Centroid moment tensor inversion of low-frequency seismic spectra using Green's functions for aspherical earth models

We present a new method for centroid moment tensor (CMT) inversion, in which we employ the Green's function computed for aspherical earth models using the Direct Solution Method. We apply this method to CMT inversion of low-frequency seismic spectra for the 1994 Bolivia and 1996 Flores Sea deep earthquakes. The estimated centroid locations agree well with those obtained by multiple-shock analyses using body-wave data. This shows that it is possible to obtain reliable CMT solutions by analyses of low-frequency seismic spectra using accurate Green's functions computed for present 3-D earth models.     

Source investigation of a small event using empirical Green's functions and simulated annealing

We propose a two-step inversion of three-component seismograms that (1) recovers the far-field source time function at each station and (2) estimates the distribution of co-seismic slip on the fault plane for small earthquakes (magnitude 3 to 4). The empirical Green's function (EGF) method consists of finding a small earthquake located near the one we wish to study and then performing a deconvolution to remove the path, site, and instrumental effects from the main-event signal.
The deconvolution between the two earthquakes is an unstable procedure: we have therefore developed a simulated annealing technique to recover a stable and positive source time function (STF) in the time domain at each station with an estimation of uncertainties. Given a good azimuthal coverage, we can obtain information on the directivity effect as well as on the rupture process. We propose an inversion method by simulated annealing using the STF to recover the distribution of slip on the fault plane with a constant rupture-velocity model. This method permits estimation of physical quantities on the fault plane, as well as possible identification of the real fault plane.
We apply this two-step procedure for an event of magnitude 3 recorded in the Gulf of Corinth in August 1991. A nearby event of magnitude 2 provides us with empirical Green's functions for each station. We estimate an active fault area of 0.02 to 0.15 km² and deduce a stress-drop value of 1 to 30 bar and an average slip of 0.1 to 1.6 cm. The selected fault of the main event is in good agreement with the existence of a detachment surface inferred from the tectonics of this half-graben.     

Recent Advances in Prediction and Processing of Strong Ground Motions

We present an overview of our recent results on utilizing small earthquakes in the earthquake engineering practice. Site-specific ground motion time-histories of large earthquakes can be successfully simulated using recordings of small earthquakes which are often referred to as 'empirical Green's functions' in seismology. Another important practical problem is whether and how these observations can be used in seismic risk studies which are based on empirical attenuation relations for ground motion parameters. We study a possibility of extrapolating attenuation relations for small earthquakes, to larger magnitudes using the data from the Garner Valley downhole array in Southern California. Finally we introduce efficient ground motion processing techniques in frequency- and time-domains and apply them to site response estimation.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

长螺旋钻机成孔压灌砼桩后插钢筋笼施工工艺及配套装置的应用

结合某一工程实例 ,探讨钻孔灌注桩先成桩后插钢筋笼法的施工工艺以及配套装置的工作原理、性能特点、适用范围和施工方法     

Determination of S-wave velocity structure using microtremors and spac method applied in Thessaloniki (Greece)

Array measurements of microtremors at 16 sites in the city of Thessaloniki were performed to estimate the V_s velocity of soil formations for site effect analysis. The spatial autocorrelation method was used to determine phase velocity dispersion curves in the frequency range from 0.8–1.5 to 6–7 Hz. A Rayleigh wave inversion technique (stochastic method) was subsequently applied to determine the Vs profiles at all the examined sites. The determination of V_s profiles reached a depth of 320 m. Comparisons with V_s values from cross-hole tests at the same sites proved the reliability of the SPAC method. The accuracy of the V_s profiles, the ability to reach large penetration depths in densely populated urban areas and its low cost compared to conventional geophysical prospecting, make Mictrotremor Exploration Method very attractive and useful for microzonation and site effects studies. An example of its application for the site characterization in Thessaloniki is presented herein.     

Curve-Fitting Schemes for Fragmentation Data

Fragmentation measurements in the form of sieve passing and mass fraction data were used to test the capability of three different distributions to fit the observed data over a wide range in fragment size and mass. These distributions were based on Rosin-Rammler, lognormal and simple sigmoidal (S-shaped) functions, having 2 input parameters for the single-component versions and 5 input parameters for the two-component versions. Provided convergence was achieved in the non-linear curve-fitting technique, the two-component versions always provided superior fits to the observed data. However, these versions were very sensitive to variations in the values chosen for the input parameters. In this particular regard, the two-component sigmoidal function was the most robust. The present results also show that the two-component lognormal function provided the best fit to the fragmentation data in a general sense, and the two-component Rosin-Rammler function provided the worst fit. However, there was not a significant difference between any of the three methods.     

基于正交多项式逼近法的岩土参数概率分布推断

针对岩上参数样本容量较大的情况,基于数值分析中的逼近原理,直接根据试验样本值,运用勒让德正交多项式来拟合岩土参数的概率密度函数,并用K-S检验法从理论上证明所求的密度函数的正确性和实用性。