Non-stationary frequency analysis of extreme precipitation in South Korea using peaks-over-threshold and annual maxima

The conventional approach to the frequency analysis of extreme precipitation is complicated by non-stationarity resulting from climate variability and change. This study utilized a non-stationary frequency analysis to better understand the time-varying behavior of short-duration (1-, 6-, 12-, and 24-h) precipitation extremes at 65 weather stations scattered across South Korea. Trends in precipitation extremes were diagnosed with respect to both annual maximum precipitation (AMP) and peaks-over-threshold (POT) extremes. Non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with model parameters made a linear function of time were applied to AMP and POT respectively. Trends detected using the Mann–Kendall test revealed that the stations showing an increasing trend in AMP extremes were concentrated in the mountainous areas (the northeast and southwest regions) of South Korea. Trend tests on POT extremes provided fairly different results, with a significantly reduced number of stations showing an increasing trend and with some stations showing a decreasing trend. For most of stations showing a statistically significant trend, non-stationary GEV and GPD models significantly outperformed their stationary counterparts, particularly for precipitation extremes with shorter durations. Due to a significant-increasing trend in the POT frequency found at a considerable number of stations (about 10 stations for each rainfall duration), the performance of modeling POT extremes was further improved with a non-homogeneous Poisson model. The large differences in design storm estimates between stationary and non-stationary models (design storm estimates from stationary models were significantly lower than the estimates of non-stationary models) demonstrated the challenges in relying on the stationary assumption when planning the design and management of water facilities. This study also highlighted the need of caution when quantifying design storms from POT and AMP extremes by showing a large discrepancy between the estimates from those two approaches.     

A fully non-stationary linear coregionalization model for multivariate random fields

This paper introduces an extension of the traditional stationary linear coregionalization model to handle the lack of stationarity. Under the proposed model, coregionalization matrices are spatially dependent, and basic univariate spatial dependence structures are non-stationary. A parameter estimation procedure of the proposed non-stationary linear coregionalization model is developed under the local stationarity framework. The proposed estimation procedure is based on the method of moments and involves a matrix-valued local stationary variogram kernel estimator, a weighted local least squares method in combination with a kernel smoothing technique. Local parameter estimates are knitted together for prediction and simulation purposes. The proposed non-stationary multivariate spatial modeling approach is illustrated using two real bivariate data examples. Prediction performance comparison is carried out with the classical stationary multivariate spatial modeling approach. According to several criteria, the prediction performance of the proposed non-stationary multivariate spatial modeling approach appears to be significantly better.     

Estimation of a non-stationary prior covariance from seismic data

Non-stationarity in statistical properties of the subsurface is often ignored. In a classical linear Bayesian inversion setting of seismic data, the prior distribution of physical parameters is often assumed to be stationary. Here we propose a new method of handling non-stationarity in the variance of physical parameters in seismic data. We propose to infer the model variance prior to inversion using maximum likelihood estimators in a sliding window approach. A traditional, and a localized shrinkage estimator is defined for inferring the prior model variance. The estimators are assessed in a synthetic base case with heterogeneous variance of the acoustic impedance in a zero-offset seismic cross section. Subsequently, this data is inverted for acoustic impedance using a non-stationary model set up with the inferred variances. Results indicate that prediction as well as posterior resolution is greatly improved using the non-stationary model compared with a common prior model with stationary variance. The localized shrinkage predictor is shown to be slightly more robust than the traditional estimator in terms of amplitude differences in the variance of acoustic impedance and size of local neighbourhood. Finally, we apply the methodology to a real data set from the North Sea basin. Inversion results show a more realistic posterior model than using a conventional approach with stationary variance.     

Non-stationary response of structures excited by random seismic processes with time variable frequency content

Seismic random processes are characterized by high non-stationarity and, in most cases, by a marked variability of frequency content. The hypothesis modeling seismic signal as a simple product of a stationary signal and a deterministic modulation function, consequently, is hardly ever applicable. Several mathematical models aimed at expressing the recorded process by means of a system of stationary random processes and deterministic amplitude and frequency modulations are proposed. Models oriented into the frequency domain with subsequent response analysis based on integral spectral resolution and models oriented into the time domain based on the multicomponent resolution are investigated. The resolution into individual components (non-stationary signals) is carried out by three methods. The resolution into intrinsic mode functions seems to possess the best characteristics and yields results almost not differing from the results obtained by stochastic simulation. An example of the seismic response of an existing bridge obtained by two older models and three variants of multicomponent resolution is given.     

强震地面运动超随机特性的几点注记

本文详细论述了功率谱与功率谱密度、平方和SS与平方平均值MS的适用范围及物理意义,比较深入地分析了平稳和非平稳加速度时间历程的统计特征,通过引入时域包线获取特定相位的方法,考虑了非平稳特性。本文基于非平稳时间历程,在不考虑频率非平稳性的基础上,提出了设计地震动三要素:(1)平方和SS;(2)归一化自相关函数或其对应的功率谱;(3)地震动时域包线.本文提出了在设计地震动合成中考虑超随机特性的实用方法,并指出地震动的超随机特性(即归一化功率谱的锯齿状波动部分,或归一化自相关函数的随机噪声部分)与相位的随机性是地震动随机特性的两个重要方面。本文将地震动的统计分析直接建立在非平稳时间历程的基础之上,从而避开了要对地震动进行平稳化处理的困难。这样提取出的地震动统计特征完全是以真实地震动记录为基础的。应用这些统计特征,可模拟出符合统计要求的基于非平稳时间历程的设计地震动。最后本文还讨论了工程上常用的将反应谱近似转换为功率谱的转换式的一些缺陷。     

Integration of multiple soft data sets in MPS thru multinomial logistic regression: a case study of gas hydrates

A new approach is described to allow conditioning to both hard data (HD) and soft data for a patch- and distance-based multiple-point geostatistical simulation. The multinomial logistic regression is used to quantify the link between HD and soft data. The soft data is converted by the logistic regression classifier into as many probability fields as there are categories. The local category proportions are used and compared to the average category probabilities within the patch. The conditioning to HD is obtained using alternative training images and by imposing large relative weights to HD. The conditioning to soft data is obtained by measuring the probability–proportion patch distance. Both 2D and 3D cases are considered. Synthetic cases show that a stationary TI can generate non-stationary realizations reproducing the HD, keeping the texture indicated by the TI and following the trends identified in probability maps obtained from soft data. A real case study, the Mallik methane-hydrate field, shows perfect reproduction of HD while keeping a good reproduction of the TI texture and probability trends.     

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.     

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.     

测井、井间地震与地面地震数据联合约束下的地质统计学随机建模方法研究

利用能够整合测井信息与井间地震信息的地质统计学随机模拟方法,结合传统的地质统计学反演思路,得到了一种能够同时整合测井、井间地震与地面地震三种先验信息的地质统计学反演与储层建模方法.由于井间射线信息、测井信息与地面地震数据在随机反演与建模过程当中都得到了尊重,因此与传统地质统计学反演仅利用了测井与地面地震数据相比,本文的地质统计学反演与建模方法更充分地利用了先验信息,有效提高了反演的精度,降低了随机建模中的多解性.基于理论数据的测试证实了上述观点.     

非平稳地震动过程模拟方法(Ⅱ)

在总结回顾具有同一统计特征非平稳地震动过程模拟的研究基础上, 提出了一个模拟产生与某个给定地震动记录具有同一统计特征[CD2]时变功率谱的非平稳地震动过程的方法. 方法具有如下一些特点: 模拟产生的样本过程在幅值上和频率上均是非平稳的, 幅值和频率的非平稳性均取决于目标时变功率谱; 任意一个样本过程的时变功率谱未必符合目标谱, 但在统计意义上却严格符合目标谱. 最后, 论文以Landers地震的一个加速度记录为例对方法进行了验证.      

Simulation of non-stationary ground motion processes (II)

Introduction When doing Monte Carlo analysis of stochastic earthquake response of nonlinear structures, it is necessary to have a great number of ground motion processes with an identical statistical feature; In theoretical analysis of earthquake resistance of structures, to study the effect of one parameter, it is required to keep other parameters fixed, and it is often needed one set of ground motion proc-esses having an identical statistical feature; Similarly, while doing structural model…     

Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

2013年海西5.0级地震波形时频特性研究

传统的傅里叶变换因缺乏信号局域性的信息,无法对地震波等非平稳信号进行全面描述,时频分析通常是分析非平稳信号较好的工具.本文利用大柴旦地震台记录到的2013年6月5日青海海西5.0级地震波形,采用赵-阿特拉斯-马克时频分布（ZAM）分别研究了该地震P波、S波段的时间-频率特征.研究结果很好地解释了地震波的非平稳性,较全面地展示了地震波的衰减特征.     

CHDS: conflict handling in direct sampling for stochastic simulation of spatial variables

Stochastic Environmental Research and Risk Assessment - In recent years, multiple-point geostatistical (MPS) approaches have gained significant popularity for modeling subsurface heterogeneity in...     

均质土坝非平稳随机地震反应分析

考虑地震荷载的随机性及强度、频率的非平稳性,基于作者提出的适用于非平稳随机过程的一般随机地震动模型,采用虚拟激励法,建立了非平稳随机地震反应分析方法,并将其应用于某实际均质土坝动力分析中。土石坝及坝基体系采用整体有限元离散,坝体和坝基材料的动力非线性性能以等效线性化方法考虑。首先,基于目标加速度时程的强度和能量信息,确定了作为输入的加速度时—频演变功率谱密度;其次,比较了确定性时程动力分析和非平稳随机分析的结果,探讨了频率非平稳随机地震激励下的土石坝地震反应特性;最后,比较了2种不同坝基条件下的土石坝非平稳随机地震反应,探讨了频率非平稳随机激励下的土石—坝基动力相互作用。分析结果表明：地震动的频率非平稳性对土石坝动力反应有一定影响;坝体—坝基动力相互作用在地震过程中的不同阶段表现有所不同,主震阶段的相互作用显著。     

Performance of the COSERO precipitation–runoff model under non-stationary conditions in basins with different climates

Abstract

This study is a contribution to a model intercomparison experiment initiated during a workshop at the 2013 IAHS conference in Göteborg, Sweden. We present discharge simulations with the conceptual precipitation–runoff model COSERO in 11 basins located under different climates in Europe, Africa and Australia. All of the basins exhibit some form of non-stationary conditions, due, for example, to warming, droughts or land-cover change. The evaluation of the daily discharge simulations focuses on the overall model performance and its decomposition into three components measuring temporal dynamics, mean flow volume and distribution of flows. Calibration performance is similarly high as in previous COSERO applications. However, when looking at evaluation periods independent of the calibration, the model performance drops considerably, mainly due to severely biased discharge simulations in semi-arid basins with strong non-stationarity in rainfall. Simulations are more robust in European basins with humid climates. This highlights the fact that hydrological models frequently fail when simulations are required outside of calibration conditions in basins with non-stationary conditions. As a consequence, calibration periods should be sufficiently long to include both wet and dry periods, which should yield more robust predictions.     

A comparison of approaches to include outcrop information in overburden thickness estimation

The estimation of overburden sediment thickness is important in hydrogeology, geotechnics and geophysics. Usually, thickness is known precisely at a few sparse borehole data. To improve precision of estimation, one useful complementary information is the known position of outcrops. One intuitive approach is to code the outcrops as zero thickness data. A problem with this approach is that the outcrops are preferentially observed compared to other thickness information. This introduces a strong bias in the thickness estimation that kriging is not able to remove. We consider a new approach to incorporate point or surface outcrop information based on the use of a non-stationary covariance model in kriging. The non-stationary model is defined so as to restrict the distance of influence of the outcrops. Within this distance of influence, covariance parameters are assumed simple regular functions of the distance to the nearest outcrop. Outside the distance of influence of the outcrops, the thickness covariance is assumed stationary. The distance of influence is obtained thru a cross-validation. Compared to kriging based on a stationary model with or without zero thickness at outcrop locations, the non-stationary model provides more precise estimation, especially at points close to an outcrop. Moreover, the thickness map obtained with the non-stationary covariance model is more realistic since it forces the estimates to zero close to outcrops without the bias incurred when outcrops are simply treated as zero thickness in a stationary model.     

Geostatistical simulation of random fields with bivariate isofactorial distributions by adding mosaic models

This work deals with the geostatistical simulation of a family of stationary random field models with bivariate isofactorial distributions. Such models are defined as the sum of independent random fields with mosaic-type bivariate distributions and infinitely divisible univariate distributions. For practical applications, dead leaf tessellations are used since they provide a wide range of models and allow conditioning the realizations to a set of data via an iterative procedure (simulated annealing). The model parameters can be determined by comparing the data variogram and madogram, and enable to control the spatial connectivity of the extreme values in the realizations. An illustration to a forest dataset is presented, for which a negative binomial model is used to characterize the distribution of coniferous trees over a wooded area.     

经验模态分解法在地震资料处理中的应用试验

通过对多种连续小波基的平稳序列时频分析,提出经验模态的非平稳序列的时频分析方法,并在地震资料处理解释应用中作探索性试验.试验结果表明：对地震记录道长为5 s和2 ms采样资料,经验模态分解后可获得6-7个固有模态函数单分量,分解的第一个分量C1（t）具有较宽的频率成分,可作为地震属性的主分量.     

Detecting and quantifying sources of non-stationarity via experimental semivariogram modeling

Conventional geostatistics often relies on the assumption of second order stationarity of the random function (RF). Generally, local means and local variances of the random variables (RVs) are assumed to be constant throughout the domain. Large scale differences in the local means and local variances of the RVs are referred to as trends. Two problems of building geostatistical models in presence of mean trends are: (1) inflation of the conditional variances and (2) the spatial continuity is exaggerated. Variance trends on the other hand cause conditional variances to be over-estimated in certain regions of the domain and under-estimated in other areas. In both cases the uncertainty characterized by the geostatistical model is improperly assessed. This paper proposes a new approach to identify the presence and contribution of mean and variance trends in the domain via calculation of the experimental semivariogram. The traditional experimental semivariogram expression is decomposed into three components: (1) the mean trend, (2) the variance trend and (3) the stationary component. Under stationary conditions, both the mean and the variance trend components should be close to zero. This proposed approach is intended to be used in the early stages of data analysis when domains are being defined or to verify the impact of detrending techniques in the conditioning dataset for validating domains. This approach determines the source of a trend, thereby facilitating the choice of a suitable detrending method for effective resource modeling.