The Effect of Spatial Correlation of Cone Tip Resistance on the Bearing Capacity of Shallow Foundations

The effect of directional behaviour of correlation structure of cone tip resistance on the bearing capacity of shallow strip footing resting on cohesionless soil deposit in 2-D random field is analysed using probabilistic approach. The results obtained from the analysis show that the assumption of perfect (or infinite) correlation of cone tip resistance data leads to lower values of probability of failure. In contrast, the isotropic assumption of correlation behaviour based on vertical scale of fluctuation leads to higher values of probability of failure. The results also show that the transformation model would play a major role in the evaluation of variability of design property. In conclusion, the need for a proper evaluation methodology for calculation of correlation lengths of soil properties and their influence in foundation design is highlighted.     

Seismic interferometry by tangent‐phase correction

We present a modified interferometry method based on local tangent‐phase analysis, which corrects the cross‐correlated data before summation. The approach makes it possible to synthesize virtual signals usually vanishing in the conventional seismic interferometry summation. For a given pair of receivers and a set of different source positions, a plurality of virtual traces is obtained at new stationary projected points located along the signal wavefronts passing through the real reference receiver. The position of the projected points is estimated by minimizing travel times using wavefront constraint and correlation‐signal tangent information. The method uses mixed processing, which is partially based on velocity‐model knowledge and on data‐based blind interferometry. The approach can be used for selected events, including reflections with different stationary conditions and projected points with respect to those of the direct arrivals, to extend the interferometry representation in seismic exploration data where conventional illumination coverage is not sufficient to obtain the stationary‐phase condition. We discuss possible applications in crosswell geometry with a velocity anomaly and a time lapse.     

Kriging in the Presence of Locally Varying Anisotropy Using Non-Euclidean Distances

A stationary specification of anisotropy does not always capture the complexities of a geologic site. In this situation, the anisotropy can be varied locally. Directions of continuity and the range of the variogram can change depending on location within the domain being modeled. Kriging equations have been developed to use a local anisotropy specification within kriging neighborhoods; however, this approach does not account for variation in anisotropy within the kriging neighborhood. This paper presents an algorithm to determine the optimum path between points that results in the highest covariance in the presence of locally varying anisotropy. Using optimum paths increases covariance, results in lower estimation variance and leads to results that reflect important curvilinear structures. Although CPU intensive, the complex curvilinear structures of the kriged maps are important for process evaluation. Examples highlight the ability of this methodology to reproduce complex features that could not be generated with traditional kriging.     

Stationarity of Turbulence in Light Winds during the Maritime Continent Thunderstorm Experiment

Non-stationary turbulence can invalidate eddy flux calculations. Two-hour longrecords of wind velocity, temperature and humidity are classified as stationaryor non-stationary based on the behaviour of the flux as a function of Reynoldsaveraging period; a number of indicators of stationarity are investigated. Thetwo-hour Maritime Continent Thunderstorm Experiment wind datasets are notcompletely stationary, as indicated by the lack of a spectral gap, but can beclassified as approximately stationary, when the mean wind speed is greaterthan 3.8 m s^-1, or the standard deviation of true wind direction is 10°, or the ratio of horizontal wind variance to wind speed is <0.25. In the stationary case the calculated friction velocity exhibits a 7% decrease on average when the Reynolds averaging period is doubled, while data classified as non-stationaryexhibit an increase of 32%. There is little non-stationary behaviour in the kinematicheat fluxes, and is independent of the non-stationarity of the friction velocity. Thekinematic heat fluxes show small decreases (around 3%) when the Reynolds averagingperiod is doubled.     

陆地地震勘探随机噪声统计特性

在地震勘探随机噪声压制领域,噪声通常被假设为平稳、高斯随机过程的信息.然而,在某些情况下,这样的假设并不准确.本文应用现代统计检验方法对地震勘探随机噪声的平稳性、高斯性和线性进行了研究.结果表明地震勘探随机噪声并不是传统意义上认为的平稳随机过程,其平稳性受到噪声时长和采集环境复杂程度的影响.发现噪声时间越长,采集环境越复杂,随机噪声的平稳性越差,但是对于短时长随机噪声而言,其可以近似认为是平稳的.同时,采集环境的复杂程度也影响着随机噪声的高斯性和线性特性,环境条件越复杂,随机噪声高斯性越好,线性特性越差,但总的来说随机噪声可以归为线性非高斯随机过程.     

Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data

Statistical analysis of extremes currently assumes that data arise from a stationary process, although such an hypothesis is not easily assessable and should therefore be considered as an uncertainty. The aim of this paper is to describe a Bayesian framework for this purpose, considering several probabilistic models (stationary, step-change and linear trend models) and four extreme values distributions (exponential, generalized Pareto, Gumbel and GEV). Prior distributions are specified by using regional prior knowledge about quantiles. Posterior distributions are used to estimate parameters, quantify the probability of models and derive a realistic frequency analysis, which takes into account estimation, distribution and stationarity uncertainties. MCMC methods are needed for this purpose, and are described in the article. Finally, an application to a POT discharge series is presented, with an analysis of both occurrence process and peak distribution.     

On the estimation of spatial autocorrelation in geomorphology

Attention is drawn to the existence of different estimators of autocorrelation in one-dimensional series. It is shown that variation between estimates for series which appear only mildly nonstationary may be of the same order as sampling variation expected under stationarity. Hence it is important that investigators choose estimators carefully and state their choices explicitly.     

Is the wind wave frequency spectrum outdated

This paper presents a detailed examination of the practice of using the frequency spectrum to characterize wind waves. In particular, the issue of stationarity and Gaussian random process in connection with wind wave studies is addressed. We describe a test for nonstationarity based on the wavelet spectrum. When this test is applied to wind wave time series, the results significantly diverge from those expected for a Gaussian random process, thus casting critical doubts on the conventional concept of the wind wave frequency spectrum.     

Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

The present article reports studies to develop a univariate model to forecast the summer monsoon (June–August) rainfall over India. Based on the data pertaining to the period 1871–1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described.