工程随机地震动物理模型的参数统计与检验

利用PEER强震记录数据库,确定了一类实用的随机地震动模型的参数概率分布。依据GB50011-2010规定的场地类别,对4438条实测地震动记录进行分组。引用系统识别方法对不同场地上的地震动记录进行参数识别,据此结果,对工程地震动物理随机函数模型的基本参数进行了统计分析,给出了随机地震动模型参数的概率分布密度。对基本随机参数的概率空间进行剖分,结合波群叠加方法生成地震动时程,计算获得了随机地震动反应谱。通过比较随机反应谱和实测地震动反应谱的统计特征量,验证了地震动物理随机函数模型及基本随机参数统计结果的正确性。     

基于相位差谱的时─频非平稳人造地震动的生成

本文首先考察了地震动加速度时程在时域和频域上的非平稳性，通过实例分析说明地震动加速度时程 的非平稳性不能由相位谱的概率分布唯一决定，进而阐明了相位差谱是影响地震动非平稳的决定性因素。经 统计检验确定了脉动相位差的概率分布模型，利用相位差谱的数字特征与地震特性参数之间的统计关系，给 出了基于相位差谱的地震动时程生成方法。最后，通过对计算实例的分析，证实了此方法能够反映并模拟实 际地震动的时─频非平稳性。     

基于相位差谱的时-频非平稳人造地震动的生成

本文首先考察了地震动加速度时程在时域和频域上的非平稳性，通过实例分析说明地震动加速度时程的非平稳性不能由相位谱的概率分布唯一决定，进而阐明了相位差谱是影响地震动非平稳的决定性因素。经统计检验确定了脉动相位差的概率分布模型，利用相位差谱的数字特征与地震特性参数之间的统计关系，给出了基于相位差谱的地震动时程生成方法。最后，通过对计算实例的分析，证实了此方法能够反映并模拟实际地震动的时－频非平稳性。     

随机模型上的地震动模拟研究

介绍了一项随机模拟实验,该实验通过模拟一个具体场址模型的地震动响应了解模型参数不确定性对模拟结果的影响。首先给定一个典型的第四纪沉积层场址模型的参数(包括地层厚度、介质密度、横波速度和品质因子等)的统计特征,并据此在计算机上按截尾的正态分布随机抽样形成了16384个随机模型,然后分别在各个模型上进行SH波地震动响应模拟,最后对所有的模拟结果进行统计分析,了解模型参数的不确定程度与模拟结果变化范围之间的关系。实验结果表明,模型参数不确定性对模拟结果的影响程度是随频率增大的。随机模型地震动响应的第一个放大峰发生在353±031Hz的频率上,其幅值为438±076;第二放大峰发生在885±108Hz的频率上,幅值为422±090。两放大峰值的均方差与均值之比分别为18%和25%。与模型参数20%的相对变化程度大致相当。但更高频率上振幅响应的均方差与均值之比则高达30%～40%。     

地震动强度非平稳特性参数与结构响应之间的近似定量关系分析

本文在对实际地震加速度记录统计分析的基础上,给出了能够合理描述地震动强度非平稳特性的参数及其取值范围;然后引入实验设计方法,建立了适合于地震动强度非平稳特性参数分析的实验设计算法,用来分析地震动强度非平稳特性参数的变化对结构响应的影响;最后通过与近似技术相结合,建立了地震动强度非平稳特性参数与结构响应之间的近似定量关系模型.结果表明,本文提出的实验设计方法适合于对地震动强度非平稳特性参数进行分析,该方法在有效地减小计算量的同时,获得了结构响应与参数变化之间的对应关系.基于实验设计方法进行的特性参数方差分析结果表明:地震动的稳态持时对结构地震响应的影响比较显著;对于周期较小的结构,特性参数之间的交互作用对结构地震响应的影响显著,但当周期大于1 s时,则不显著.本文建立的近似定量关系模型能够较好地反映不同特性参数、不同周期结构动力响应之间的联系,为工程实践中基于结构特性合理设置地震动特性参数、合成或挑选地震加速度时程提供理论依据.      

地震动随机函数模型研究(Ⅱ)——参数统计与模型验证

依场地类别进行了强震记录分组,对模型参数的变化规律进行了统计分析.在模型随机参数向量满足独立性假设的前提下,得到了地震动随机函数模型的联合概率密度函数.引入数论选点方法对地震动随机函数模型的概率空间进行剖分,可以较少的样本点描述概率空间.以所选模型参数代表点代入地震动随机函数模型,即可以得到地震动时程样本集合.在集合层次上对比了模型预测地震动与真实记录的差异,两者在均值谱和标准差谱层次上均吻合较好,证实了模型预测结果的合理性.     

众值烈度作用下基于抗震结构响应等效的地震动模型研究

运用结构响应等效的思想，对地震动模型进行了研究。在用Ｍｏｎｔｅ－Ｃａｒｌｏ地求取大量结构在众值烈度作用下弹性时程分析的响应统计特征的基础上，将地震地面运动等效成了形式简单的简谐激励模型，同时在频域上为随机地震模型－Ｋａｎａｉ模型确定了有明显意义的模型参数。     

强地震动的表征

一些水下滑坡是由地震引起的强地震动触发的。本文评述了有关强地震动描述的当前概念及其发震趋势。改进的经验地震动模型是由强震数据集导出的，这方面的数据在近10年也得到了显著的充实。然而，由于这些模型中的震级一距离一场地分类的参数化常常过于简化实际情况，因此这些经验模型具有很大程度的不确定性。这表现在一些已知的、对强地震动具有很大影响的其他条件并没有作为这些简单模型的参数，例如近断层破裂的方向性效应、地壳波导效应以及盆地响应效应。基于地震学理论的包含这些附加效应的数字地动模型已经建立，并且从已有的地震动记录中得以广泛验证。它们可以用来评估过去地震的地震动，也可以用来预测未来潜在地震的地震动。     

脉冲型地震动潜在破坏势参数的相关性分析

为了在众多参数中挑选其中最有代表性的参数,来解释和反映脉冲型地震动对结构的潜在破坏能力,以338条脉冲型地震动记录作为研究对象,分析地震动参数与中低层结构响应的相关性。选取了14个常用地震动参数,对各地震动参数之间的相关性进行分析,从中选出7个代表性地震动参数;并将脉冲型地震动输入中低层结构模型中计算结构响应,分析代表性地震动参数与结构响应的相关性,与基于非脉冲型地震动的相关性计算结果进行对比。选用了3层和7层2个RC框架结构作为中低层结构代表,其基本周期为0.62s和0.89s。结果表明:对于脉冲型地震动,对于3层结构时与结构响应相关性最好的为EPV,对于7层结构时与结构响应相关性最好的为PGV,因此可以用PGV和EPV作为表征脉冲型地震动对中低层结构潜在破坏能力的参数;而对于非脉冲型地震动,与结构响应相关性最好的参数为PGV,可以用PGV作为表征脉冲型地震动对中低层结构的潜在破坏能力的参数。因此,通过地震动参数来解释和表征脉冲型地震动对结构的破坏能力是可行的。     

基于随机Fourier谱的地震动合成研究

本文基于与物理随机模型相对应的随机Fourier谱,通过对地震动非平稳性及其所受影响机制的分析,建立了非平稳地震动合成的新方法。通过引入基本相位差谱,并将初始相角视为随机变量,以相位差谱的分布特性、随机地震动的统计特征以及对结构随机反应的影响为原则,利用计算机程序生成相位差谱,提出了基于随机Fourier谱的合成地震动方法。同时,利用快速FFT技术提高合成精度。根据本文提出的合成方法获得的地震动具有非平稳特性,将为后续研究工作提供合理的地震动加速度时程。     

Stochastic Lagrangian trajectory model for drifting objects in the ocean

The prediction of drifting object trajectories in the ocean is a complex problem plagued with uncertainties. This problem is usually solved simulating the possible trajectories based on wind and advective numerical and/or instrumental data in real time, which are incorporated into Lagrangian trajectory models. However, both data and Lagrangian models are approximations of reality and when comparing trajectory data collected from drifter exercises with respect to Lagrangian models results, they differ considerably. This paper introduces a stochastic Lagrangian trajectory model that allows quantifying the uncertainties related to: (i) the wind and currents numerical and/or instrumental data, and (ii) the Lagrangian trajectory model. These uncertainties are accounted for within the model through random model parameters. The quantification of these uncertainties consists in an estimation problem, where the parameters of the probability distribution functions of the random variables are estimated based on drifter exercise data. Particularly, it is assumed that estimated parameters maximize the likelihood of our model to reproduce the trajectories from the exercise. Once the probability distribution parameters are estimated, they can be used to simulate different trajectories, obtaining location probability density functions at different times. The advantage of this method is that it allows: (i) site specific calibration, and (ii) comparing uncertainties related to different wind and currents predictive tools. The proposed method is applied to data collected during the DRIFTER Project (eranet AMPERA, VI Programa Marco), showing very good predictive skills.     

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.     

基于FFT-MA的随机介质建模方法

随机介质是描述地球介质小尺度非均匀性的有效模型,在地震散射波场分析、储层描述等领域具有广泛应用,快速准确的随机介质建模方法是开展相关研究的基础与前提.本文首次将FFT-MA（Fast Fourier Transform Moving Average）算法引入到随机介质建模研究中,分析了该方法与传统随机介质建模方法相比具有的优势,并提出了基于FFT-MA的非平稳随机介质建模方法.建模实验表明,与传统的基于谱分解定理的随机介质建模方法相比,基于FFT-MA的方法在空间域产生随机数序列,使随机数序列与结构参数分离,因此,随机数序列与所建模型存在空间上的对应关系,可以分区域建模和局部修改模型.在非平稳随机介质模型建模时,滑动窗口的大小可以根据自相关长度变化而变化,避免了每个采样点都建立一次完整大小的模型,提高了建模效率.因此,FFT-MA随机介质建模方法能准确构建满足自相关函数要求的平稳及非平稳随机介质模型,具有建模效率高、灵活、实用的优点.     

Stochastic modeling of the permeability of randomly generated porous media

Permeability of porous media in subsurface environments is subject to potentially large uncertainties due to the heterogeneity of natural systems. In this study, a first-order reliability method (FORM) is combined with a lattice Boltzmann method (LBM) to estimate the permeability of randomly generated porous media. The proposed procedure provides an increased ease of addressing complex pore structures by employing LBM to model fluid flow, while inheriting the computational efficiency from FORM. Macroscale-equivalent permeability can thus be estimated with significantly reduced computational efforts, while maintaining a connection to the complex microscale fluid dynamics within a pore structure environment. Implemented on several randomly generated porous media domains, the proposed method provides 13–120 times the efficiency compared to Monte Carlo methods.     

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another “equivalent” sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.     

利用地质规则块体建模方法的频率域有限元弹性波速度反演

在频率域弹性波有限元正演方程的基础上,依据匹配函数（也就是观测数据和正演数据残差的二次范数）最小的准则,用矩阵压缩存储与LU分解技术来存储和求解频率域正演方程中的大型稀疏复系数矩阵、用可调阻尼因子的Levenberg Marquard方法求解反演方程组,直接求取地下介质的弹性波速度,导出了频率域弹性波有限元最小二乘反演算法. 为了利用地下地质体的分布规律,减少反演所求的未知数个数,本文又提出了规则地质块体建模方法引入到反演中来. 经数值模型验证,在噪声干扰很大（噪声达到50髎）或初始模型与真实模型相差很大的情况下,反演也能取得很满意的效果,证明本方法具有很好的抗噪性与“强壮性”.     

Fair function minimization for interpretation of magnetic anomalies due to thin dikes, spheres and faults

A new method is proposed to interpret magnetic anomalies due to a thin dike, a sphere, and a fault like structure, where depth, horizontal location, effective magnetization intensity and effective magnetization inclination of a buried structure are simultaneously obtained. The proposed method is based on Fair function minimization and also on stochastic optimization modeling. This new technique was firstly tested on a theoretical synthetic data randomly generated by a chosen statistical distribution from a known model with different random noises components. This mathematical simulation shows a very close agreement between the assumed and the estimated parameters. The applicability and validity of this method are thereafter applied to magnetic anomaly data taken from United States, Australia, India, and Brazil. The agreement between the results obtained by the new method and those obtained by other interpretative methods is good and comparable. Moreover, the depth obtained by such a method is found to be in high accordance with that obtained from drilling information.     

Probabilistic evaluation of soil–foundation–structure interaction effects on seismic structural response

Complex seismic behaviour of soil–foundation–structure (SFS) systems together with uncertainties in system parameters and variability in earthquake ground motions result in a significant debate over the effects of soil–foundation–structure interaction (SFSI) on structural response. The aim of this study is to evaluate the influence of foundation flexibility on the structural seismic response by considering the variability in the system and uncertainties in the ground motion characteristics through comprehensive numerical simulations. An established rheological soil‐shallow foundation–structure model with equivalent linear soil behaviour and nonlinear behaviour of the superstructure has been used. A large number of models incorporating wide range of soil, foundation and structural parameters were generated using a robust Monte‐Carlo simulation. In total, 4.08 million time‐history analyses were performed over the adopted models using an ensemble of 40 earthquake ground motions as seismic input. The results of the analyses are used to rigorously quantify the effects of foundation flexibility on the structural distortion and total displacement of the superstructure through comparisons between the responses of SFS models and corresponding fixed‐base (FB) models. The effects of predominant period of the FB system, linear vs nonlinear modelling of the superstructure, type of nonlinear model used and key system parameters are quantified in terms of different probability levels for SFSI effects to cause an increase in the structural response and the level of amplification of the response in such cases. The results clearly illustrate the risk of underestimating the structural response associated with simplified approaches in which SFSI and nonlinear effects are ignored. Copyright © 2010 John Wiley & Sons, Ltd.     

Reservoir parameter inversion based on weighted statistics

Variation of reservoir physical properties can cause changes in its elastic parameters. However, this is not a simple linear relation. Furthermore, the lack of observations, data overlap, noise interference, and idealized models increases the uncertainties of the inversion result. Thus, we propose an inversion method that is different from traditional statistical rock physics modeling. First, we use deterministic and stochastic rock physics models considering the uncertainties of elastic parameters obtained by prestack seismic inversion and introduce weighting coefficients to establish a weighted statistical relation between reservoir and elastic parameters. Second, based on the weighted statistical relation, we use Markov chain Monte Carlo simulations to generate the random joint distribution space of reservoir and elastic parameters that serves as a sample solution space of an objective function. Finally, we propose a fast solution criterion to maximize the posterior probability density and obtain reservoir parameters. The method has high efficiency and application potential.     

Effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data

This paper investigates the effects of uncertainty in rock-physics models on reservoir parameter estimation using seismic amplitude variation with angle and controlled-source electromagnetics data. The reservoir parameters are related to electrical resistivity by the Poupon model and to elastic moduli and density by the Xu-White model. To handle uncertainty in the rock-physics models, we consider their outputs to be random functions with modes or means given by the predictions of those rock-physics models and we consider the parameters of the rock-physics models to be random variables defined by specified probability distributions. Using a Bayesian framework and Markov Chain Monte Carlo sampling methods, we are able to obtain estimates of reservoir parameters and information on the uncertainty in the estimation. The developed method is applied to a synthetic case study based on a layered reservoir model and the results show that uncertainty in both rock-physics models and in their parameters may have significant effects on reservoir parameter estimation. When the biases in rock-physics models and in their associated parameters are unknown, conventional joint inversion approaches, which consider rock-physics models as deterministic functions and the model parameters as fixed values, may produce misleading results. The developed stochastic method in this study provides an integrated approach for quantifying how uncertainty and biases in rock-physics models and in their associated parameters affect the estimates of reservoir parameters and therefore is a more robust method for reservoir parameter estimation.