Trend assessment of annual instantaneous maximum flows in Turkey

A comprehensive evaluation of trends in annual instantaneous maximum flows (AIMF) from 153 gauge stations located in 26 river basins in Turkey is presented. Two traditional non-parametric trend tests, the Mann-Kendall (MK) and Spearman’s rho (SR), are used to quantify the significance of trends, while Sen’s slope method is applied to determine the magnitude of trends. The traditional tests indicate that the AIMF records of 57 stations showed statistically decreasing trends, while those of six stations showed an increasing trend. Sen’s trend method, which provides more detailed assessment of the trends in different clusters (low, medium and high), was applied to the AIMF series and the results were compared with traditional tests. Sen’s trend method indicated that all flow clusters at nine stations have increasing or decreasing trends, although no significant trend was detected by the MK and SR tests.     

How reliable are standard indicators of stationarity?

In modern hydrological practice large confidence is placed on modelling results that are used for planning and design. This is especially the case where the modelling results have been carefully verified against independent data. An underlying assumption of the calibration/verification process is that the whole data series is stationarity. Standard parametric and non-parametric tests are available for examining the stationary of hydrologic time series but it has been shown here that these may be inadequate for that purpose unless applied with care. Annual, seasonal, monthly and daily time series of precipitation and climate data were examined considering parts of the series formed using sequential windows. Seven standard parametric and non-parametric tests were applied to these relatively long series and while it was shown that some tests suggested that all series were stationary, most series were shown to be non-stationary in more than one of the tests, some of them at very high levels of significance. This apparently hidden non-stationarity could have very large effects on water resources modelling. These effects would have considerable influence in calibration and verification of models and in simulation of long series of water resources characteristics and could be especially important as the effects of climate change become more pervasive.     

Non-parametric identification of a class of non-linear close-coupled dynamic systems

A non-parametric identification technique for the identification of arbitrary memoryless non-linearities has been presented for a class of close-coupled dynamic systems which are commonly met with in mechanical and structural engineering. The method is essentially a regression technique and expresses the non-linearities as series expansions in terms of orthogonal functions. Whereas no limitation on the type of test signals is imposed, the method requires the monitoring of the response of each of the masses in the system. The computational efficiency of the method, its easy implementation on analogue and digital machines and its relative insensitivity to measurement noise make it an attractive approach to the non-parametric identification problem.     

The solution of the one-dimensional inverse problem for induction soundings by an efficient linearization technique

A non-parametric method, the efficient linearization technique (ELT), is proposed for the inversion of induction soundings for a spherical Earth model with a radial conductivity distribution. The method is based on the fact that when linearizing the operator of the direct problem, the quantitative estimate of its non-linear term is taken into account, which makes the solution search procedure stable. At each iteration stage, the conductivity function correction is sought in the region where linearization is admissible. ELT makes it possible to evaluate the resolving power of the data and also the confidence interval for the conductivity function. Model examples illustrate the features of this method. The results of interpretation of magnetotelluric data are compared with nearby drill hole resistivity logs. A model of the conductivity distribution in the mantle based on global magnetovariational data available up to 1986, is constructed. This model is consistent with modern knowledge of physical processes occurring in the interior of the Earth.     

Derivation and assessment of a bivariate IDF relationship using paired rainfall intensity–duration data

A procedure is presented for developing a rainfall intensity–duration–frequency (IDF) relationship that is consistent with bivariate normal distribution modeling. The Box–Cox transformation was used to derive the relation and two methods of determining the parameters of this transformation were evaluated. To assess the uncertainty of the parameters, a confidence interval was constructed and verified with the non-parametric bootstrap method. Additionally, the effect of sample size on the bivariate normality assumption was examined. Case studies, based on data from significant gauge stations in Korea, were performed. The result shows that the use of the bivariate normal model as an IDF relationship is particularly recommended when the available data size is small.     

Prediction of the seismic capacity of RC structural walls by non-parametric multidimensional regression

The use of a non-parametric multidimensional regression is suggested for the prediction of the seismic capacity of reinforced concrete structural walls. This capacity is expressed in terms of the shear strength, drift, ductility and failure mode of the walls. For the application of the method a data base is needed which contains data on the results of tests performed on the structural elements under consideration. The data base used in the study was compiled from the available literature, and includes data from laboratory tests carried out on 262 structural walls. The method was validated by comparing the predicted and test results. A comparison with some available closed-form empirical predictions has been made. The results show that the accuracy of the predictions obtained by the proposed method is, on average, higher than that provided by existing formulae. The proposed method has several advantages when compared to closed-form empirical expressions based on standard linear regression: (1) The relationship between the input and output variables is not selected a priori by a prediction law. (2) It takes into account the currently available data base, which can be updated when new information becomes available. (3) An arbitrary number of input and output variables can be taken into account using the same data base. (4) The method can be applied directly to similar problems in structural and earthquake engineering, wherever a suitable data base is available.     

核电站设备的振动台试验

本文进行了核电站设备（IS型单级单吸清水离心泵）的动力特性测试和振动台试验，使用自由振动衰减法、白噪声激振法和半功率点法分别测出了水泵在三个方向上的自振频率和阻尼比。给出了楼板加速度时程的模拟方法。根据本文给出的模拟方法和楼板反应谱，模拟了楼板加速度时程，并进行了水泵的振动台试验。振动台试验是在水泵工作状态下进行的。     

基于二阶导数的磁源边界与顶部深度快速反演

为实现磁异常反演,首先提出了磁异常非参数快速反演的概念,即无需提供先验信息.在Nabighian提出的磁场通用梯度公式的基础上,推导实现了基于磁异常垂向二阶导数的非参数快速反演(V2D_depth).它不仅可以获取场源的边界信息,同时可以反演场源的埋深.通过与Tilt_depth方法对比,本文方法计算的场源边界更清晰,反演的深度也更接近真实深度,同时较大程度上克服了Tilt_depth方法受叠加异常的影响.理论模型验证了方法的有效性,并通过准噶尔盆地某区块磁异常数据的处理,提取了受强大的区域背景场掩盖的石炭系火成岩产生的弱异常,突出了构造分区、断裂分布等信息,获取了火成岩的位置及埋深参数,为该区的火成岩油气藏勘探提供了有效的处理途径.     

滇西南地区品质因子、场地反应与震源参数研究

为深入了解滇西南地区地震动影响因素特征,更好地服务于目标区的抗震设防工作与地震预测等相关研究,利用两步非参数化广义反演方法对滇西南地区2007-2019年242组三分量强震记录进行分析,通过对地表观测地震动在频域内的分离求解,给出了该地区0.5～20 Hz的品质因子经验关系,获得了26个强震台站在研究频段内的场地反应,并与HVSR法揭示的场地放大结果进行对比分析。基于网格搜索法确定了35次地震事件的地震矩M0、拐角频率fc及震源模型参数γ,拟合得到了部分震源参数之间的定标关系。研究结果表明,滇西南地区品质因子Q值与频率的关系为Q=94.23f 0.43;相较于HVSR法,非参数化广义反演方法确定的场地反应整体偏高,但2种方法得到的场地反应谱形较一致;拐角频率、地震矩及应力降等震源参数互相具有一定依赖性。     

Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression

This paper presents a Bayesian non-parametric method based on Gaussian Process (GP) regression to derive ground-motion models for peak-ground parameters and response spectral ordinates. Due to its non-parametric nature there is no need to specify any fixed functional form as in parametric regression models. A GP defines a distribution over functions, which implicitly expresses the uncertainty over the underlying data generating process. An advantage of GP regression is that it is possible to capture the whole uncertainty involved in ground-motion modeling, both in terms of aleatory variability as well as epistemic uncertainty associated with the underlying functional form and data coverage. The distribution over functions is updated in a Bayesian way by computing the posterior distribution of the GP after observing ground-motion data, which in turn can be used to make predictions. The proposed GP regression models is evaluated on a subset of the RESORCE data base for the SIGMA project. The experiments show that GP models have a better generalization error than a simple parametric regression model. A visual assessment of different scenarios demonstrates that the inferred GP models are physically plausible.     

Comparing directional line sets using non-parametric statistics: a new approach for geoenvironmental applications

The need to compare the distributions of directions of the discontinuities present in rock masses prompted the development of a new surrogate measure for non-parametric statistical tests. This is used to quantify the degree of matching between polymodal azimuth direction distributions determined from remotely sensed data for different areas and also between these and field measurements. The approach is based on an application of the Kolmogorov–Smirnov (K–S) goodness-of-fit test. However, in this application the main interest is to accept the null hypothesis (instead of rejecting it) so that there is a risk of committing a Type II statistical error when it is false, particularly if sample sizes are too small. Therefore, a method that employs a set of empirical criteria for calibrating the statistical decision was devised. The statistic used (D _ratio) provides a measure of the degree of reliability about the decision on whether or not to accept or reject the hypothesis. The methodology is tested and implemented using existing geological data and a tectonic model valid over a limited region, within which two study areas were taken for these developments. The results obtained indicate reasonable improvement of the performance of K–S tests for inferential purposes when empirical reliability criteria are used. This was acknowledged by increased matching between occurring and inferred discontinuities (tectonic structures) and reduction in rates of errors. Other applications envisaged include different data sources such as climate and soil data.     

Power of the Mann–Whitney test for detecting a shift in median or mean of hydro-meteorological data

 The non-parametric Mann–Whitney (MW) statistic test has been popularly used to assess the significance of a shift in median or mean of hydro-meteorological time series. It has been considered that the test is more suitable for non-normally distributed data and it may be not sensitive to the distribution type of sample data. However, no evidence has been provided to demonstrate these. This study investigates the power of the test in various circumstances by means of Monte Carlo simulation. Simulation results demonstrate that the power of the test is very sensitive to various properties of sample data. The power depends on the pre-assigned significance level, magnitude of a shift, sample size, and its occurrence position within a time series; and it is also strongly affected by the variation, skewness, and distribution type of a time series. The bigger the magnitude of a shift, the more powerful the test is; the larger the sample size, the more powerful the test is; and the bigger the variation within a time series, the less power the test has. The test has the highest power if a shift occurs at the midpoint of a time series. For the samples with different distribution types, the power of the test is dramatically different. The test has the highest power for time series with the extreme value type III (EV3) distribution while it indicates the lowest power for time series with the lognormal distribution.     

高阶统计量在大地电磁测深数据处理中的应用研究

在大地电磁（MT）测深法中,大地电磁响应函数经常出现个别频点分散、误差棒较大、形态异常等现象,在作反演解释时,许多地质特征难以有效提取出来,这些问题严重阻碍了MT的实际应用效能和发展.本文利用高阶统计量方法检验了MT信号的高斯性. 结果表明,MT信号是非高斯的. 根据任何高斯过程,其高阶统计量（高阶累积量,高阶谱）均为零的性质,通过信号的高阶谱恢复功率谱,再由功率谱估算MT响应函数,能有效抑制高斯有色噪声的影响,提高MT资料的处理质量. 从应用效果看,这种方法在抑制高斯有色噪声、提取信号中有用信息方面优于传统功率谱方法.     

Non-parametric seismic hazard analysis in the presence of incomplete data

The distribution of earthquake magnitudes plays a crucial role in the estimation of seismic hazard parameters. Due to the complexity of earthquake magnitude distribution, non-parametric approaches are recommended over classical parametric methods. The main deficiency of the non-parametric approach is the lack of complete magnitude data in almost all cases. This study aims to introduce an imputation procedure for completing earthquake catalog data that will allow the catalog to be used for non-parametric density estimation. Using a Monte Carlo simulation, the efficiency of introduced approach is investigated. This study indicates that when a magnitude catalog is incomplete, the imputation procedure can provide an appropriate tool for seismic hazard assessment. As an illustration, the imputation procedure was applied to estimate earthquake magnitude distribution in Tehran, the capital city of Iran.     

Assessment of modified Anderson–Darling test statistics for the generalized extreme value and generalized logistic distributions

An important problem in frequency analysis is the selection of an appropriate probability distribution for a given sample data. This selection is generally based on goodness-of-fit tests. The goodness-of-fit method is an effective means of examining how well a sample data agrees with an assumed probability distribution as its population. However, the goodness of fit test based on empirical distribution functions gives equal weight to differences between empirical and theoretical distribution functions corresponding to all observations. To overcome this drawback, the modified Anderson–Darling test was suggested by Ahmad et al. (1988b). In this study, the critical values of the modified Anderson–Darling test statistics are revised using simulation experiments with extensions of the shape parameters for the GEV and GLO distributions, and a power study is performed to test the performance of the modified Anderson–Darling test. The results of the power study show that the modified Anderson–Darling test is more powerful than traditional tests such as the χ², Kolmogorov–Smirnov, and Cramer von Mises tests. In addition, to compare the results of these goodness-of-fit tests, the modified Anderson–Darling test is applied to the annual maximum rainfall data in Korea.     

基于幂指数法的地震模拟振动台频域前馈补偿方法

针对地震模拟振动台受到系统和试件非线性等因素影响,导致波形再现精度不高的问题,提出基于幂指数法的地震模拟振动台频域前馈补偿方法以提高振动台的控制效果,通过数值仿真和实际振动台试验对该方法的控制性能进行研究。研究结果表明：基于幂指数法的地震模拟振动台频域前馈补偿方法能够快速收敛,迭代次数较少,可以大幅提高振动台的波形复现精度,能够提高振动台的控制性能。     

Non-parametric identification of a class of nonlinear multidegree dynamic systems

A non-parametric identification technique is presented for chain-like multidegree-of-freedom non-linear dynamic systems. The method uses information about the state variables of non-linear systems to express the system characteristics in terms of two-dimensional orthogonal functions. The technique is applied to a model of a steel frame that has been extensively investigated both analytically and experimentally. The method can be used with deterministic or random excitation to identify dynamic systems with arbitrary non-linearities, including those with hysteretic characteristics. It is also shown that the method is easy to implement and needs much less computer time and storage requirements compared to the Wiener-kernel approach. The method is shown to have low sensitivity to the effects of additive noise in the experimental data.     

Spatial correlation of earthquake ground motion: non-parametric estimation

A non-parametric method, called multidimensional correlation mapping (MCM), is used to describe the local spatial correlation of different components of earthquake ground acceleration. The method estimates spatial correlation without presuming homogeneity or isotropy of the phase-aligned local ground motion fields. For selected events recorded by the SMART1 accelerograph array, we obtain contour plots of equal correlation of ground acceleration, with respect to the center of the array. Displacement time-histories are also computed from the accelerograms and the spatial correlation of the displacements is estimated for comparison with that of the accelerations.     

二维叠后地震数据的平稳随机介质参数估计

随机介质参数估计是随机介质理论应用于地震勘探的关键.本文提出了一种从二维叠后地震数据中估计平稳随机介质参数的方法.文中阐述了二维叠后地震数据与随机介质波阻抗模型的关系,以及随机介质自相关函数参数的估计原理和方法,并结合实例详细介绍了应用功率谱法进行随机介质参数估计的具体步骤;通过多个二维理论模型的估计试验,验证了方法的可行性和正确性;还对实际地震数据进行了随机介质参数的估计试验,结果表明,随机介质参数可以为三角洲沉积相的进一步划分提供参考,反映了该方法有较好的应用前景.相比前人的研究,本文所提出的随机介质参数估计方法是一种真正的二维算法,特别是能给出自相关角度θ的估计,这种基于功率谱的估计方法具有直观且高效率的优点,但也存在着误差较大的问题,需要进一步的改进和完善.     

基于连续小波变换的大地电磁信号谱估计方法

在基于连续小波变换的大地电磁信号谱估计方法中 ,通过引入整体平均、小波系数收缩和显著性检验等统计技术 ,以提高谱估计的精度 .文中同时讨论了连续小波变换中各种参数的选取问题 ,给出了Morlet小波函数中尺度与傅里叶频率之间转换的经验公式 ,并给出了谱估计的具体算法 .结果表明 ,本文方法可有效压制较强的白噪声和局部相关噪声 .与FFT谱估计方法相比 ,该方法大大降低了对信号记录长度的要求 ,因而对大地电磁信号的处理有实际意义 .