世界石油探明储量分布特征与空间格局演化

石油探明储量是一个动态变化的过程,文章对1980年以来不同区域尺度,包括全球、各大区以及国家层面的石油探明储量变化、分布特征等进行分析,得出结论:(1)世界石油探明储量自1980年以来大致经历了4个阶段,呈现明显的阶梯状增长的态势,基本每十年出现一次储量跃升,并保持一段时间的平稳。储采比一直稳定在40年以上,呈现缓慢上升的趋势。(2)大区尺度的石油探明储量分布不均衡,且探明储量的变化趋势不同。中东一直是石油探明储量最大的地区,其次为中南美洲地区。中东、北美占世界比重先升后降。非洲和中南美洲稳步提升,亚太地区持续下滑。(3)国家层面的石油探明储量呈现明显的集中分布。储量前4的国家占世界储量的53.75%,储量超过10亿吨的国家在很大程度上主导着世界石油开发的基本格局。从各国演变来看,世界石油储量呈现出多极化的趋势,从中东、北美向中亚、俄罗斯和中南美洲等转移。     

比值R分析及其在宁夏中强地震预报中的应用

本文提出比值Ｒ分析计算方法，用其求解时间序列数据矩平差和标准差结构的分形，目的的于判定地震预报指标，作为实例，宁夏区和同心区的地震频度，最大震级，以及同心地办水氡观测数据的比值Ｒ随时间的分形变化进行了初步分析，证明本方法能为我国提供可信的震情信息。     

库水位波动条件下万州瓦窑坪滑坡稳定性分析

库水位波动对滑坡的稳定性变化有重要影响,传统的定值分析方法未能考虑土体强度参数取值的不定性,而概率分析法能考虑土体强度参数分布的随机性,对库区滑坡的稳定性分析提供了更有力的依据。文章以万州瓦窑坪滑坡为例,在基于库水位实际波动进行拟合的条件下对滑坡的渗流场进行了模拟,同时对不同水位下滑坡的稳定性和破坏概率进行了分析。结果表明,滑坡在库水位波动条件下整体稳定性系数均大于1,处于基本稳定状态,滑坡具有低危险性。库水位下降到160.47 m水位时,边坡破坏概率超过30%,具有中等危险性。采用R/S分析法对监测数据进行分析,结果表明滑坡具有持续性的小变形,滑坡处于基本稳定状态,与稳定性分析结果一致。     

毛乌素沙地1960-2013年极端气温变化

毛乌素沙地处于多层次生态地理景观过渡带,拥有丰富的自然及人文资源.研究该生态脆弱区极端气温变化,有利于揭示全球气温变化与局地气温响应之间的复杂关系,为当地的可持续发展和气象灾害预防提供参考。通过线性趋势分析、累计距平、Mann-Kendall法、因子分析、R/S分析等方法,分析了该地极端气温指数的变化特征\,突变特点、相互关系,探讨了该地区气温变化的空间差异和极端气温未来的变化趋势。结果表明:(1)极端最低气温、极端最高气温、夏日日数、热夜日数、暖夜日数、暖日日数分别以不同的速率上升;冰日日数、霜冻日数、冷夜日数、冷日日数分别以不同的速率下降。(2)除极端最高气温、极端最低气温外,其余8项极端气温指数均发生突变,突变年份集中在20世纪90年代。(3)冷指数大幅下降是毛乌素沙地极端气温变化的主要特点,并且各冷指数间、各暖指数间相关关系显著。(4)毛乌素沙地极端气温在空间上表现出不同的变化特点,东北方向(榆林及鄂托克旗)冷指数呈现大幅减少,西南方向(横山及盐池)暖指数呈现大幅增加。(5)ENSO事件具有使毛乌素沙地极端最高气温降低的作用以及使极端最低气温升高的作用。(6)10项极端气温指数的Hurst指数均大于0.5,表明未来毛乌素沙地极端气温仍将呈现冷指数下降、暖指数上升的变化趋势。     

龙门山平驿铺组沉积体系及旋回层序研究

本文首次从龙门山区下泥盆统平驿铺组中，划分出河口湾、三角洲、滨岸和陆棚四个沉积体系和三个Ⅲ级Ｔ－Ｒ旋回层序。平面上，四个沉积体系组成了扬子板块西侧的古大陆边缘由过渡相区和滨岸相区相间分布的古地理格局；垂向上，两相区中的三个Ⅲ级Ｔ—Ｒ旋回层序也由不同的沉积体系组成，旋回层序的演化虽然受构造差异沉降影响，但仍以Ⅲ级海平面升降变化为主要控制因素，并具同步演化规律，可分别代表古大陆边缘活动型和相对稳定型的两种Ⅲ级Ｔ—Ｒ旋回层序模式。     

循环经济时空信息流与示范应用分析

本文从循环经济的背景入手,叙述了循环经济的必要性,介绍了循环体系中物质的时空特性,物质信息的时空流动性以及流动过程中的信息增熵与信息负熵问题,并选择了具有一定特性的科技示范园作为实验支撑,在蟹岛绿色生态度假村的实践中展现了信息流分析在资源节约利用方面显示的优势,最后笔者阐述了信息化技术在循环经济发展过程中的作用及发展趋势。     

沿高变刚度设支撑高层大开间异型柱框架抗震性能试验研究

提出了沿异型住框架楼层变刚度设支撑的设计方法,进行了1／3结构模型试验研究,测试分析了弹性侧移刚度、结构的承载力、延性、滞回曲线、破坏机制及刚度衰减特性。计算所得的结构弹性侧移刚度与实测值符合较好．     

A hybrid method for the vulnerability assessment of R/C and URM buildings

The methodology followed by the Aristotle University (AUTh) team for the vulnerability assessment of reinforced concrete (R/C) and unreinforced masonry (URM) structures is presented. The paper focuses on the derivation of vulnerability (fragility) curves in terms of peak ground acceleration (PGA), as well as spectral displacement (s _d), and also includes the estimation of capacity curves, for several R/C and URM building types. The vulnerability assessment methodology is based on the hybrid approach developed at AUTh, which combines statistical data with appropriately processed (utilising repair cost models) results from nonlinear dynamic or static analyses, that permit extrapolation of statistical data to PGA’s and/or spectral displacements for which no data are available. The statistical data used herein are from earthquake-damaged greek buildings. An extensive numerical study is carried out, wherein a large number of building types (representing most of the common typologies in S. Europe) are modelled and analysed. Vulnerability curves for several damage states are then derived using the aforementioned hybrid approach. These curves are subsequently used in combination with the mean spectrum of the Microzonation study of Thessaloniki as the basis for the derivation of new vulnerability curves involving spectral quantities. Pushover curves are derived for all building types, then reduced to standard capacity curves, and can easily be used together with the S _d fragility curves as an alternative for developing seismic risk scenarios.     

Fractal Method for Statistical Analysis Geological Data

This paper establishes the phase space in the light of spacial series data,discussesthe fractal structure of geological data in terms of correlated functions and studies thechaos of these data.In addition,it introduces the R/S analysis for time series analysisinto spacial series to calculate the structural fractal dimensions of ranges and standard de-viation for spacial series data,and to establish the fractal dimension matrix and the proce-dures in plotting the fractal dimension anomaly diagram with vector distances of fractal di-mension.At last,it has examples of its application.     

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NO_x during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO₂, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O₃ and NO_x) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.