Instantaneous global spatial interaction? Exploring the Gaussian inequality, distance and Internet pings in a global network

The Internet has been publicly portrayed as a new technological horizon yielding instantaneous interaction to a point where geography no longer matters. This research aims to dispel this impression by applying a dynamic form of trip modelling to investigate pings in a global computer network compiled by the Stanford Linear Accelerator Centre (SLAC) from 1998 to 2004. Internet flows have been predicted to have the same mathematical operators as trips to a supermarket, since they are both periodic and constrained by a distance metric. Both actual and virtual trips are part of a spectrum of origin–destination pairs in the time–space convergence of trip time-lines. Internet interaction is very near to the convergence of these time-lines (at a very small time scale in milliseconds, but with interactions over thousands of kilometres). There is a lag effect and this is formalised by the derivation of Gaussian and gravity inequalities between the time taken (Δt) and the partitioning of distance (Δx). This inequality seems to be robust for a regression of Δt to Δx in the SLAC data set for each year (1998 to 2004). There is a constant ‘forbidden zone’ in the interaction, underpinned by the fact that pings do not travel faster than the speed of light. Superimposed upon this zone is the network capacity where a linear regression of Δt to Δx is a proxy summarising global Internet connectivity for that year. The results suggest that there has been a substantial improvement in connectivity over the period with R ² increasing steadily from 0.39 to 0.65 from less Gaussian spreading of the ping latencies. Further, the regression line shifts towards the inequality boundary from 1998 to 2004, where the increased slope shows a greater proportional rise in local connectivity over global connectivity. A conclusion is that national geography still does matter in spatial interaction modelling of the Internet.     

Bayesian Modelling of Spatial Data Using Markov Random Fields, With Application to Elemental Composition of Forest Soil

Spatial datasets are common in the environmental sciences. In this study we suggest a hierarchical model for a spatial stochastic field. The main focus of this article is to approximate a stochastic field with a Gaussian Markov Random Field (GMRF) to exploit computational advantages of the Markov field, concerning predictions, etc. The variation of the stochastic field is modelled as a linear trend plus microvariation in the form of a GMRF defined on a lattice. To estimate model parameters we adopt a Bayesian perspective, and use Monte Carlo integration with samples from Markov Chain simulations. Our methods does not demand lattice, or near-lattice data, but are developed for a general spatial data-set, leaving the lattice to be specified by the modeller. The model selection problem that comes with the artificial grid is in this article addressed with cross-validation, but we also suggest other alternatives. From the application of the methods to a data set of elemental composition of forest soil, we obtained predictive distributions at arbitrary locations as well as estimates of model parameters.     

INTEGRATED FRACTIONS SKILL SCORE FOR THE ASSESSMENT OF PRECIPITATION

Traditional skill scores (e.g., the threat score) used in the high-resolution verification of precipitation are affected by a “double penalty” caused by slight spatial or temporal displacements, which can lead to misleading evaluations. The fractions skill score (FSS) is a popular spatial verificaiton measure that can be used to solve these problems. It can determine useful and skillful scores by neighborhood analysis, which can be used to monitor the performance of operational forecasts. However, the FSS provides different scores at each spatial scale and it is difficult to obtain a definite score for the assessment of precipitation to analyze the temporal variabilities of daily forecasts. We previously reported a modified FSS assessment method and showed that a particular analysis scale had a significant advantage in the verification of operational forecasts of precipitation. To compensate for the lack of artificial definition in the analysis scale, we report here a new integrated score that satisfies a Gaussian weight function to average the FSS over all scales. We describe the advantages of the new score in the verification of forecasts of daily and hourly precipitation, taking forecast products from the GRAPES regional model and quantitative precipitation estimation products from the National Meteorological Information Center during June and July 2017 and investigating the differences between these results and those obtained with the traditional category score. We found that a value of 0.5 can be used as a standard for the skillful FSS in the forecast of heavy rainfall. The integrated score can maintain all the advantages seen in previous studies in the verification of daily and hourly precipitation and show excellent application prospects. The long-term verification including different seasons also find that the score can effectively improve the identification characteristics of the assessment.     

黄土丘陵沟壑区村域生态系统服务簇的时空分异及其地形梯度分析

探究村域生态系统服务簇对于厘清黄土丘陵沟壑区农业生态系统复杂特征、从多重服务供给角度提高服务的福祉贡献具有重要意义。论文以黄土丘陵沟壑区米脂县为例,基于高斯混合模型识别2009年和2019年村域生态系统服务簇并分析其时空分布,同时,结合地形分布指数探究服务簇在地形梯度上的分布模式。结果如下： ① 各类服务空间分异明显,且服务之间空间分布差异较大;近10 a间各类服务水平均有不同程度提高,其中调节服务有较大改善,而食物供给与文化服务增幅较小;各类服务空间变化格局存在较大差异,这与土地利用的空间演变关系密切。② 利用高斯混合模型识别4类服务簇,包括农业生产簇、农业美学簇、生态保育簇与城镇休闲簇;各类服务簇空间分异明显,且研究期间存在明显数量和空间转移变化;农业美学簇与生态保育簇是当地主导服务簇,且主导性增强,而农业生产簇与城镇休闲簇比重下降。③ 地形起伏度与服务簇的空间关联密切。随起伏度上升,农业生产簇与农业美学簇呈倒U型,生态保育簇优势分布增加,城镇休闲簇优势分布减弱;研究期间农业生产簇与城镇休闲簇向低起伏度的转移趋势明显,而农业美学簇与生态保育簇趋向于向高起伏度转移。     

TI介质局部角度域高斯束叠前深度偏移成像

各向异性射线理论基础上的局部角度域叠前深度偏移方法能够为深度域构造成像与基于角道集的层析反演提供有力支撑,但是对于复杂地质构造而言,高斯度叠前深度偏移在不失高效、灵活等特点的情况下,具有明显的精度优势.为此,本文研究局部角度域理论框架下的高斯束叠前深度偏移方法.为提高算法效率与实用性,文中讨论了一种从经典弹性参数表征的各向异性介质运动学和动力学射线方程演变而来的由相速度表征的简便形式,并提出了一种比较经济的各向异性高斯束近似合成方案.结合地震波局部角度域成像原理,讨论一种适合高斯束偏移的角度参数计算方法.国际上通用的理论模型合成数据试验表明：相比局部角度域Kirchhoff叠前深度偏移成像方法,本文方法具有更高的成像精度与抗噪能力,既适用于复杂构造成像,也可为TI介质深度域偏移速度分析与模型建立提供高效的偏移引擎.     

The Hurst phenomenon and fractional Gaussian noise made easy

Abstract

The Hurst phenomenon, which characterizes hydrological and other geophysical time series, is formulated and studied in an easy manner in terms of the variance and autocorrelation of a stochastic process on multiple temporal scales. In addition, a simple explanation of the Hurst phenomenon based on the fluctuation of a hydrological process upon different temporal scales is presented. The stochastic process that was devised to represent the Hurst phenomenon, i.e. the fractional Gaussian noise, is also studied on the same grounds. Based on its studied properties, three simple and fast methods to generate fractional Gaussian noise, or good approximations of it, are proposed.     

Mixture model based multivariate statistical analysis of multiply censored environmental data

Environmental data are commonly constrained by a detection limit (DL) because of the restriction of experimental apparatus. In particular due to the changes of experimental units or assay methods, the observed data are often cut off by more than one DL. Measurements below the DLs are typically replaced by an arbitrary value such as zeros, half of DLs, or DLs for convenience of analysis. However, this method is widely considered unreliable and prone to bias. In contrast, maximum likelihood estimation (MLE) method for censored data has been developed for better performance and statistical justification. However, the existing MLE methods seldom address the multivariate context of censored environmental data especially for water quality. This paper proposes using a mixture model to flexibly approximate the underlying distribution of the observed data due to its good approximation capability and generation mechanism. In particular, Gaussian mixture model (GMM) is mainly focused in this study. To cope with the censored data with multiple DLs, an expectation–maximization (EM) algorithm in a multivariate setting is developed. The proposed statistical analysis approach is verified from both the simulated data and real water quality data.     

Complex Resistivity Measurement Method and Model Simulation

The development and the current research situations at home and abroad of complex resistivity measurement were introduced and existing problems pointed out. A measurement method in time domain with Gaussian White Noise as input signal was proposed. The complex resistivity measurement model was built by simulink in matlab and the simulation was tested. The complex resistivity was obtained combined by time domain and frequency domain method. Then, the comparison and analysis between the calculated value and theoretical value were conducted, and results showed that the complex resistivity gained by this method was coincided with the real electrical characteristics of the model.     

一种基于GMM和Graph Cuts的图像分割方法

图像分割是图像处理中的基础问题。研究利用GMM构造T链的可行性,并探索一种可以兼顾精度与速度的确定GMM中K值的方法,结合GMM和Graph Cuts理论完成了对不同图像的分割实验。实验表明,文中的分割方法准确、有效。     

Application of Gaussian beam pre-stack depth migration in rugged seabed

Strong fluctuation of seabed, abrupt variation in depth and dip of seabed bring seismic imaging problems, such as irregular reflection waves, obvious multiple waves, serious lateral wave development, poor imaging on base surface and depression structure, low signal-to-noise ratio of middle and deep layers. In this paper, Gaussian beam migration imaging method is used to analyze the imaging effect of rugged seabed in deep water area, and the ray tracing method of wavefront construction method is used to analyze the kinematic characteristics of seismic waves. By improving the design of seismic data acquisition and observation system, imaging quality of fine structures is improved.