This paper presents results recently obtained for generating site-specific ground motions needed for design of critical facilities. The general approach followed in developing these ground motions using either deterministic or probabilistic criteria is specification of motions for rock outcrop or very firm soil conditions followed by adjustments for site-specific conditions. Central issues in this process include development of appropriate attenuation relations and their uncertainties, differences in expected motions between Western and Eastern North America, and incorporation of site-specific adjustments that maintain the same hazard level as the control motions, while incorporating uncertainties in local dynamic material properties. For tectonically active regions, such as the Western United States (WUS), sufficient strong motion data exist to constrain empirical attenuation relations for M up to about 7 and for distances greater than about 10–15 km. Motions for larger magnitudes and closer distances are largely driven by extrapolations of empirical relations and uncertainties need to be substantially increased for these cases.
For the Eastern United States (CEUS), due to the paucity of strong motion data for cratonic regions worldwide, estimation of strong ground motions for engineering design is based entirely on calibrated models. The models are usually calibrated and validated in the WUS where sufficient strong motion data are available and then recalibrated for applications to the CEUS. Recalibration generally entails revising parameters based on available CEUS ground motion data as well as indirect inferences through intensity observations. Known differences in model parameters such as crustal structure between WUS and CEUS are generally accommodated as well. These procedures are examined and discussed. 相似文献
The frequency-dependent attenuation of seismic waves causes decreased resolution of seismic images with depth, and the difference in transmission losses induces amplitude variations with offset. Transmission losses may occur due to friction or fluid movement, or may result from scattering in thin-layer. Whatever the physical mechanism, they can often be conveniently described using an empirical formulation wherein the elastic moduli and propagation velocity are complex functions of frequency.We have compiled and compared algebraically and numerically eight different models involving complex velocity: the Kolsky-Futterman model, the power-law model, Kjartansson's model, Müller's model, Azimi's second and third model, the Cole-Cole model, and the standard linear-solid model.For two different parameter sets, the attenuation and phase velocity are computed in the seismic frequency band, and the plane-wave propagation of a Ricker wavelet for the other models is compared with that for the Kolsky-Futterman model. The first parameter set consists of parameters for each of the models calculated from expressions given in the appendix. These expressions make the different models behave similarly to the KF model. The second parameter set consists of model parameters that are numerically adapted to the KF model.By selecting proper parameters, all models, except the standard linear-solid model, show behavior similar to that of the Kolsky-Futterman model. The SLS model behaves differently from the other models as the frequency goes to zero or infinity. Broadband measurement data is needed to select a specific model for a given seismic experiment. 相似文献
Reservoir models have large uncertainty because of spatial variability and limited sample data. The ultimate aim is to use simultaneously all available data sources to reduce uncertainty and provide reliable reservoir models for resource assessment and flow simulation. Seismic impedance or some other attribute provides a key source of data for reservoir modeling. These seismic data are at a coarser scale than the hard well data and it not an exact measurement of facies proportions or porosity. A requirement for data integration is the cross-covariance between the well and seismic data.The size-scaling behavior of the cross correlation for different measurement scales was nvestigated. The size-scaling relationship is derived theoretically and validated by numerical studies (including an example with real data). The limit properties of the cross-correlation coefficient when the averaging volume becomes large is shown. After some averaging volume, the volume-dependent cross-correlation coefficient reaches a limit value. This plateau value is controlled mainly by the large-scale behavior of the cross and direct variograms.The cross correlation can increase or decrease with volume support depending on the relative importance of long- and short-scale covariance structures. If the direct and cross variograms are proportional, there is no change in the cross correlation as the averaging volume changes. Our study shows that the volume-dependent cross-correlation coefficient is sensitive to the shape of the cross variogram and differences between the direct variograms of the well data and seismic data. 相似文献