震源参数反演及精度评定的Bootstrap方法

在震源参数反演理论研究中,地表形变与震源参数之间为复杂多维的非线性关系,针对传统泰勒级数展开的精度评定方法可能无法适用于震源参数的精度评定问题,本文将Bootstrap方法引入到震源参数非线性反演及精度评定研究中.通过对GPS地表形变观测数据实施Bootstrap重采样获取自助样本,使用遗传算法(Genetic Algorithm, GA)搜索震源参数,设计并给出了震源参数精度评定的Bootstrap方法计算流程.将本文方法用于6个模拟地震、Amatrice地震及Visso地震实验中,通过反演震源参数、获取参数的置信区间及中误差,并与Jackknife方法、Monte Carlo方法进行对比分析.实验结果表明,通过执行本文精度评定方法能够获取比Jackknife方法更加可靠的震源参数置信区间以及更加精确的精度信息.实验验证了将Bootstrap方法用于震源参数精度评定的有效性和可靠性,为研究震源参数精度评定理论研究提供了一种新的采样思路.     

2013 年3 月27 日台湾南投MW 6. 0 地震震源机制解与发震构造研究

采用基于近远震波形的CAPjoint 方法,对2013 年3 月27 日台湾南投MW6. 0 地震分别进行了单独反演和联合反演,获得了各自的震源机制解及震源深度。其中,联合反演所得的最优双力偶机制解参数为,节面Ⅰ:方位角0°,倾角30°,滑动角78°;节面Ⅱ:方位角193°,倾角60°,滑动角96°;震源深度18km。为了验证联合反演的有效性和可靠性,采用由重抽样理论发展而来的Bootstrap 方法,对近远震数据分别进行了重采样,并对其单独反演和联合反演所得的参数进行了统计,验证了联合反演的有效性及稳定性,并发现远震反演震源深度的结果一定程度上受台站分布的影响,若要获得精确的震源深度则需要方位角分布较好的远震台站记录。根据前人对集集地震及车笼埔断层的研究成果以及南投地震的发震断层性质接近车笼埔断层的认识,推测此次地震可能发生在车笼埔断层上。     

2010年高雄地震震源参数的近远震波形联合反演

本文改进了传统基于近震波形数据的点源震源参数反演的Cut And Paste(CAP)方法,实现了近震Pnl波、面波和远震P波、SH波的联合反演的CAPjoint算法.对2010年3月高雄地震,分别进行单独反演以及联合反演,获得各自的震源机制解及深度,其中联合反演所得的最佳双力偶机制解参数为,节面1:走向317°,倾角36°,滑移角52°,节面2:走向181°,倾角62°,滑移角114°,深度为21 km.并对不同震中距波形对本次地震以及几种典型机制解断层几何参数的敏感性进行测试.为验证联合反演方法的可靠性,本文采用重抽样思想发展而来的Bootstrap方法,对近震数据的子集及其与远震数据的联合反演所得的参数进行统计,验证了在稀疏近台条件下联合反演中添加远震数据对地震震源参数约束的作用.     

土非线性参数变异性对标准设计谱的影响

岩土工程中存在许多不确定性,建筑物抗震规范设计中的标准设计谱实际代表着一种平均结果,并没有考虑到场地土体非线性参数的变异性。文中采用数值模拟的方法研究了土非线性参数变异性对标准设计谱的影响。给出了考虑土非线性参数变异性的概率设计谱。结果表明,土非线性参数变异性对标准设计谱的影响显著;粘性土和无粘性土的动剪切模量比对标准设计谱的影响决定于土非线性参数的概率水准,同时也取决于概率设计谱的频段;烈度7度、8度和9度下土体动剪切模量比变异性使标准设计谱平台最大变化50%、80%和100%,阻尼比变异性使标准设计谱平台最大变化40%、45%和60%。     

谱比法反演震源谱拐角频率的改进研究

由于地震波的信噪比,传播路径效应,震源机制差异和震源过程等因素的影响,传统的谱比法反演的拐角频率结果不精确,离散性大。以谱比法为基础,引入Bootstrap重采样法,KolmogorovSmirnov检验以及谱比趋势检验的筛选方法,对谱比法的反演结果进行精度评定和筛选,提高谱比法反演的拐角频率精度。将此方法应用于汶川地震余震的拐角频率、地震辐射能量、辐射效率以及能矩比等震源参数反演,建立拐角频率与地震矩之间的对应关系,验证谱比法以及三种筛选方法的可靠性。汶川地震余震震源参数反演结果表明,汶川地震余震的拐角频率与地震矩对应关系M₀∝f_c^-3.295±0.25存在与自相似关系M₀∝f_c^-3偏离现象。在地震释放能量方面,存在能矩比随地震矩增大而增大,以及大地震的辐射效率大于小地震的现象。     

基于景观形状指数的地表覆盖检验样本自适应抽样方法

大范围地表覆盖常常具有极强的空间异质性,直接影响着检验样本的数量计算与空间布设.致使传统抽样方法在区域样本量确定、地类间样本量分配和样本空间分布方面存在着较大局限性.本文提出了一种顾及地表覆盖空间异质性的样本抽样方法,计算验证区域、区内地类和抽样格网三个层级的景观形状指数,推导出基于区域级景观形状指数及面积的区域样本量解析计算公式,建立了顾及地类级景观形状指数的样本量内曼分配公式,提出了基于格网级景观形状指数曲线的样点抽取方法,为大范围地表覆盖验证提供了定量化的自适应抽样方法.以毛里塔尼亚、西班牙和孟加拉三个国家为验证区域进行的实验表明:(1)基于区域级景观形状指数的样本量解析计算,较好地顾及了地表覆盖空间异质性强弱的影响,有效实现了区域样本量的自动调整;(2)引入地类级景观形状指数的内曼分配计算,有效提高了稀少类的抽样比例,解决了现有抽样方法难以顾及稀少类的问题;(3)在格网级景观形状指数曲线所构成的异质性强度空间中抽取样点,可使样本分布兼顾破碎与均质地域.此外,在西班牙区域进行了精度对比,发现本文提出的自适应抽样方法的精度评价结果更接近真实精度,尤其是稀少类的精度评价准确度平均提高了10%以上.     

基于区间分析的不确定性参数识别方法

提出一种基于区间分析的不确定性参数识别方法。该方法首先通过计算所定义的区间灵敏度指标来确定各个参数对结构动力不确定性的影响程度,经比较分析可将对动力不确定性影响较大的参数确定为不确定性参数。然后在参数选择的基础上,采用有界区间数来描述不确定性参数并进行区间响应面建模,利用响应上下限构造目标函数并按照区间序数关系,将不确定性优化目标和约束条件转化为确定性表达式,通过优化反演即可识别不确定性参数的变异性区间。最后以钢板的材料参数区间识别为例验证所提方法的可行性及有效性。参数识别结果表明所提方法在参数较多的情况下仍可准确地识别不确定性参数的变异性区间,具有较高的计算效率且可有效地避免区间运算存在的误差问题。     

震害预测中抽样方法的研究

本文阐述了在震害预测中采用分块抽样方法的一般原理,对于确定抽样样本总额的方法和样本总额在分块抽样中的理想分配进行了讨论,文中以乌鲁木齐市钢筋混凝土房屋的震害预测为对象,说明了分块抽样法的实际应用及其对于随机抽样法的优越性。文中对逐栋调查和分块抽样调查所得到的震害预测结果进行了对比,论述了这一抽样方法的可行性。     

沈阳地铁开挖引发地面沉降的BP神经网络反分析

以沈阳地铁一号线重启区间隧道为研究背景,采用基于均匀试验设计法的有限元数值分析法确定隧道围岩土体物理力学参数与地面沉降之间的关系,并以此作为神经网络的输入样本,通过BP神经网络对样本的训练、学习,建立隧道围岩土体力学参数与地面沉降之间的映射关系,然后利用这种映射关系,根据地铁开挖引发的地面沉降实测值反演岩土体的物理力学参数,最后根据参数反演结果,建立有限元应力应变模型预测地面沉降,并与实测值相比较,以检验BP神经网络地面沉降位移反分析方法的有效性和合理性。     

基于SVM与SRM耦合的抗剪强度参数随机场模拟

实验数据表明土体参数具有很大的空间变异性,而随机场理论为模拟土体参数空间变异性提供了有效途径。因为传统的谱表示法(SRM)无法正确模拟多维多元随机场参数间的互相关性,提出支持向量机法(SVM)与SRM耦合的方法。SVM是基于统计学习理论和结构风险最小化原理基础上的通用机器学习方法,它在解决小样本、非线性和高维模式识别问题中表现出诸多优势。以土体抗剪强度参数:黏聚力c和内摩擦角φ为例,通过实验证明二者之间存在天然负相关性,即为二维二元随机场。结果表明,在样本数量较少的条件下,基于耦合算法模拟随机场不仅能有效地描述变量的自相关性,而且能够准确地描述变量间的互相关性,为解决小样本条件下模拟多维多元随机场提供了一种有效的方法。     

Improvement of overtopping risk evaluations using probabilistic concepts for existing dams

Hydrologic risk analysis for dam safety relies on a series of probabilistic analyses of rainfall-runoff and flow routing models, and their associated inputs. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated in order to evaluate the probability of dam overtopping. Typically, parametric density estimation methods have been applied in this setting, and the exhaustive Monte Carlo simulation (MCS) of models is used to derive some of the distributions. Often, the distributions used to model some of the random variables are inappropriate relative to the expected behaviour of these variables, and as a result, simulations of the system can lead to unrealistic values of extreme rainfall or water surface levels and hence of the probability of dam overtopping. In this paper, three major innovations are introduced to address this situation. The first is the use of nonparametric probability density estimation methods for selected variables, the second is the use of Latin Hypercube sampling to improve the efficiency of MCS driven by the multiple random variables, and the third is the use of Bootstrap resampling to determine initial water surface level. An application to the Soyang Dam in South Korea illustrates how the traditional parametric approach can lead to potentially unrealistic estimates of dam safety, while the proposed approach provides rather reasonable estimates and an assessment of their sensitivity to key parameters.     

Seismic Zonation of Barcelona Based on Numerical Simulation of Site Effects

—?Seismic responses of different sites of Barcelona have been investigated through numerical modelling. Geological maps and geotechnical data available from drillings for buildings and infrastructures have been used to determine the dynamical properties of the soils through different correlations between standard geotechnical data and dynamical parameters obtained in other regions. An estimation of the depth of the Palaeozoic basement has been obtained through an inversion of a detailed gravity survey. A 1-D equivalent linear method has been used to compute complete transfer functions and other spectral responses, such as PSA and PSV for various damping values, with the purpose of classifying zones with similar behaviour. Given the uncertainties associated with the input data, a Montecarlo's simulation process has been carried out. Four zones, characterized by their corresponding transfer function and by PGA amplifications, are proposed. The numerical results are compared with those previously obtained through microtremor measurements, showing that predominant periods derived from Nakamura's technique should be taken carefully.     

Multivariate nonparametric resampling scheme for generation of daily weather variables

A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.     

Quantification of the predictive uncertainty of artificial neural network based river flow forecast models

The meaningful quantification of uncertainty in hydrological model outputs is a challenging task since complete knowledge about the hydrologic system is still lacking. Owing to the nonlinearity and complexity associated with the hydrological processes, Artificial neural network (ANN) based models have gained lot of attention for its effectiveness in function approximation characteristics. However, only a few studies have been reported for assessment of uncertainty associated with ANN outputs. This study uses a simple method for quantifying predictive uncertainty of ANN model output through first order Taylor series expansion. The first order partial differential equations of non-linear function approximated by the ANN with respect to weights and biases of the ANN model are derived. A bootstrap technique is employed in estimating the values of the mean and the standard deviation of ANN parameters, and is used to quantify the predictive uncertainty. The method is demonstrated through the case study of Upper White watershed located in the United States. The quantitative assessment of uncertainty is carried out with two measures such as percentage of coverage and average width. In order to show the magnitude of uncertainty in different flow domains, the values are statistically categorized into low-, medium- and high-flow series. The results suggest that the uncertainty bounds of ANN outputs can be effectively quantified using the proposed method. It is observed that the level of uncertainty is directly proportional to the magnitude of the flow and hence varies along time. A comparison of the uncertainty assessment shows that the proposed method effectively quantifies the uncertainty than bootstrap method.     

基于逆建模理论的储层特征定量预测方法

定量描述储层特征的物理量称为储层参数,在储层描述工作中储层参数预测是一个重要环节.本文以岩石物理理论为基础研究了利用岩石物理逆建模理论进行储层参数预测的方法.在合理、有效的岩石物理模型的基础上,逆建模方法通过弹性参数的等值面的空间交会可以预测出储层参数.在此基础上提出了一种扩展的岩石物理逆建模方法,在两个不同的储层参数域进行三维岩石物理逆建模,通过逆建模结果的参数域转换可以预测更多的储层参数,为储层特征描述提供了更加充分的数据支持.输入数据的种类对于预测结果的准确性有较大影响,利用本文建立的弹性参数适用性分析方法可以选择出适用性好的弹性参数组合作为输入数据.将本方法应用于模型数据和实际工区数据,取得了较好的预测效果,证明方法具有一定的实用性.     

Flood variability east of Australia’s Great Dividing Range

The variability of flow in river channels influences the spatial and temporal variability of many biophysical processes including the transport of sediment and waterborne pollutants and the recruitment of aquatic animals and plants. In this study, inter- and intra-basin patterns of flood variability are examined for catchments east of Australia’s Great Dividing Range. Three measures of flood variability are explored with uncertainty quantified using bootstrap resampling. The two preferred measures of flood variability (namely a flood quantile ratio and a power law scaling coefficient) produced similar results. Catchments in the wet tropics of far north Queensland experience low flood variability. Flood variability increased southwards through Queensland, reaching a maximum in the vicinity of the Fitzroy and Burnett River basins. The small near-coast catchments of southern Queensland and northern New Wales experience low flood variability. Flood variability is also high in the southern Hunter River and Hawkesbury–Nepean basins. Using L-moment ratio diagrams with data from 424 streamflow stations, we also conclude that the Generalised Pareto distribution is preferable for modelling flood frequency curves for this region. These results provide a regional perspective that can be used to develop new hypotheses about the effects of hydrologic variability on the biophysical characteristics of these Australian rivers.     

Revised runoff curve number for runoff prediction in the Loess Plateau of China

The soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS-CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall-runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN₂ values obtained from the USDA-SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS-CN method, but still performs poorly in most study sites. This is mainly due to the SCS-CN method using a constant CN value and discounting of the temporal variation in rainfall-runoff process. Therefore, three factors—soil moisture, rainfall depth and intensity—affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS-CN and the Shi and Wang (2020b) method, a modified SCS-CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.