Bedrock structure in Adapazari, Turkey—a possible cause of severe damage by the 1999 Kociaeli earthquake

The 1999 Kocaeli earthquake brought serious damage to downtown of Adapazari. To study why strong motions were generated at the town, a bedrock structure was investigated on the basis of Bouguer gravity anomaly, and SPAC and H/V analyses of microseisms. It was revealed that, the basin consists of three narrow depressions of bedrock with very steep edges, extending in E–W or NE–SW directions along the North Anatolia faults, and the depth to bedrock reaches 1000 m or more. Downtown of Adapazari is located 1–2 km apart from the basin-edge. It is considered that, the specific configuration of bedrock amplifies ground motions at the downtown area by focusing of seismic waves and/or interference between incident S-waves and surface-waves secondarily generated at the basin-edge. Studying 3D bedrock structure is an urgent issue for microzoning an urban area in a sedimentary basin.     

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO ₂ are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO ₂ ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols c _i Scalar factor that minimizes the discrepancy between andU _i - D Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients - d Distance for which transition matrixU is estimated - d _ij Distance between observed valuesi andj - E Expected value - E _i Row vector of residuals in the standardized model - F(d _ij) Quadratic function of distanced _ij F(d _ij)=a+bd _ij+cd _ij ² - L Diagonal matrix of the eigenvalues ofU - _i Eigenvalue of the matrixU;ith diagonal element ofL - N Number of observations - p Number of variables - Q Total predictive power ofU - R Correlation matrix of the variables - R _0j Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m) - R _1j Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood - R _m ² Multiple correlation coefficient squared for themth variable - S _i Column vectori of the signal values - s _k ² Residual variance for variablek - T _i Amplitude vector corresponding toV _i;ith row ofT=V ^–1 - T Total variation in the system - U Nonsymmetric transition matrix formed by post-multiplyingR ₀₁ ^–1 byR _ij - U _i Componenti of the matrixU, corresponding to theith eigenvectorV _i;U _i= _iV_iT_i - U* _i ComponentU _i multiplied byc _i - U _ij Sum of componentsU _i+U _j - V _i Eigenvector of the matrixU;ith column ofV withUV=VL - w Weighting factor; equal to the ratio of two eigenvalues - X _i Random variable at pointi - x _i Value of random variable at pointi - y _i Residual ofx _i - Z _i Row vectori for the standardized variables - z _i Standardized value of variable     

下辽河平原浅层地下水环境风险评价及空间关联特征

以下辽河平原浅层地下水为研究对象,将自然灾害风险理论引入地下水环境风险评价,从脆弱性、功能性、胁迫性、适应性4个方面选取指标,构建地下水环境风险评价指标体系和模型。运用GIS空间分析方法对地下水环境风险进行评价,并对风险值进行空间关联特征研究。结果表明：① 研究区内地下水环境中度以上风险区占整个研究区面积的63.12%,其中高风险区占6.79%,较高风险区占18.96%,中等风险区占37.37%;较低风险区占21.98%,低风险区占14.90%。② 地下水环境风险最高的地区主要位于下辽河平原中部的新民市东北部、灯塔市、辽中县西部、黑山县部分地区及凌海市东南部。③ 研究区内地下水环境风险呈现较高的正相关性,相似性高的区域主要分布在中东和中西部风险高值区,以及东北和东南部风险低值区。研究成果丰富了地下水环境风险理论,对下辽河平原地下水环境保护实践具有一定的理论和实践意义。     

中国人口死亡率空间格局演变及其影响因素

人口死亡率是人口转型关键因素之一,而人口转型又关系着地区社会经济发展质量。以2000—2015年粗死亡率数据为基础,通过变异系数、趋势分析和空间自相关分析中国人口死亡率空间格局差异及演变态势,并结合偏最小二乘回归定量分析人口死亡率影响因素。结果发现：（1）中国人口死亡率大致呈西北、东北和东南低,中部及西南高态势,且各时段人口死亡率降低省份高于增长省份;（2）中国各省死亡率变异系数差异悬殊,且在空间趋势中东西方向呈现两端低中间高、南北方向由两端低中间高向北低南高转变;（3）人口死亡率全局空间集聚程度不断减弱,区域差异缩小,且局部空间上以高-高和低-低聚集为主;（4）人口老龄化是人口死亡率提升的主要推动因子,其次分别为工业废水排放总量、突发环境事件次数和气候条件等。人均生产总值对人口死亡率起主要抑制作用,其次分别为每千人口床位数、每十万人口大学平均在校生数、人均用水量和空气质量。农作物受灾面积合计对人口死亡率在不同年份作用力不一致。     

北京市人口密度空间分布及其尺度特性研究

利用北京市第六次人口普查数据,在对人口统计数据进行空间化处理的基础上,分析了城市人口密度分布的统计特征及其空间自相关性,充分证明了北京市人口密度分布具有非常强烈的空间自相关性,并已经凸显出城市人口密度多核心分布模型的特征;在对人口统计数据进行格网化处理的基础上,分析了6种不同尺度下北京市人口密度空间分布的统计学特性和空间自相关性,证明了北京市人口密度的空间分布具有尺度特性;本文进一步对尺度特性影响下的人口密度空间分布特性空间自相关性进行了对比研究,从而揭示出空间自相关性与尺度变化间的规律。     

考虑超随机特性的人工地震动合成

文中根据自相关函数随机噪声部分的2个重要性质提出了自相关函数随机噪声部分的迭代生成算法,并给出了以确定性自相关函数为目标函数,考虑随机噪声部分影响的人工合成地震动方法.本文的研究结果表明地震动加速度的平方和（SS）比峰值加速度（PGA）更好地量度了地震地面运动强度.文中用蒙特卡罗方法分析了自相关函数噪声项对人工合成地震动幅值和反应谱的影响：由于自相关函数的随机噪声项对地震动总功率或平方和的贡献为0,因此随机噪声项对合成地震动幅值的影响不大,但由于自相关函数随机噪声项明显改变了地震动功率谱在频域上的分布特征,使原本光滑的平均反应谱出现了明显的波动,从而在一定程度上提高了平均反应谱的峰值,使自相关函数噪声部分对平均反应谱也有一些影响.     

中国高技能劳动力与一般劳动力的空间分布格局及其影响因素

基于2000和2010年全国人口普查分县数据和地级行政单元数据,采用不均衡指数和空间自相关分析等方法,刻画中国高技能劳动力和一般劳动力的空间分布格局及其变化,并采用空间滞后模型,识别高技能劳动力与一般劳动力空间分布的决定因素及其变化。结果表明：① 两类劳动力总体的空间特征均为东南密集,西北稀疏;② 2000—2010年,高技能劳动力在空间分布上呈集中化的趋势,而一般劳动力呈分散化的趋势;③ 十年间高技能劳动力集聚区虹吸作用加强,其分布越发集中于沿海特大城市群,而一般劳动力集聚区回波作用加强,其分布越发均衡;④ 空间回归分析结果表明,职工平均工资、是否直辖市或省会、每万人在校高校学生、是否沿海城市、常住人口数和空间溢出效应在十年中一直都是决定高技能劳动力和一般劳动力空间分布的主要因素,而失业率、中学生师比、二氧化硫排放量和绿地率在2010年也成为影响两类劳动力空间分布的主要因素。对比两类劳动力的模型结果可得,高技能劳动力的空间分布受行政因素、高校因素和地区生活舒适度的影响更大,而一般劳动力的空间分布受劳动力市场因素的影响更大。     

辽中南城市群人口分布的时空演变特征

采用人口分布的结构指数和空间自相关分析方法,并结合GIS技术对20世纪90年代以来辽中南城市群人口分布的时空演变特征进行分析。研究结果表明:①2007年,辽中南城市群的人口分布具有各地市人口总量差异较大、人口密度呈西高东低态势、市县人口密度差异显著、人口沿交通轴线集中分布、人口分布的局部空间集聚现象十分显著等现状特征;②20世纪90年代以来,城市群的人口分布变化具有各地市人口增幅差异明显、人口分布具有不断集中趋势、人口重心逐渐由东北向西南方向移动,人口分布趋同趋势不断增强等演变特征。研究结果表明空间自相关的统计分析方法能够更好地揭示出人口的分布特征、人口集聚及其变化的热点,对于人口的合理布局方案等政府决策具有重要的参考价值。     

Auto-correlation analysis of wave heights in the Bay of Bengal

Time series observations of significant wave heights in the Bay of Bengal were subjected to auto-correlation analysis to determine temporal variability scale. The analysis indicates an exponential fall of auto-correlation in the first few hours with a decorrelation time scale of about six hours. A similar figure was found earlier for ocean surface winds. The nature of variation of auto-correlation with time lags was also found to be similar for winds and wave heights