy of ground shaking is estimated from the Fourier amplitude spectra of the incident waves. The susceptible materials are characterized only by their corrected standard penetration test value N and overburden pressure 0. Illustrative microzonation maps of liquefaction opportunity are shown for the Los Angeles metropolitan area. Two types of maps are presented, one showing the average return period of liquefaction occurrence (for given N and 0), and another one showing distribution of N with equal probability to liquefy during 50 years exposure (for given 0). An advantage of the method is that the result is given directly in terms of in situ albeit simple soil characteristics rather than in terms of laboratory tests and peak acceleration. Possible applications of the computed opportunity maps are discussed.

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Frequency dependent attenuation of strong earthquake ground motion

Cited by 63 publications

References 9 publications

Preliminary determination of the interdependence among strong-motion amplitude, earthquake magnitude and hypocentral distance for the Himalayan region

Preliminary determination of the interdependence among strong-motion amplitude, earthquake magnitude and hypocentral distance for the Himalayan region

Preliminary Model for Scaling of Fourier Spectra of Strong Ground Motion Recorded on Kamchatka

Liquefaction Opportunity Mapping via Seismic Wave Energy

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