利用脂肪酸标记法对南极罗斯海桡足类优势物种的食性研究

脂肪酸标记法是浮游动物食性研究的一种重要方法.将提取的脂质区分为中性脂和极性脂,可以进一步查明不同脂质在浮游动物食性研究中的作用.本研究通过分析不同脂质的脂肪酸构成,对2013年1月中国第29次南极考察期间在罗斯海采集的5种优势种桡足类的食性进行了研究.结果显示:5种桡足类的极性脂脂肪酸具有相似的组成,无法给出桡足类食性差异信息,食性信息主要体现于中性脂脂肪酸组成的差异.通过分析中性脂脂肪酸的组成信息,将5种桡足类的食性分为三类:Cala-noides acutus和Calanus propinquus主要摄食浮游植物,Metridia gerlachei及小型桡足类Oncaea curvata为杂食性种,Paraeuchaeta antarctica为肉食性种.本文的结果说明,在应用脂肪酸作为浮游动物食性标记的研究中,应尽量区分不同脂质种类的脂肪酸信息,从而得到更为精确的浮游动物食性信息.     

Large Eddy Simulation for Wave Breaking in the Surf Zone

In this paper, (he large eddy simulation method is used combined with the marker and cell method to study the wave propagation or shoaling and breaking process. As wave propagates into shallow water, the shoaling leads lo the increase of wave height, and then at a certain position, the wave will be breaking. The breaking wave is a powerful agent for generating turbulence, which plays an important role in most of the fluid dynamic processes throughout the surf zone, such as transformation of wave energy, generation of near-shore current and diffusion of materials. So a proper numerical model for describing the turbulence effect is needed. In this paper, a revised Smagorinsky subgrid-scale mode! is used to describe the turbulence effect. The present study reveals that the coefficient of the Smagorinsky model for wave propagation or breaking simulation may be taken as a varying function of the water depth and distance away from the wave breaking point. The large eddy simulation model presented in this pape     

微卫星DNA标记技术及其在海洋生物遗传学中的应用

DNA简单重复序列从1974年在海洋生物中被发现,到1989年“微卫星”术语开始使用这段时间是这种新型分子标记技术的发展阶段。伴随着PCR技术的发明、成熟与拓展,微卫星DNA以其多态性高、随机分布、共显性遗传、重复性好等特点在生物学的个体及系统发育方面得到很广泛的应用。本文从微卫星DNA的发展简史开始,较详细地介绍了它的原理、方法及策略,然后概括了在海洋动、植物的遗传学中,微卫星DNA在遗传多样性分析、遗传图谱构建、基因鉴定和标记以及系谱认证等领域的研究,展望了其在海洋生物分子育种、基因克隆、生物保护等方面的应用前景。     

Hierarchical probabilistic regionalization of volcanism for Sengan region,Japan

A 1 km square regular grid system created on the Universal Transverse Mercator zone 54 projected coordinate system is used to work with volcanism related data for Sengan region. The following geologic variables were determined as the most important for identifying volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater pH value, presence of volcanic rocks and presence of hydrothermal alteration. Data available for each of these important geologic variables were used to perform directional variogram modeling and kriging to estimate geologic variable vectors at each of the 23949 centers of the chosen 1 km cell grid system. Cluster analysis was performed on the 23949 complete variable vectors to classify each center of 1 km cell into one of five different statistically homogeneous groups with respect to potential volcanism spanning from lowest possible volcanism to highest possible volcanism with increasing group number. A discriminant analysis incorporating Bayes’ theorem was performed to construct maps showing the probability of group membership for each of the volcanism groups. The said maps showed good comparisons with the recorded locations of volcanism within the Sengan region. No volcanic data were found to exist in the group 1 region. The high probability areas within group 1 have the chance of being the no volcanism region. Entropy of classification is calculated to assess the uncertainty of the allocation process of each 1 km cell center location based on the calculated probabilities. The recorded volcanism data are also plotted on the entropy map to examine the uncertainty level of the estimations at the locations where volcanism exists. The volcanic data cell locations that are in the high volcanism regions (groups 4 and 5) showed relatively low mapping estimation uncertainty. On the other hand, the volcanic data cell locations that are in the low volcanism region (group 2) showed relatively high mapping estimation uncertainty. The volcanic data cell locations that are in the medium volcanism region (group 3) showed relatively moderate mapping estimation uncertainty. Areas of high uncertainty provide locations where additional site characterization resources can be spent most effectively. The new data collected can be added to the existing database to perform future regionalized mapping and reduce the uncertainty level of the existing estimations.     

滇西南昌宁—孟连带三叠纪牡音河组硅质岩地球化学特征及沉积环境

滇西南昌宁—孟连带牡音河组硅质岩已进行了较好的生物地层学和沉积学研究, 表明为中三叠世深水盆地沉积, 但对其沉积构造背景存在不同的认识. 本文尝试用分子有机地球化学方法, 并辅以无机地球化学的手段, 研究牡音河组硅质岩的物源环境, 分析沉积区构造背景. 研究结果表明硅质岩中正构烷烃碳数分布范围较宽C₁₄—C₃₆, 且具双峰型分布模式, 说明其有机质为陆生高等植物和水生生物混合来源; 萜类和甾类化合物特征说明研究区有机质来源总体上以水生生物为主, 同时有不同程度陆生高等植物混入. 牡音河组硅质岩Σw (REE) 为83.56 × 10-6 124.1 × 10-6, w (Ce)/w (Ce) 在0.760.99之间变化, w (La)_n/w (Yb)_n在0.771.98之间, 说明该盆地明显受陆源区物质影响. 另外, 该硅质岩的w (La)_n/w (Ce)_n和w (Al₂O₃)/w (Al₂O₃+Fe₂O₃) 图解及w (MnO)/w (TiO₂) 比值也说明牡音河组硅质岩沉积过程中受到大陆作用. 两方面的特征互相印证, 说明研究区中三叠世为受陆地强烈作用的深水局限盆地. 这项研究还说明, 分子有机地球化学方法能够为硅质岩沉积环境的探讨提供更多的物源和环境信息.      

利用小波变换的高分辨率多光谱遥感图像多尺度分水岭分割

为了减少仅用分水岭变换而导致的过分割问题,本文提出利用小波变换的多尺度处理方式用于融合后多光谱QuickBird图像的分割。整个分割过程包括多尺度图像表示、图像分割、区域合并和结果映射等过程。首先,依据原始图像的大小确定分解尺度并用小波变换产生各波段的低尺度图像。采用相位一致模型提取各近似系数的梯度,并逐尺度地融合各梯度图。分析不同尺度下的不同地物的局部梯度方差,以选择最佳的小波分解尺度。然后,通过移动阈值与扩展最小变换,利用多层次标记提取方法标记均质区域。进而,在梯度重建的基础上利用标记分水岭变换得到分割图像。其次,采取空间相邻关系、面积、光谱与纹理等多约束策略,以搜索最小合并代价的方式合并最初分割区域中的邻接区域对。最后,修改细节子图并进行小波逆变换将最初分割结果投影到更高尺度图像,同时处理边界上的像元以保持区域边界直至原始图像。实验结果表明本文方法不仅能够用于高分辨率多光谱遥感图像的分割,而且缓解了过分割问题且取得了较准确的分割效果。     

测井方法在山西宁武煤田马营堡井田勘探中的应用

马营堡井田基本构造形态为一单斜构造,区内不同岩性地层的电阻率、密度、声波时差及伽马值均有明显差异,其中煤层的电阻率、密度及声波时差曲线均表现出出高异常及自然伽马低异常;而砂岩随粒度的变细及泥质含量的增加,自然伽玛逐渐增大。依据该区岩性基本物性特征,确定了井田内新生界与基岩、下石盒子组与山西组分界及主采煤层4、9号等主要标志层的响应特征。这些地层标志在测井曲线上形态,可作为该区地层对比的重要依据。     

我国小城镇功能结构初探：以广东省为例

本文通过对广东省两种类型城镇的功能特征进行定量分析,探讨了广东省小城镇的功能结构,揭示了县城和非县城建制镇在企业所有制构成、就业结构、文教卫生设施、基础设施和接纳农村剩余劳动力等方面的差异。     

内蒙古扎鲁特盆地陶海营子剖面林西组烃源岩 生物标志化合物特征及意义

采用气相色谱、气相色谱-质谱技术,研究了扎鲁特盆地陶海营子剖面林西组烃源岩的生物标志化合物特征,剖析其蕴含的有机质生源、沉积环境、有机质热演化程度等方面的信息及地质地球化学意义.林西组烃源岩饱和烃气色谱以“前峰型”的单峰分布为主,主峰碳以C23为主,表明有机质来源以混合有机质为主;Pr/Ph值主要介于0.3～0.6之间,植烷优势较明显,指示为强还原的沉积环境.林西组烃源岩具有“长侧链的三环萜烷丰富、伽马蜡烷含量高和C27甾烷分布占优势”的分子化石组合特征,伽马蜡烷比值为0.15～0.29,平均值为0.19,说明林西组烃源岩形成于微咸水—半咸水环境;藿烷与甾烷的含量比值主要分布于0.6～1.0之间,平均值为0.84,表明细菌和藻类对有机质的贡献大致相当;C27甾烷丰度较高,C27(甾烷)/C29(甾烷)的比值为1.13,表明生烃母质生源构成是低等水生生物藻类来源的有机质特征.