影响b值计算误差的Monte Carlo实验研究

本文假设地震的发生与时间的关系遵循稳态泊松模型,假设地震震级M服从参数为1/β的指数分布,其中β=b/loge,在此基础上生成地震目录.用最小二乘法估算b值,分别从样本数量、震级间隔、震级误差三方面分析b值误差的大小.统计分析结果表明,b值的标准偏差随着样本数量的增多而减小;随着震级间隔的增大而减小;震级误差使b值的标准误差变大.本文可以为利用真实地震目录求b值时选择适当的方法提供参考.     

Assessing Natural and Cultural Determinants of Urban Forest Quality in Nanjing (China)

This study assessed underlying natural and cultural determinants of tree health and ways to improve urban forest performance in compact Nanjing city. Two groups of factors were investigated: (1) habitat conditions, including planting site, ground-cover, land cover, and district; and (2) tree traits, including species provenance and seasonality, and tree dimensions. A stratified sampling strategy selected 6351 trees for detailed assessment. The data were analyzed by multivariate logistic regressions. Institutional, park-garden and industrial trees with more open-space sites with vegetated ground-cover performed better than roadside and residential ones. Poor trees in confined residential sites deviated from those in other cities. Park-garden sites did not always nurture meritorious, large and diversified trees. District differentiation in tree performance was related to land cover. Exotic and evergreen species grew better than indigenous and deciduous, offering hints on species-habitat matching. Detailed field and statistical analyses could yield tree-environment information to enhance urban forestry research and management.     

转型期中国城镇居民住房类型分化及其影响因素——基于CGSS (2005) 的分析

转型时期我国城镇居民主要通过市场竞争机制和科层分配体制来获取个人住房,获取稀缺的住房资源的途径和机会的差异在一定程度上反映了住房不平等和社会分化。处于体制转型时期的中国城镇,制度、市场和家庭因素等多重因素相互交织,共同影响中国城镇居民住房选择。以覆盖全国88 个城镇的《中国综合社会调查(CGSS)》(2005 年城镇部分) 调查资料为基础,本文分析了转型背景下中国城镇居民住房类型分化及其影响因素,研究发现：改革 开放30 多年以来,中国城镇的住房自有率大幅度提高,其主要原因从1998 年前的公房房改政策向1998 年后住房市场化政策转变。家庭生命周期、家庭收入、户籍、单位性质、职业等变 量显著影响住房选择,体现市场与制度对住房资源分配的双重影响。经济发达地区由于住房资源紧缺、住房价格高,居民住房承受能力差异较大,工作单位性质、户籍和家庭生命周期变量对住房选择的影响更显著。随着住房市场化进一步发展和住房制度改革的逐步深入,地方政府应逐步放宽社会住房的享受标准,尤其是户籍限制条件;开发商应更关注社会经济快 速转型背景下的家庭结构变化及其对居住偏好的影响。     

基于地貌单元的小区域地质灾害易发性分区方法研究

以汶川县城周边区域为研究区,分别以栅格单元与地貌单元作为单位评价单元,以信息量法与逻辑回归法两种评价模型对研究区进行地质灾害易发性评价分区。根据对评价结果的比较分析,在小区域范围内,基于地貌单元的区域易发性分区不仅仅能够更好地体现出区域内局部综合特性,而且评价分区结果与地质灾害实际分布情况更加吻合,分级层次更加明显,数学模型的适用效率很好。由此可见,在小范围区域内,基于地貌单元的地质灾害易发性评价分区具有良好的适用性与可塑性,在大比例尺地质灾害易发性和危险性制图中是一个有益的尝试与启发。     

COMMENTS ON THE NIPALS ALGORITHM

The Non-linear lterative Partial Least Squares(NIPALS)algorithm is used in principal componentanalysis to decompose a data matrix into score vectors and eigenvectors(loading vectors)plus a residualmatrix.N1PALS starts with some guessed starting vector.The principal components obtained by NIPALSdepends on the starting vector;the first principal component could not always be computed.Wold hassuggested a starting vector for NIPALS,but we have found that even if this starting vector is used,thefirst principal component cannot be obtained in all cases.The reason why such a situation occurs isexplained by the power method.A simple modification of the original NIPALS procedure to avoid gettingsmaller eigenvalues is presented.     

Mineral-Potential Mapping for MVT Deposits with Limited Data Sets Using Landsat Data and Geological Evidence in the Borden Basin, Northern Baffin Island, Nunavut, Canada

Posterior probabilities of occurrence for Zn-Pb Mississippi Valley Type (MVT) mineralization were calculated based on evidence maps derived from regional geology, Landsat-TM, RADARSAT-1, a digital elevation model and aeromagnetic data sets in the Borden Basin of northern Baffin Island, Canada. The vector representation of geological contacts and fault traces were refined according to their characteristics identified in Landsat-TM, RADARSAT-1, DEM, slope, aspect, and shaded relief data layers. Within the study area, there is an association between the occurrence of MVT mineralization and proximity to the contact of platformal carbonates and shale units of the adjacent geological formation. A spatial association also tends to exist between mineralization and proximity to E-W and NW-SE trending faults. The relationships of known MVT occurrences with the geological features were investigated by spatial statistical techniques to generate evidence maps. Supervised classification and filtering were applied to Landsat-TM data to divide the Society Cliffs Formation into major stratigraphic subunits. Because iron oxides have been observed at some of the MVT occurrences within the Borden Basin, Landsat-TM data band ratio (3/1) was calculated to highlight the potential presence of iron-oxides as another evidence map. Processed Landsat-TM data and other derived geological evidence maps provided useful indicators for identifying areas of potential MVT mineralization. Weights of evidence and logistic regression were used independently to integrate and generate posterior probability maps showing areas of potential mineralization based on all derived evidence maps. Results indicate that in spite of the lack of important data sets such as stream or lake sediment geochemistry, Landsat-TM data and regional geological data can be useful for MVT mineral-potential mapping.     

PREDICTIVE ABILITY OF REGRESSION MODELS.PART Ⅰ:STANDARD DEVIATION OF PREDICTION ERRORS (SDEP)

The standard deviation of prediction errors(SDEP)is used to evaluate and compare the predictive abilityof some regression models,namely MLR,ACE and linear and non-linear PLS,the last being the bestone.The parameter is determined by a cross-validation approach as an average of several runs obtainedon forming groups in a random way.The variation in SDEP with the number of latent variables in PLSis also discussed.     

Effect of grazing on soil-water content in semiarid rangelands of southeast Idaho

Although numerous factors influence soil-water content, it is considered a key indicator of rangeland health. This paper investigates the effect of grazing on soil-water content using three treatments within the same soil association. The treatments, simulated holistic planned grazing (SHPG), rest-rotation (RESTROT), and total rest (TREST) applied stocking rates of 36, 6, and 0 animal days/hectare respectively. Soil-water content was measured continuously from 2006 to 2008 using 36 capacitance sensors. Statistical analyses revealed differences in percent volumetric-water content (%VWC) and in all treatments, the SHPG pasture had the highest %VWC. Mixed procedures models indicate strong environmental and treatment effects as explanatory variables for the observed difference in %VWC. Although results of vegetation cover analyses indicated no difference in percent shrub cover in the two production pastures (SHPG and RESTROT), percent litter cover differed in the latter years of this study. It was concluded that in addition to a variety of other factors, management decisions (grazing and rest) can have substantial influence upon soil-water content and that soil-water content can vary substantially as a result of animal impact and the duration of grazing.     

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.     

Spatial Analysis Techniques as Successful Mineral-Potential Mapping Tools for Orogenic Gold Deposits in the Northern Fennoscandian Shield,Finland

Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.