地震危险性分析中专家意见的评定

本文发展了一种加权平均方法，用于综合考虑地震危险性分析中的专家意见。其中假定接近综合结果的专家意见具有的权系数较大，这样可以合理反映专家意见的一致性与差异性，另外，利用随机加权方法估计综合结果及权系数的不确定性，给出其统计特征量。     

地震发生的空间概率模型

本文的主要目的是为工程地震危险性分析提供一种考虑地震发生空间不均匀性的概率模型。采用伯努利试验模型描述了在一给定震源区内地震发生位置的不确定性,并利用贝叶斯方法将观测数据与专家的主观判断相结合给出了地震发生概率的均衡估计。同时也给出了综合专家主观意见的方法和计算场地烈度超过概率的方法。从而为在工程地震危险性分析中充分反映地震地质学家和地球物理学家关于地震中长期预报的意见提供了一种定量的方法。     

地震分析预报综合环境建设现状及其发展构想

对自１９６６年我国开展地震预报工作以来的分析预报综合环境建设现状进行了分析，指出了目前部分单位日常分析预报工作中存在的一些问题，在征求专家集团意见的基础上，提出了加大地震分析预报综合环境建设工作力度的一些近期初步设想，并对国家地震局《震情应急指挥系统》作了初步介绍。     

熵权决策法在地震灾害风险评价中的应用

为了更好地评价和比较城市的地震灾害风险,建立了基于熵权的新的地震灾害风险指数(EDRI)模型。在地震灾害风险指数的建立过程中,根据信息熵理论计算熵权,采用熵权与专家意见相结合的方法确定风险指数的综合权重,从而使模型权重的确定更加合理。通过算例分析,新的计算模型可以降低城市地震灾害风险指数对专家主观意见的依赖,从而使计算变得更加客观、科学。     

结构可靠性分析中各类不确定性的综合处理方法

本文在分析了文［１］推广的一次二阶矩方法的局限性后，利用高阶矩标准化方法进一步将该方法推广应用于含有离散变量及模糊不确定性的可靠性分析中，从而给出了一个结构可靠性分析中各类不确定性的综合处理方法。文中通过几个算例说明了该方法的实用性并初步分析了不确定性对可靠度分析结果的影响规律。最后，作为一个工程算例，对一个核电厂安全壳进行了地震可靠性分析。     

地震危险性分析的不确定性及其对策

本文指出,目前地震危险性分析工作中的不确定性有五个层次的来源。就性质而言,上述不确定性可分为客观不确定性和主观不确定性两类。文中提出了处理各层次不确定性的对策原则。研究发现,对实际危险性分析模型来说,客观不确定性往往伴同主观不确定性一起出现,因而在对客观不确定性作校正时应重视减小其中主观不确定性的影响;而在排除主观倾向性并尽可能减少主观失误之后,具相对真理性的不同专家方案的差异可视为客观不确定性,需视其统计特征作进一步校正。     

大震近场地震动数值模拟的不确定性研究

地震动数值模拟是研究地震发生、发展特征和规律性的基础研究,涉及震源、传播路径、场地三方面的一系列基本问题。研究结果可以直接用于强地震动基本参数的标定和地震动的工程预测以及地震危险性分析,是防震减灾中一项很重要的基础工作。当前考虑断层模型中的几个重要震源参数模拟未来地震动成为热点。例如,不管是随机有限断层方法,还是经验格林函数方法,都特别重视震源参数的选取,但当前对于强地面运动的数值模拟大多是对已发生地震的反演,反演所需的震源参数多是根据地震发生后的观测资料、经验关系推断或者人为的假定得到的。地震动特征预测的好坏,关键也在于震源参数的选取是否可靠,针对工程应用,对未来近断层地面运动的预测应当考虑参数选取的不确定性。强震动数值模拟方法已经比较成熟,比如随机有限断层法、经验格林函数法、混合方法等。但这些模拟地震动的方法多是确定性方法,对模拟过程中各个环节的不确定性因素考虑较少,尤其是断层震源参数的选取都是精确的数值,模拟结果自然不能综合表征未来地震发生时的地震动特征。另外一种地震动特性表征形式是利用地震动预测方程来表现参数的衰减关系,但是预测方程也会受观测数据、模型选取、随机因素的影响,不确定性无法避免。在地震动预测中每个步骤都带有很大的不确定性,主要包括认知的不确定性和随机的不确定性。震源模型的建立依赖各种专家的观点和意见,这种观点和意见往往相互差别很大,有很大的人为不确定性。地震资料的完整性、可靠性,以及理论模型等的建立也会造成很大的不确定性。因此,本论文的研究内容重点在预测未来大震近场地震动时震源环节的不确定性因素,系统地研究影响大震近场地震动数值模拟不确定性的影响因素,以期建立描述影响大震近场地震动数值模拟不确定性影响因素的模型,并将考虑不确定性因素的震源模型应用在破坏性大地震的地震动特征预测中去。针对地震发生时震源参数具有的随机不确定性,通过统计学方法得到震源参数间的经验关系。同时由于地球内部的不可入性以及观测技术手段的限制,地震震源模型还存在很多的认知不确定性因素,我们采用逻辑树方法处理认知不确定性因素,引入强震生成区的概念,重点分析Asperity的认知不确定性因素并在此基础上建立Asperity震源模型,取多种方案结果的最优值作为地震动的预测结果。论文具体的研究内容有以下5点:(1)在Hiroe Miyake工作的基础上,用经验格林函数法模拟了1997年日本九州鹿儿岛县的MJMA6.5地震,验证了经验格林函数法模拟强地面运动的有效性。数据使用的是日本K-NET强震台站数据。选择此次地震以及用日本K-NET数据的原因是日本的地震记录比较丰富,台站收集到的地震记录质量相对较好。同时分析了地震动模拟值中在工程领域使用较多的参数,表明模拟结果较好地反映了真实的地震动记录。证明经验格林函数预测未来强地震动是切实可行的。(2)本论文在大量地震记录及文献调研的基础上,运用统计学方法,通过统计回归分析得到地震密集区域震源参数间的经验关系,重点统计了包含龙门山断裂带在内的中国西南地区震源参数间的经验关系。为了得到更加适应于中国大陆区域的局部震源参数的经验关系,我们将地震样本局限在一定的区域内,缩小统计区域的范围,同时得到多个Asperity等局部震源参数的经验关系。(3)研究和分析各不确定性因素对地震动结果的影响程度对提高地震动预测的可靠性和准确性意义重大。本论文以2016年发生的日本熊本7.3级地震为例,通过参数敏感性分析技术筛选了对拟合结果有决定性影响的关键模型参数。未来地震动预测时会考虑在这几个不确定性因素中加入更高的权重,以提高地震动预测的可信度和准确性。(4)基于汶川地震资料,研究分析Asperity的认知不确定性对地震动预测的影响,运用逻辑树方法建立了考虑不确定性因素的Asperity震源模型。采用Hiroe Miyake提出的强震生成区的概念,将Asperity区域等同于强震生成区域,主要从Asperity的面积、数量、应力降、上升时间等方面进行了认知不确定性的分析研究,得到了针对强震的可能性较大的Asperity的面积和数量的最佳搭配形式。本文的创新点将概率地震危险性分析中不确定性因素的处理方法应用到未来大震近场地震动数值模拟中去。随机不确定性因素用统计学方法处理,认知不确定性用逻辑树方法处理。模拟结果能更好地表征未来大震近场的地震动特征,地震动参数的模拟结果在未来抗震设防及灾害预防中会有更好、更高的参考价值。考虑不确定性因素预测未来破坏性大地震,从而定量地评价发生地震时近场地面运动的综合特征,为防震减灾提供科学的依据,为工程设计决策提供参考依据。     

聚类分析在地震综合预报中的应用

本文针对思普——勐腊地震区综合预报中的实际问题,尝试聚类分析在综合预报中应用的可行性,依据聚类分析方法提出了1984年上半年中期预报意见,并给出了地震实况。     

地震预报的证据理论方法

概率论和数理统计方法一直是地震预报中处理不确定性的主要方法，但在很多情况下并不是特别有效。本引进了一种新的不确定性处理方法一证据理论方法，介绍了如何用该理论表达地震预报问题。讨论了应用其D-S合成规则进行预报的一般过程，并根据地震综合预报的特点，提出了加权的D-S规则。研究结果表明，证据理论应用于地震预报有其独特的优点。     

基于改进云图法的结构概率地震需求分析

概率地震需求分析是美国太平洋地震工程研究中心(Pacific Earthquake Engineering ResearchCenter,PEER)提出的新一代"性能化地震工程(Performance-Based Earthquake Engineering,PBEE)"理论框架的重要一环。传统的概率地震需求分析方法称为"云图法",这种方法针对确定性结构进行一系列地震动作用下的非线性动力分析,从而得到地震动强度参数与结构地震需求的"云图"。然而,传统的云图法只能考虑地震动的不确定性,而无法考虑结构的不确定性。为此,结合拉丁超立方体抽样技术,提出一种能综合考虑地震动不确定性和结构不确定性的改进云图法,并将传统的概率地震需求分析内容拓展为概率地震需求模型、概率地震需求易损性分析、概率地震需求危险性分析三个层次。以一榀五层三跨钢筋混凝土框架结构为例,分别采用传统云图法和改进云图法对其进行概率地震需求分析,得到了该结构的概率地震需求模型、地震需求易损性曲线和地震需求危险性曲线。分析结果表明:提出的方法可以有效地考虑地震动与结构的不确定性,避免不考虑结构的不确定性而低估结构的地震风险性。     

A method to reduce the computational requirement while assessing uncertainty of complex hydrological models

The quantification of uncertainty in the simulations from complex physically based distributed hydrologic models is important for developing reliable applications. The generalized likelihood uncertainty estimation method (GLUE) is one of the most commonly used methods in the field of hydrology. The GLUE helps reduce the parametric uncertainty by deriving the probability distribution function of parameters, and help analyze the uncertainty in model output. In the GLUE, the uncertainty of model output is analyzed through Monte Carlo simulations, which require large number of model runs. This induces high computational demand for the GLUE to characterize multi-dimensional parameter space, especially in the case of complex hydrologic models with large number of parameters. While there are a lot of variants of GLUE that derive the probability distribution of parameters, none of them have addressed the computational requirement in the analysis. A method to reduce such computational requirement for GLUE is proposed in this study. It is envisaged that conditional sampling, while generating ensembles for the GLUE, can help reduce the number of model simulations. The mutual relationship between the parameters was used for conditional sampling in this study. The method is illustrated using a case study of Soil and Water Assessment Tool (SWAT) model on a watershed in the USA. The number of simulations required for the uncertainty analysis was reduced by 90 % in the proposed method compared to existing methods. The proposed method also resulted in an uncertainty reduction in terms of reduced average band width and high containing ratio.     

Research on the attribution evaluating methods of dynamic effects of various parameter uncertainties on the in-structure floor response spectra of nuclear power plant

Consideration of the dynamic effects of the site and structural parameter uncertainty is required by the standards for nuclear power plants (NPPs) in most countries. The anti-seismic standards provide two basic methods to analyze parameter uncertainty. Directly manually dealing with the calculated floor response spectra (FRS) values of deterministic approaches is the first method. The second method is to perform probability statistical analysis of the FRS results on the basis of the Monte Carlo method. The two methods can only reflect the overall effects of the uncertain parameters, and the results cannot be screened for a certain parameter’s influence and contribution. In this study, based on the dynamic analyses of the floor response spectra of NPPs, a comprehensive index of the assessed impact for various uncertain parameters is presented and recommended, including the correlation coefficient, the regression slope coefficient and Tornado swing. To compensate for the lack of guidance in the NPP seismic standards, the proposed method can effectively be used to evaluate the contributions of various parameters from the aspects of sensitivity, acuity and statistical swing correlations. Finally, examples are provided to verify the set of indicators from systematic and intuitive perspectives, such as the uncertainty of the impact of the structure parameters and the contribution to the FRS of NPPs. The index is sensitive to different types of parameters, which provides a new technique for evaluating the anti-seismic parameters required for NPPs.     

基于能力谱的概率-非概率结构体系抗震可靠度

本文用概率模型考虑结构体系抗力不确定性的主要影响因素,用凸集模型考虑地震作用的不确定性,采用能力谱方法,分别求得了结构抗力的概率分布参数及地震作用效应的区间范围,通过二级功能方程方法求得了结构体系抗震可靠度。算例给出了本文方法的计算结果,并与经典概率可靠度进行了对比,表明本文方法是一种简便、合理的结构体系抗震可靠性分析方法。     

地震危险性分析衰减不确定性校正中的主观不确定性

实际使用中的危险性分析模型均带有主观的性质，其与现实原型之间的差异包含两种性质不同的不确定性，即现实模型与理想模型之间的主观不确定性，和理想模型与现实之间的随机误差或客观不确定性。本文具体讨论对衰减规律作（客观）不确定性校正时主观不确定性的影响问题。文中分析了衰减不确定性校正的两种做法，即在危险性分析中直接校正和先忽略衰减不确定性，求得危险性曲线后总校正的做法，讨论了两者的等价性，并用半定量的方法     

A computationally efficient method for uncertainty analysis of SWAT model simulations

The physically based distributed hydrological models are ideal for hydrological simulations; however most of such models do not use the basic equations pertaining to mass, energy and momentum conservation, to represent the physics of the process. This is plausibly due to the lack of complete understanding of the hydrological process. The soil and water assessment tool (SWAT) is one such widely accepted semi-distributed, conceptual hydrological model used for water resources planning. However, the over-parameterization, difficulty in its calibration process and the uncertainty associated with predictions make its applications skeptical. This study considers assessing the predictive uncertainty associated with distributed hydrological models. The existing methods for uncertainty estimation demand high computational time and therefore make them challenging to apply on complex hydrological models. The proposed approach employs the concepts of generalized likelihood uncertainty estimation (GLUE) in an iterative procedure by starting with an assumed prior probability distribution of parameters, and by using mutual information (MI) index for sampling the behavioral parameter set. The distributions are conditioned on the observed information through successive cycles of simulations. During each cycle of simulation, MI is used in conjunction with Markov Chain Monte Carlo procedure to sample the parameter sets so as to increase the number of behavioral sets, which in turn helps reduce the number of cycles/simulations for the analysis. The method is demonstrated through a case study of SWAT model in Illinois River basin in the USA. A comparison of the proposed method with GLUE indicates that the computational requirement of uncertainty analysis is considerably reduced in the proposed approach. It is also noted that the model prediction band, derived using the proposed method, is more effective compared to that derived using the other methods considered in this study.     

Parametric uncertainty assessment of hydrological models: coupling UNEEC-P and a fuzzy general regression neural network

Due to the complicated nature of environmental processes, consideration of uncertainty is an important part of environmental modelling. In this paper, a new variant of the machine learning-based method for residual estimation and parametric model uncertainty is presented. This method is based on the UNEEC-P (UNcertainty Estimation based on local Errors and Clustering – Parameter) method, but instead of multilayer perceptron uses a “fuzzified” version of the general regression neural network (GRNN). Two hydrological models are chosen and the proposed method is used to evaluate their parametric uncertainty. The approach can be classified as a hybrid uncertainty estimation method, and is compared to the group method of data handling (GMDH) and ordinary kriging with linear external drift (OKLED) methods. It is shown that, in terms of inherent complexity, measured by Akaike information criterion (AIC), the proposed fuzzy GRNN method has advantages over other techniques, while its accuracy is comparable. Statistical metrics on verification datasets demonstrate the capability and appropriate efficiency of the proposed method to estimate the uncertainty of environmental models.     

地震学预报方法综合程式的研究

本文将专家系统技术中的 MYCIN 不精确推理方法推广应用到地震学方法的综合预报中,给出了一套确定各类可信度,对异常证据进行相关改正及其计算发震综合信度的方法.并据此利用华北地区1966年以来的18次震例在各阶段的地震学异常进行了检验,求出相应的发震综合信度.最后对应用中的一些问题进行了讨论.      

On unified analysis of uncertainty in seismic hazard assessment

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Bayesian flood frequency analysis in the light of model and parameter uncertainties

The specific objective of the paper is to propose a new flood frequency analysis method considering uncertainty of both probability distribution selection (model uncertainty) and uncertainty of parameter estimation (parameter uncertainty). Based on Bayesian theory sampling distribution of quantiles or design floods coupling these two kinds of uncertainties is derived, not only point estimator but also confidence interval of the quantiles can be provided. Markov Chain Monte Carlo is adopted in order to overcome difficulties to compute the integrals in estimating the sampling distribution. As an example, the proposed method is applied for flood frequency analysis at a gauge in Huai River, China. It has been shown that the approach considering only model uncertainty or parameter uncertainty could not fully account for uncertainties in quantile estimations, instead, method coupling these two uncertainties should be employed. Furthermore, the proposed Bayesian-based method provides not only various quantile estimators, but also quantitative assessment on uncertainties of flood frequency analysis.     

UNCERTAINTY AND SENSITIVITY ANALYSES OF SEDIMENT TRANSPORT FORMULAS

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