沉积有机相的研究现状及其应用

沉积有机相是近年来国内外广泛运用于油气勘探的一种有效的研究方法。本文介绍了沉积有机相的概念、划分方案，及其在油气勘探、盆地分析和层序地层学中的应用，指出了沉积有机相与层序地层分析相结合对油气资源评价和预测烃源岩有广阔的发展前景。     

镇旬铅锌矿田热水沉积硅岩研究

从镇(安)旬(阳)铅锌矿田热水沉积硅岩的产状、形态、岩石结构、构造、矿物成分、化学成分、微量元素、稀土元素、硫同位素、氧同位素等研究表明，本区硅岩具有以热液为主兼有生物和正常沉积的特征，并非典型热水沉积岩。     

陕西金矿床类型及其找矿方向

通过对陕西金矿资源的分析、总结，将陕西金矿成因类型划分为7类14亚类；与金矿有关的成矿系列划分为3个矿床成矿系列组合，7个成矿系列类型，10个成矿系列和4个亚系列；金矿床空间分布严格受隐伏基底和出露基底，隐伏岩体、重磁吻合构造，构造和成矿时代的控制；与金成矿有关的Ⅲ级成矿区(带)有4条，有关的Ⅳ级成矿区(带)有10条，有关的Ⅴ级主要金成矿区(带)有16条；提出了金矿新的找矿方向，为今后本区金矿地质找矿的新突破提供信息。     

利用热红外温差识别沙尘

沙尘识别是沙尘灾害监测和沙尘气溶胶特性研究的首要工作。利用辐射传输方程进行了沙尘气溶胶的辐射计算 ,对不同沙尘气溶胶光学厚度下的热红外通道温差ΔT(T11μm-T12 μm)的变化进行了分析。理论分析表明 ,利用热红外通道的温度差ΔT进行陆地沙尘识别是可行的。并利用NOAA AVHRR热红外通道的温度差ΔT进行了陆地沙尘识别的实验 ,经与地面气象站实测的沙尘天气现象相比较 ,结果一致。     

基于灰关联分析的GPS高程拟合加权平均函数模型权函数优选

基于灰色系统理论的灰关联分析方法,提出了权函数的优选方法,实例分析表明其具有较好的实用价值.     

The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data

Based on the land surface temperature (LST), the land cover classification map,vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains,especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃ between Beijing city and suburb areas, comparatively is 8- 10℃ between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative.The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481).The results demonstrate that the vegetation greenness is an important factor for reducing the UHI,and large-scale construction of greenbelts can considerably reduce the UHI effect.     

Leaf area index retrieval based on canopy reflectance and vegetation index in eastern China

The aim of this paper is to investigate the feasibility of using Landsat TM data to retrieve leaf area index (LAI). To get a LAI retrieval model based ground reflectance and vegetation index, detailed field data were collected in the study area of eastern China, dominated by bamboo, tea plant and greengage. Plant canopy reflectance of Landsat TM wavelength bands has been inversed using software of 6S. LAI is an important ecological parameter. In this paper, atmospheric corrected Landsat TM imagery was utilized to calculate different vegetation indices (VI), such as simple ratio vegetation index (SR), shortwave infrared modified simple ratio (MSR), and normalized difference vegetation index (NDVI). Data of 53 samples of LAI were measured by LAI-2000 (LI-COR) in the study area. LAI was modeled based on different reflectances of bands and different vegetation indices from Landsat TM and LAI samples data. There are certainly correlations between LAI and the reflectance of Tm3, TM4, TM5 and TM7. The best model through analyzing the results is LAI = 1.2097*MSR + 0.4741 using the method of regression analysis. The result shows that the correlation coefficient R2 is 0.5157, and average accuracy is 85.75%. However, whether the model of this paper is suitable for application in subtropics needs to be verified in the future.     

Sedimentation rates in the Wanggang salt marshes, Jiangsu

1 IntroductionLand-ocean interaction in coastalzone is one of the key m atters of the International G eosphereand Biosphere Program (IG BP). The key problem s of the second phase in the next decadeinclude the m aterial cycle, the system evolution process …     

对我国金刚石钻头胎体经典配方的理论优化和建议

选取合理可信的约束条件对我国经典的63号金刚石钻头胎体配方进行了优化验证，获得理论认识，并依此对已有成熟配方的改进提出了建议。     

Element mobility during pyrite weathering: implications for acid and heavy metal pollution at mining-impacted sites

Based on back scattered electron images and electron micro-probe analysis results, four alteration layers, including a transition layer, a reticulated ferric oxide layer, a nubby ferric oxide layer and a cellular ferric oxide layer, were identified in the naturally weathering products of pyrite. These layers represent a progressive alteration sequence of pyrite under weathering conditions. The cellular ferric oxide layer correlates with the strongest weathering phase and results from the dissolution of nubby ferric oxide by acidic porewater. Leaching coefficient was introduced to better express the response of element mobility to the degree of pyrite weathering. Its variation shows that the mobility of S, Co and Bi is stronger than As, Cu and Zn. Sulfur in pyrite is oxidized to sulfuric acid and sulfate that are basically released into to porewater, and heavy metals Co and Bi are evidently released by acid dissolution. As, Cu and Zn are enriched in ferric oxide by adsorption and by co-precipitation, but they would re-release to the environment via desorption or dissolution when porewater pH becomes low enough. Consequently, Co, Bi, As, Cu and Zn may pose a substantial impact on water quality. Considering that metal mobility and its concentration in mine waste are two important factors influencing heavy metal pollution at mining-impacted sites, Bi and Co are more important pollutants in this case.