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1.
Introduction The azimuth and slowness are two major features of seismic signals. The accurate estimation of them is quite important for both phase identification and event location. Generally, there are two types of seismic stations, i.e. 3-component stations (3C) and arrays. To estimate the two direc-tional parameters, the polarization analysis (Jurkevics, 1988) is commonly used for 3C stations and the frequency-wavenumber spectrum analysis ( f-k) (Capon, 1969; Kvaerna, Doornbos, 1986) is …  相似文献   
2.
靳平  潘常洲 《地震学报》2002,24(6):617-626
介绍一种新的适合于地方遥测台网数据处理的方法,估算远震信号到达台站的方位角和慢度.该方法是根据信号在各台站上的到时与台站位置矢量在信号传播方向上的投影之间的相关性的原理.实际分析结果表明,应用该方法对地方台网的记录进行处理时可以准确地计算出信号的方位角和慢度,并能准确快捷地对地方台网记录的远震信号进行解释.   相似文献   
3.
上海地震台阵的地震定位方法   总被引:5,自引:0,他引:5  
详细介绍了上海地震台阵数据处理软件系统中的地震定位方法,用台阵的聚束方法得到地震的方位角和视慢度,根据统计得到的视慢度-震中距表推算震中距。并结合了地震台网的定位方法,由单台记录的各类主要震相从J-B走时表得到震中距,然后进行地震定位。该定位方法可对近震、远震进行定位处理,并由深震相得到震源深度。  相似文献   
4.
The contravariant components of the wave-propagation metric tensor equal half the second-order partial derivatives of the selected eigenvalue of the Christoffel matrix with respect to the slowness-vector components. The relations of the wave-propagation metric tensor to the curvature matrix and Gaussian curvature of the slowness surface and to the curvature matrix and Gaussian curvature of the ray-velocity surface are demonstrated with the help of ray-centred coordinates.  相似文献   
5.
变参考慢度Born近似傅氏偏移   总被引:1,自引:0,他引:1  
针对常规Born近似傅氏偏移方法对于剧烈横向变速介质不能精确成像的状况而提出了变参考慢度Born近似傅氏偏移,理论上解决了任意速度变化地质模型的偏移成像问题。此外,为进一步提高复杂地层的成像精度和波场延拓算子的稳定性,对散射波场的计算公式作了改进。将改进的方法运用于盐丘模型的正演和偏移试验,并与常规Born近似偏移方法相比较,可明显看出变参考慢度Born近似傅氏偏移方法在效果上要优于后者,其处理速度横向变化的能力大大增强。  相似文献   
6.
Inversion for elastic parameters in weakly anisotropic media   总被引:1,自引:0,他引:1  
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7.
8.
Summary Dispersion relations for Love and Rayleigh waves in a layer on a half-space are modified by introducing quadratic slownesses instead of velocities. The advantages of this approach are demonstrated on analytical formulae for computing the group velocity.  相似文献   
9.
In this contribution, we extend the existing theory of minimum mean squared error prediction (best prediction). This extention is motivated by the desire to be able to deal with models in which the parameter vectors have real-valued and/or integer-valued entries. New classes of predictors are introduced, based on the principle of equivariance. Equivariant prediction is developed for the real-parameter case, the integer-parameter case, and for the mixed integer/real case. The best predictors within these classes are identified, and they are shown to have a better performance than best linear (unbiased) prediction. This holds true for the mean squared error performance, as well as for the error variance performance. We show that, in the context of linear model prediction, best predictors and best estimators come in pairs. We take advantage of this property by also identifying the corresponding best estimators. All of the best equivariant estimators are shown to have a better precision than the best linear unbiased estimator. Although no restrictions are placed on the probability distributions of the random vectors, the Gaussian case is derived separately. The best predictors are also compared with least-squares predictors, in particular with the integer-based least-squares predictor introduced in Teunissen (J Geodesy, in press, 2006).  相似文献   
10.
In climate models, the land–atmosphere interactions are described numerically by land surface parameterization (LSP) schemes. The continuing improvement in realism in these schemes comes at the expense of the need to specify a large number of parameters that are either directly measured or estimated. Also, an emerging problem is whether the relationships used in LSPs are universal and globally applicable. One plausible approach to evaluate this is to first minimize uncertainty in model parameters by calibration. In this paper, we conduct a comprehensive analysis of some model diagnostics using a slightly modified version of the Simple Biosphere 3 model for a variety of biomes located mainly in the Amazon. First, the degree of influence of each individual parameter in simulating surface fluxes is identified. Next, we estimate parameters using a multi‐operator genetic algorithm applied in a multi‐objective context and evaluate simulations of energy and carbon fluxes against observations. Compared with the default parameter sets, these parameter estimates improve the partitioning of energy fluxes in forest and cropland sites and provide better simulations of daytime increases in assimilation of net carbon during the dry season at forest sites. Finally, a detailed assessment of the parameter estimation problem was performed by accounting for the decomposition of the mean squared error to the total model uncertainty. Analysis of the total prediction uncertainty reveals that the parameter adjustments significantly improve reproduction of the mean and variability of the flux time series at all sites and generally remove seasonality of the errors but do not improve dynamical properties. Our results demonstrate that error decomposition provides a meaningful and intuitive way to understand differences in model performance. To make further advancements in the knowledge of these models, we encourage the LSP community to adopt similar approaches in the future. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   
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