A generalized conditional intensity measure approach and holistic ground‐motion selection

A generalized conditional intensity measure (GCIM) approach is proposed for use in the holistic selection of ground motions for any form of seismic response analysis. The essence of the method is the construction of the multivariate distribution of any set of ground‐motion intensity measures conditioned on the occurrence of a specific ground‐motion intensity measure (commonly obtained from probabilistic seismic hazard analysis). The approach therefore allows any number of ground‐motion intensity measures identified as important in a particular seismic response problem to be considered. A holistic method of ground‐motion selection is also proposed based on the statistical comparison, for each intensity measure, of the empirical distribution of the ground‐motion suite with the ‘target’ GCIM distribution. A simple procedure to estimate the magnitude of potential bias in the results of seismic response analyses when the ground‐motion suite does not conform to the GCIM distribution is also demonstrated. The combination of these three features of the approach make it entirely holistic in that: any level of complexity in ground‐motion selection for any seismic response analysis can be exercised; users explicitly understand the simplifications made in the selected suite of ground motions; and an approximate estimate of any bias associated with such simplifications is obtained. Copyright © 2010 John Wiley & Sons, Ltd.     

A ground motion selection algorithm based on the generalized conditional intensity measure approach

An algorithm is presented for the selection of ground motions for use in seismic response analysis. The algorithm is based on the use of random realizations from the conditional multivariate distribution of ground motion intensity measures, IM|IM_j, obtained from the generalized conditional intensity measure (GCIM) approach. The algorithm can be applied to the selection of both as-recorded amplitude-scaled and synthetic/simulated ground motions. A key feature is that the generality of the GCIM methodology allows for ground motion selection based on only explicit measures of the ground motions themselves, as represented by the various IM’s considered, rather than implicit causal parameters (e.g., source magnitude, source-to-site distance) which are presently used in other contemporary ground motion selection procedures. Several examples are used to illustrate the salient features of the algorithm, including: the effect of intensity measures considered; and the properties of ground motions selected for multiple exceedance probabilities. The flexibility of the proposed algorithm coupled with the GCIM methodology allows for objective and consistent ground motion selection as a natural extension of seismic hazard analysis.     

基于局部采样MCMC方法的时移探地雷达反演

马尔科夫链蒙特卡洛方法(MCMC)是一种启发式的全局寻优算法,可以用来解决概率反演的问题.基于MCMC方法的反演不依赖于准确的初始模型,可以引入任意复杂的先验信息,通过对先验概率密度函数的采样来获得大量的后验概率分布样本,在寻找最优解的过程中可以跳出局部最优得到全局最优解.MCMC方法由于计算量巨大,应用难度较高,在地...     

Spectral shape,epsilon and record selection

Selection of earthquake ground motions is considered with the goal of accurately estimating the response of a structure at a specified ground motion intensity, as measured by spectral acceleration at the first‐mode period of the structure, Sa(T₁). Consideration is given to the magnitude, distance and epsilon (ε) values of ground motions. First, it is seen that selecting records based on their ε values is more effective than selecting records based on magnitude and distance. Second, a method is discussed for finding the conditional response spectrum of a ground motion, given a level of Sa(T₁) and its associated mean (disaggregation‐based) causal magnitude, distance and ε value. Records can then be selected to match the mean of this target spectrum, and the same benefits are achieved as when records are selected based on ε. This mean target spectrum differs from a Uniform Hazard Spectrum, and it is argued that this new spectrum is a more appropriate target for record selection. When properly selecting records based on either spectral shape or ε, the reductions in bias and variance of resulting structural response estimates are comparable to the reductions achieved by using a vector‐valued measure of earthquake intensity. Copyright © 2006 John Wiley & Sons, Ltd.     

Ground motion selection for scenario ruptures using the generalised conditional intensity measure (GCIM) method

In this paper, the generalised conditional intensity measure (GCIM) method is extended to ground motion selection for scenario earthquake ruptures. The selection algorithm is based on generating random realisations of the considered intensity measure (IM) distributions for a specific rupture scenario and then finding the prospective ground motions that best fit the realisations using an optimal amplitude scale factor. Using different rupture scenarios and site conditions, two important aspects of the GCIM methodology are scrutinised: (i) different weight vectors for the various IMs considered and (ii) quantifying the importance of replicate selections for ensembles with different numbers of desired ground motions. It is demonstrated that considering only spectral acceleration (SA) ordinates in the selection process, as is common in many conventional selection procedures, may result in selected motions with a biased representation for duration and cumulative ground motion effects. In contrast, considering IMs other than SA ordinates (in particular, significant duration, cumulative absolute velocity, and Arias intensity) results in ensembles with an appropriate representation of these IMs, without a practically significant effect on SA ordinates. The benefit of conducting replicate selections to obtain a suite of motions with an improved representation for the distribution of the considered IMs is demonstrated, and a minimum number of replicates are suggested for different ground motion ensemble sizes. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of bias on the probability of collapse from amplitude scaling using spectral-shape-matched records

Although for many years it was thought that amplitude scaling of acceleration time series to reach a target intensity did not introduce any bias in the results of nonlinear response history analyses, recent studies have showed that scaling can lead to an overestimation of deformation demands with increasing scale factors. Some studies have suggested that the bias can be explained by differences in spectral shape between the response spectra of unscaled and scaled records. On the basis of these studies, some record selection procedures assume that if records are selected using spectral-shape-matching procedures, amplitude scaling does not induce any bias on the structural response. This study evaluates if bias is introduced on lateral displacement demands and seismic collapse risk estimates even when spectral shape is carefully taken into consideration when selecting ground motions. Several single-degree-of-freedom and multiple-degree-of-freedom systems are analyzed when subjected to unscaled and scaled ground motions selected to approximately match the mean and the variance of the conditional spectrum at the target level of intensity. Results show that an explicit consideration of spectral shape is not enough to avoid a systematic overestimation of lateral displacement demands and collapse probabilities as the scale factor increases. Moreover, the bias is observed in practically all cases for systems with strength degradation and it increases with decreasing period and decreasing lateral strength relative to the strength required to remain elastic. Key reasons behind the bias are presented by evaluating input energy, causal parameters, and damaging pulse distributions in unscaled and scaled ground motion sets.     

EFFICIENT 2D AND 3D SHOT RECORD REDATUMING1

In order to make 3D prestack depth migration feasible on modern computers it is necessary to use a target-oriented migration scheme. By limiting the output of the migration to a specific depth interval (target zone), the efficiency of the scheme is improved considerably. The first step in such a target-oriented approach is redatuming of the shot records at the surface to the upper boundary of the target zone. For this purpose, efficient non-recursive wavefield extrapolation operators should be generated. We propose a ray tracing method or the Gaussian beam method. With both methods operators can be efficiently generated for any irregular shooting geometry at the surface. As expected, the amplitude behaviour of the Gaussian beam method is better than that of the ray tracing based operators. The redatuming algorithm is performed per shot record, which makes the data handling very efficient. From the shot records at the surface‘genuine zero-offset data’are generated at the upper boundary of the target zone. Particularly in situations with a complicated overburden, the quality of target-oriented zero-offset data is much better than can be reached with a CMP stacking method at the surface. The target-oriented zero-offset data can be used as input to a full 3D zero-offset depth migration scheme, in order to obtain a depth section of the target zone.     

Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior

We invert prestack seismic amplitude data to find rock properties of a vertical profile of the earth. In particular we focus on lithology, porosity and fluid. Our model includes vertical dependencies of the rock properties. This allows us to compute quantities valid for the full profile such as the probability that the vertical profile contains hydrocarbons and volume distributions of hydrocarbons. In a standard point wise approach, these quantities can not be assessed. We formulate the problem in a Bayesian framework, and model the vertical dependency using spatial statistics. The relation between rock properties and elastic parameters is established through a stochastic rock model, and a convolutional model links the reflectivity to the seismic. A Markov chain Monte Carlo (MCMC) algorithm is used to generate multiple realizations that honours both the seismic data and the prior beliefs and respects the additional constraints imposed by the vertical dependencies. Convergence plots are used to provide quality check of the algorithm and to compare it with a similar method. The implementation has been tested on three different data sets offshore Norway, among these one profile has well control. For all test cases the MCMC algorithm provides reliable estimates with uncertainty quantification within three hours. The inversion result is consistent with the observed well data. In the case example we show that the seismic amplitudes make a significant impact on the inversion result even if the data have a moderate well tie, and that this is due to the vertical dependency imposed on the lithology fluid classes in our model. The vertical correlation in elastic parameters mainly influences the upside potential of the volume distribution. The approach is best suited to evaluate a few selected vertical profiles since the MCMC algorithm is computer demanding.     

Numbers of scaled and matched accelerograms required for inelastic dynamic analyses

Selecting, scaling and matching accelerograms are critically important to engineering design and assessment, enabling structural response to be determined with greater confidence and through fewer analyses than if unscaled accelerograms are employed. This paper considers the response of an 8‐storey multiple‐degree‐of‐freedom reinforced concrete structure to accelerograms selected, linearly scaled or spectrally matched using five different techniques. The first method consists of selecting real records on the basis of seismological characteristics, while the remaining methods make an initial selection on the basis of magnitude and spectral shape before (1) scaling to the target spectral acceleration at the initial period; (2) scaling to the target spectrum over a range of periods; (3) using wavelet adjustments to match the target spectrum and (4) using wavelet adjustments to match multiple target spectra for multiple damping ratios. The analyses indicate that the number of records required to obtain a stable estimate of the response decreases drastically as one moves through these methods. The exact number varies among damage measures and is related to the predictability of the damage measure. For measures such as peak roof and inter‐storey drift, member end rotation and the Park and Ang damage index, as few as one or two records are required to estimate the response to within ±5% (for a 64% confidence level) if matching to multiple damping ratios is conducted. Bias checks are made using predictive equations of the expected response derived from the results of 1656 nonlinear time‐domain analyses of the structure under the action of unscaled accelerograms. Copyright © 2008 John Wiley & Sons, Ltd.     

随机地震反演关键参数优选和效果分析(英文)

随机地震反演技术是将地质统计理论和地震反演相结合的反演方法,它将地震资料、测井资料和地质统计学信息融合为地下模型的后验概率分布,利用马尔科夫链蒙特卡洛(MCMC)方法对该后验概率分布采样,通过综合分析多个采样结果来研究后验概率分布的性质,进而认识地下情况。本文首先介绍了随机地震反演的原理,然后对影响随机地震反演效果的四个关键参数,即地震资料信噪比、变差函数、后验概率分布的样本个数和井网密度进行分析并给出其优化原则。资料分析表明地震资料信噪比控制地震资料和地质统计规律对反演结果的约束程度,变差函数影响反演结果的平滑程度,后验概率分布的样本个数决定样本统计特征的可靠性,而参与反演的井网密度则影响反演的不确定性。最后通过对比试验工区随机地震反演和基于模型的确定性地震反演结果,指出随机地震反演可以给出更符合地下实际情况的模型。     

A nonhomogeneous Poisson process geostatistical model

This paper introduces a new geostatistical model for counting data under a space-time approach using nonhomogeneous Poisson processes, where the random intensity process has an additive formulation with two components: a Gaussian spatial component and a component accounting for the temporal effect. Inferences of interest for the proposed model are obtained under the Bayesian paradigm. To illustrate the usefulness of the proposed model, we first develop a simulation study to test the efficacy of the Markov Chain Monte Carlo (MCMC) method to generate samples for the joint posterior distribution of the model’s parameters. This study shows that the convergence of the MCMC algorithm used to simulate samples for the joint posterior distribution of interest is easily obtained for different scenarios. As a second illustration, the proposed model is applied to a real data set related to ozone air pollution collected in 22 monitoring stations in Mexico City in the 2010 year. The proposed geostatistical model has good performance in the data analysis, in terms of fit to the data and in the identification of the regions with the highest pollution levels, that is, the southwest, the central and the northwest regions of Mexico City.     

Record selection for aftershock incremental dynamic analysis

The back‐to‐back application of mainshock records as aftershock is often considered in conducting aftershock incremental dynamic analysis. In such an approach, the characteristics of mainshock records are considered to be similar to those of major aftershock records within the same mainshock–aftershock sequences. The underlying assumption is that the characteristics of selected mainshocks, other than those used for record selection, are not significant in the assessment of structural responses. A case study is set up to investigate the effects of aftershock record selection on the collapse vulnerability assessment. The numerical results for a specific wood‐frame structure indicate that the aftershock fragility can be affected by the aftershock record characteristics, particularly response spectral shape. Copyright © 2014 John Wiley & Sons, Ltd.     

不同采样率数字石英水平摆记录资料对比

通过对采样率不同的两套数字石英水平摆倾斜仪观测曲线进行对比分析,结果显示：秒采样获得的倾斜记录的分钟值与分钟采样获得的倾斜记录的潮汐波一致。采用秒采样观测能够获得大量丰富的信息,在同震响应中,能清晰记录到P波、S波。秒采样提高了地震响应能力。     

目标谱选波方法在算术与对数坐标下的差异分析

目前时程分析选波常采用目标谱法,即选择反应谱与目标谱有较好匹配的地震波。目前为止,关于在谱匹配中采用不同的坐标体系会给地震波缩放以及时程分析结果造成的差异性影响,还鲜有相关研究结果。本文旨在对比分析谱匹配中反应谱与目标谱采用算术值(算术坐标下ASM方法)和对数值(对数坐标下LSM方法)所得结构非线性时程分析结果的差异。以美国SAC Steel Project提出的3层、9层和20层钢框架结构为实例,以该计划提出的代表3种超越概率(即50年超越概率50%、10%和2%)的各组地震波平均反应谱作为目标谱,以这3组地震波(每组20条波)时程分析所得最大层间位移角的算术均值和几何均值作为目标反应,以简单地震信息初选的小型地震波数据库(共40条波)作为备选波,将ASM和LSM方法优选出的7条地震波所得时程分析结果进行了差异性分析。研究表明, LSM方法所得地震波的缩放系数明显大于ASM方法,并且地震波的排序即选择结果也存在一定差异。ASM和LSM方法对结构反应均值(算术均值和对数均值)估计的准确度均控制在±20%以内,但LSM方法所得结构反应更大。LSM方法在降低结构反应离散性方面较ASM方法更有优势,且对于较长周期结构(如20层结构)及结构非线性程度较高(如50年超越概率10%、2%)时,这种优势会更为凸显。     

Conditional spectrum‐based ground motion record selection using average spectral acceleration

The use of a seismic intensity measure (IM) is paramount in decoupling seismic hazard and structural response estimation when assessing the performance of structures. For this to be valid, the IM needs to be sufficient;that is, the engineering demand parameter (EDP) response should be independent of other ground motion characteristics when conditioned on the IM. Whenever non‐trivial dependence is found, such as in the case of the IM being the first‐mode spectral acceleration, ground motion selection must be employed to generate sets of ground motion records that are consistent vis‐à‐vis the hazard conditioned on the IM. Conditional spectrum record selection is such a method for choosing records that are consistent with the site‐dependent spectral shape conditioned on the first‐mode spectral acceleration. Based on a single structural period, however the result may be suboptimal, or insufficient, for EDPs influenced by different period values, for example, peak interstory drifts or peak floor accelerations at different floors, potentially requiring different record suites for each. Recently, the log‐average spectral acceleration over a period range, AvgSA, has emerged as an improved scalar IM for building response estimation whose hazard can be evaluated using existing ground motion prediction equations. Herein, we present a recasting of conditional spectrum record selection that is based on AvgSA over a period range as the conditioning IM. This procedure ensures increased efficiency and sufficiency in simultaneously estimating multiple EDPs by means of a single IM. Copyright © 2017 John Wiley & Sons, Ltd.     

Selecting and scaling real ground motion records using harmony search algorithm

In this study, a solution model is proposed to obtain input ground motion datasets compatible with given design spectra based on meta-heuristic harmony search algorithm. The utility of the solution model is demonstrated by generating ground motion datasets matching the Eurocode-8 design spectra for different soil types out of an extensive database of recorded motions. A total of 352 records are selected from the Pacific Earthquake Engineering Center (PEER) Strong Motion Database based on magnitude, distance, and site conditions to form the original ground motion domain. Then, the proposed harmony search based solution algorithm is applied on the pre-selected 352 time-series to obtain the ground motion record sets compatible with design spectra. The results demonstrate that the proposed HS based solution model provides an efficient way to develop input ground motion record sets that are consistent with code-based design spectra.     

于田M_s7.3地震震前重力扰动信号研究

2008年3月21日和2014年2月12日,新疆和田地区于田县先后发生两次M_s7.3地震.为分析两次地震前是否存在重力短临异常信号,应用Tsoft软件对原始数据进行理论固体潮改正和气压改正,并基于小波分解去除其低频趋势,有效提取了两次地震前国家连续重力观测网多台g-Phone重力仪的高频扰动信息.结果表明两次地震前都记录到高频扰动信号.2008年地震前牡丹江、沈阳台和漳州台三个台站记录到的高频扰动信号,主频分布在0.2~0.4 Hz.2014年地震前8个台站记录到两组不同频段的扰动信号.第一组信号集中在震前6天的东北台站,第二组扰动信号出现在震前3天的东南台站.两组扰动信号振幅呈现从北到南、从东到西逐步衰减的时空分布规律·同时期的气象资料显示局部强降雨与第二组扰动异常存在良好的相关性.对高频重力信号的时频分析结果表明震前第一组扰动信号存在异常频段0·13~0.18 Hz.应用扰动源扫描算法对2014年两组扰动信号进行空间定.位,结果显示第一组信号扰动源位置与于田震中相距约1935 km,不在于田地震的孕震区内;第二组信号扰动源来自海上.2008年和2014年于田地震第二组震前扰动信号与强降雨以及海浪影响相关.2014年第一组信号与于田地震的关系仍待进一步研究.     

Strong ground motion record selection for the reliable prediction of the mean seismic collapse capacity of a structure group

How to select a limited number of strong ground motion records (SGMRs) is an important challenge for the seismic collapse capacity assessment of structures. The collapse capacity is considered as the ground motion intensity measure corresponding to the drift‐related dynamic instability in the structural system. The goal of this paper is to select, from a general set of SGMRs, a small number of subsets such that each can be used for the reliable prediction of the mean collapse capacity of a particular group of structures, i.e. of single degree‐of‐freedom systems with a typical behaviour range. In order to achieve this goal, multivariate statistical analysis is first applied, to determine what degree of similarity exists between each selected small subset and the general set of SGMRs. Principal Component analysis is applied to identify the best way to group structures, resulting in a minimum number of SGMRs in a proposed subset. The structures were classified into six groups, and for each group a subset of eight SGMRs has been proposed. The methodology has been validated by analysing a first‐mode‐dominated three‐storey‐reinforced concrete structure by means of the proposed subsets, as well as the general set of SGMRs. The results of this analysis show that the mean seismic collapse capacity can be predicted by the proposed subsets with less dispersion than by the recently developed improved approach, which is based on scaling the response spectra of the records to match the conditional mean spectrum. Copyright © 2010 John Wiley & Sons, Ltd.     

Multi-point source identification of sudden water pollution accidents in surface waters based on differential evolution and Metropolis–Hastings–Markov Chain Monte Carlo

Sudden water pollution accidents in surface waters occur with increasing frequency. These accidents significantly threaten people’s health and lives. To prevent the diffusion of pollutants, identifying these pollution sources is necessary. The identification problem of pollution source, especially for multi-point source, is one of the difficulties in the inverse problem area. This study examines this issue. A new method is designed by combining differential evolution algorithm (DEA) and Metropolis–Hastings–Markov Chain Monte Carlo (MH–MCMC) based on Bayesian inference to identify multi-point sudden water pollution sources. The effectiveness and accuracy of this proposed method is verified through outdoor experiments and comparison between DEA and MH–MCMC. The average absolute error of the sources’ position and intensity, the relative error and the average standard deviations obtained using the proposed method are less than those of DEA and MH–MCMC. Moreover, the relative error and the sampling relative error under four different standard deviations of measurement error (σ = 0.01, 0.05, 0.1, 0.15) are less than 2 and 0.11 %, respectively. The proposed method (i.e., DEMH–MCMC) is effective even when the standard deviation of the measurement error increases to 0.15. Therefore, the proposed method can identify sources of multi-point sudden water pollution accidents efficiently and accurately.     

Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study

This study compares formal Bayesian inference to the informal generalized likelihood uncertainty estimation (GLUE) approach for uncertainty-based calibration of rainfall-runoff models in a multi-criteria context. Bayesian inference is accomplished through Markov Chain Monte Carlo (MCMC) sampling based on an auto-regressive multi-criteria likelihood formulation. Non-converged MCMC sampling is also considered as an alternative method. These methods are compared along multiple comparative measures calculated over the calibration and validation periods of two case studies. Results demonstrate that there can be considerable differences in hydrograph prediction intervals generated by formal and informal strategies for uncertainty-based multi-criteria calibration. Also, the formal approach generates definitely preferable validation period results compared to GLUE (i.e., tighter prediction intervals that show higher reliability) considering identical computational budgets. Moreover, non-converged MCMC (based on the standard Gelman–Rubin metric) performance is reasonably consistent with those given by a formal and fully-converged Bayesian approach even though fully-converged results requires significantly larger number of samples (model evaluations) for the two case studies. Therefore, research to define alternative and more practical convergence criteria for MCMC applications to computationally intensive hydrologic models may be warranted.