乌鲁木齐市TSP微观形貌特征分析

利用环境扫描电镜(SEM/EDS)分别对2009年四季在雷达站、卫星站、年轮实验室和白杨沟站采集的TSP样品进行微观形貌和能谱分析,结果表明:乌鲁木齐市TSP形貌具有明显的时空分布特征。春季TSP颗粒较大,形状不规则,可能来源沙尘;夏季TSP中颗粒少且小,可能含有土壤扬尘和植物花粉;秋季TSP颗粒多为蓬松链状,可能是烟尘聚集体;冬季TSP颗粒较大,形状规则的颗粒增多且多为圆形,是典型的燃煤飞灰形态。北郊、市区TSP中颗粒物数量高于南郊山区的白杨沟站,秋冬季燃煤对北郊和市区TSP形貌影响较大。TSP主要含Si、S、Ca、Al、Fe、Mg、Cl、Na,说明TSP来源广泛,并且由多种颗粒混合而成。春夏季乌市TSP中Si、Ca Al、Na、Mg含量较高,秋冬季Si、S、Ca、Al、Fe、Cl含量很高。市区TSP中S、Fe、Al较白杨沟同期所占比重大,白杨沟TSP中元素种类较少,S、Fe含量不高,表明燃煤、工业排放影响不大。     

肝脏MR四种扫描序列比较

目的 探讨豺脏ＭＲ扫描序列的最佳选择。方法 对２９例患者采用四种（ＳＥ、ＦＬＡＳＨ、Ｔｓｅ１１、Ｔｓｅ２３）扫描序列进行肝脏ＭＲ扫描,测量和计算图像的信噪比９ＳＮＲ）和对比度噪声比（ＣＮＲ）,同时对影像进行视觉评价。结果 ＦＬＡＳＨ和ＴＳＥ２３序列的ＳＮＲ和ＣＮＲ均不及Ｔｓｅ１１序列。结论 肝脏ＭＲ扫描ＦＬＡＳＨ序列可作为首选,Ｔｓｅ２３序列可作为辅助使用。     

应用氩离子抛光-扫描电镜方法研究四川九老洞组页岩微观孔隙特征

微观孔隙结构是页岩储层研究的重点,而扫描电镜方法无法识别机械抛光中由于页岩硬度差异所造成的不规则形貌。本文利用氩离子抛光-扫描电镜方法对四川威远区块九老洞组页岩进行研究,发现了三种孔隙类型:1无机孔以粒间孔和黏土矿物层间孔为主,同时发育晶间孔和生物孔,孔径主体100~500 nm;2有机孔受控于热成熟度或有机黏土复合体,孔径范围数十纳米至数微米;3微裂缝包括成岩收缩裂缝、高压碎裂缝、构造裂缝和人为裂缝,缝宽数微米,缝长数微米至数十微米。研究表明无机孔和微裂缝是九老洞组页岩气的主要储集空间。     

影响黄土湿陷性因素的微观试验研究

借助扫描电子显微镜和光学数码显微镜, 分析了不同含水率黄土试样表面微区结构变化与黄土湿陷性的关系和不同埋深土样在水与外力共同作用下湿陷前后微观结构的变化特征。利用图像处理软件对所获取的微结构图像进行研究, 分析了黄土湿陷前后土样中大、中、小孔隙和微孔隙数量的变化。结果表明:湿陷后比湿陷前土样微孔隙增多31.18%, 小孔隙增多54.07%, 中孔隙减少30.49%, 大孔隙减少90.14%。这说明随着压力的不断增大, 黄土中的孔隙被逐渐压缩, 大孔隙和中孔隙的数量逐渐减少, 小孔隙和微孔隙的数量逐渐增加, 为黄土的湿陷变形提供了充分的空间。讨论了土样中4类孔隙对黄土湿陷的贡献量, 从微观角度综合分析了黄土湿陷的成因机理。     

用搜索法对1976—1987年陕西区域弱震定位

利用地震图上量得的S——P时间差,导出计算机定位直接搜索法中的准则函数,对1976～1987年的地震进行重新定位,共得到184次弱震的震源位置及各次地震的虚波速度值。同时对其中的108次地震进行了误差分析。此外还对铜川爆破和1978年2月11日石泉4.2级地震作了考察分析。     

Advanced Micro-XRF Method to Separate Sedimentary Rhythms and Event Layers in Sediments: Its Application to Lacustrine Sediment from Lake Suigetsu,Japan

Event-related sedimentary layers, which are deposited occasionally due to volcanic eruptions, earthquakes or heavy rains, are often contained in the rhythmical sequences of lacustrine and marine sediments. We have developed an analytical method for separating the sedimentary rhythms and the event layers identified using the scanning X-ray analytical microscope (SXAM) and obtained sequential profiles of seven elements Al, Si, K, Ca, Ti, Mn, and Fe in the lacustrine sediment from Lake Suigetsu, Japan. Two types of event layers could be detected from the elemental composition of 33 layers of sediment: three known volcanic ash layers and 30 clay layers containing 12 turbidites. The recurrence interval of the latter, which may potentially be initiated and archived by locally important earthquakes, is estimated to be an average of 640 ± 160 years by using Sompi event analysis (SEA) based on an autoregressive (AR) model. After removing those portions that represented event layers from the elemental profiles, we obtained event-removed (ER) temporal profiles based on the tephrochronology of the three volcanic ash layers. The ER temporal profiles of manganese and iron, probably representing the siderite content, showed a millennial-scale variation in the Holocene that corresponded well with ice-rafting events in the North Atlantic.     

A SEM-based method to determine the mineralogical composition and the particle size distribution of suspended sediment

A robust method for characterizing the mineralogy of suspended sediment in continental rivers is introduced. It encompasses 3 steps: the filtration of a few milliliters of water, measurements of X-ray energy dispersive spectra using Scanning Electron Microscopy (SEM), and robust machine learning tools of classification. The method is applied to suspended particles collected from various Amazonian rivers. A total of more than 204,000 particles were analyzed by SEM-EDXS (Energy Dispersive X-ray Spectroscopy), i.e. about 15,700 particles per sampling station, which lead to the identification of 15 distinct groups of mineralogical phases. The size distribution of particles collected on the filters was derived from the SEM micrographs taken in the backscattered electron imaging mode and analyzed with ImageJ freeware. The determination of the main mineralogical groups composing the bulk sediment associated with physical parameters such as particle size distribution or aspect ratio allows a precise characterization of the load of the terrigenous particles in rivers or lakes. In the case of the Amazonian rivers investigated, the results show that the identified mineralogies are consistent with previous studies as well as between the different samples collected. The method enabled the evolution of grain size distribution from fine to coarse material to be described in the water column. Implications about hydrodynamic sorting of mineral particles in the water column are also briefly discussed. The proposed method appears well suited for intensive routine monitoring of suspended sediment in river systems.     

多尺度突变现象的扫描式t检验方法及其相干性分析

阐明了将检测两子样本平均值之差的学生氏t检验推广到对多尺度突变现象进行扫描式检测的计算方法;对于t检验要求序列独立的限制,引用了初步的订正方法;还给出了检测两个序列间多尺度突变相干性的计算公式.扫描式t检验不仅具有相当于子波变换检测多尺度突变现象的功能,而且解决了子波变换检测突变时缺少临界值的问题.由于t统计量包含有二阶矩均方差,它不能像子波变换那样作为分解工具,但检测的尺度参数也就不必局限于2的整数幂,因而可以进行扫描式检测.应用于尼罗河年最高与最低水位历史序列(AD622—1470),能较客观和精确地检测出两序列在某些尺度上的相干性(同步或反位相)变化;并由此重新划分了该流域几十年至百余年时间尺度的相对干湿期.结果与目前查阅到的埃及灾荒历史记载相吻合.     

扫描电镜能谱与冷冻传输装置联用测试矿物中流体包裹体固液相成分的方法研究

研究流体包裹体中子矿物种类、化学成分及分布特征,对于确定热液成分和成矿物理化学条件、流体的演化规律和成矿机制等有非常重要的意义。但目前常用的各种测试研究方法存在测试目的物寻找困难、不透明子矿物难鉴定等局限性。本文采用冷冻传输装置首先将流体包裹体冷冻固定并打开,然后用扫描电镜、能谱仪测试矿物中流体包裹体中的子矿物和液相成分,该方法可解决扫描电镜下流体包裹体研究目标物不易寻找,打开流体包裹体后子矿物易散失和碎屑易混入的问题,可有效测试小颗粒(1~2μm)、不透明子矿物及不透明矿物中的流体包裹体固液相成分。用该方法实测山东邹平王家庄铜矿流体包裹体液相中Na+、K+、Cl-组分的质量分数分别为5.85%、3.60%、16.18%,计算的盐度为35.35%,并确定了其中不透明矿物子矿物为黄铜矿,证明了黄铜矿是从高盐度热液中晶出的结论。本方法为矿物中流体包裹体研究提供了一种新的测试手段。     

A neural network method for monitoring snowstorm: A case study in southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.