CINRAD/SA(B)发射机高压打火组件级故障诊断

新一代天气雷达技术保障中,发射机高压负载打火导致的综合故障的诊断和定位是比较复杂的,具有故障点多、故障涉及组件多、故障修复时间长等特点,是新一代天气雷达故障维修的难点。依据发射机高压控制和监控信号流程,提出了发射机高压打火组件级故障诊断流程。通过发射机组件级故障诊断流程快速修复发射机高压负载打火综合故障过程,表明发射机高压打火组件级故障诊断流程在雷达维修中具有规范化和适用性维修效果,进一步显示出故障分析诊断流程在新一代天气雷达技术保障中的重要作用。     

鄂尔多斯盆地北部砂岩型铀矿铀矿物地质地球化学特征及其成因意义

鄂尔多斯盆地北部已发现系列砂岩型铀矿,由东往西依次有东胜铀矿、杭锦旗(纳岭沟)铀矿、大营铀矿等大型、特大型矿床,成为我国目前砂岩型铀矿发现规模最大、最具远景的地区。研究表明,它们具有相似的矿床地质特征和形成环境。在矿床成矿作用研究中,铀的存在形式及铀矿物特征对砂岩铀矿来说是一项重要的内容,其认识对铀的地浸开采可提供重要依据,也是了解砂岩型铀矿形成机制或矿床形成环境及成因的重要信息。本文主要从三个方面对鄂尔多斯盆地北部大营铀矿、纳岭沟铀矿等主要砂岩型铀矿中铀矿物地质地球化学特征和成因进行了研究和探讨。通过电子探针测试,高分辨率扫描电镜观察等手段,认为盆地北部铀矿的铀矿物类型主要为铀石,含少量的沥青铀矿、钛铀矿、水硅铀石、钍铀石等;铀矿物常与黄铁矿、有机质(煤屑)及方解石相伴生。采用逐级化学提取等方法定量地分析了铀矿物占矿石中铀配分的比例关系,认为其中铀矿物和吸附态约各占铀存在形式的50%。首次对砂岩型铀矿的铀矿物进行微区原位LA-ICP-MS稀土元素分析,认为ΣREE在铀矿物铀石中高度富集,是矿石中稀土元素的主要载体;稀土元素是铀矿石中可综合利用的有益组份,其标准化曲线表现为明显的右倾型,轻稀土富集,轻重稀土分馏强烈,δEu和δCe具较弱的负异常,说明总体上铀矿化形成于外生后成环境。从上述铀矿物特征,初步探讨了铀矿化形成的环境与成因,认为铀矿化形成经历了至少为低温流体作用的环境;铀矿化形成于浅部地壳即外生后成环境而与深源作用无关。     

Petrogenesis and geodynamic evolution of the Kajan Neogene subvolcanic rocks,Nain, Central Iran

Kajan subvolcanic rocks in the Urumieh–Dokhtar magmatic arc (UDMA), Central Iran, form a Late Miocene-Pliocene shallow-level intrusion. These subvolcanics correspond to a variety of intermediate and felsic rocks, comprising quartz diorite, quartz monzodiorite, tonalite and granite. These lithologies are medium-K calc-alkaline, with SiO₂ (wt.%) varying from 52% (wt.%) to 75 (wt.%). The major element chemical data also show that MgO, CaO, TiO₂, P₂O₅, MnO, Al₂O₃ and Fe₂O₃ define linear trends with negative slopes against SiO₂, whilst Na₂O and K₂O are positively correlated with silica. Contents of incompatible trace elements (e.g. Ba, Rb, Nb, La and Zr) become higher with increasing SiO₂, whereas Sr shows an opposite behaviour. Chondrite-normalized multi-element patterns show enrichment in LILE relative to HFSE and troughs in Nb, P and Ti. These observations are typical of subduction related magmas that formed in an active continental margin. The Kajan rocks show a strong affinity with calc-alkaline arc magmas, confirmed by REE fractionation (La_N/Yb_N = 4.5–6.4) with moderate HREE fractionation (Sm_N/Yb_N = 1.08–1.57). The negative Eu anomaly (Eu/Eu* <1), the low to moderate Sr content (< 400 ppm) and the Dy/Yb values reflect plagioclase and hornblende (+- clinopyroxene) fractionation from a calc-alkaline melt Whole–rock Sr and Nd isotope analyses show that the ⁸⁷Sr/⁸⁶Sr initial ratios vary from 0.704432 to 0.705989, and the ¹⁴³Nd/¹⁴⁴Nd initial ratios go from 0.512722 to 0.512813. All the studied samples have similar Sr-Nd isotopes, indicating an origin from a similar source, with granite samples that has more radiogenic Sr and low radiogenic Nd isotopes, suggesting a minor interaction with upper crust during magma ascent. The Kajan subvolcanic rocks plot within the depleted mantle quadrant of the conventional Sr-Nd isotope diagram, a compositional region corresponding to mantle-derived igneous rocks.     

Landscape pattern and transition under natural and anthropogenic disturbance in an arid region of northwestern China

There is a pressing need to determine the relationships between driving variables and landscape transformations. Human activities shape landscapes and turn them into complex assemblages of highly diverse structures. Other factors, including climate and topography, also play significant roles in landscape transitions, and identifying the interactions among the variables is critical to environmental management. This study analyzed the configurations and spatial-temporal processes of landscape changes from 1998 to 2011 under different anthropogenic disturbances, identified the main variables that determine the landscape patterns and transitions, and quantified the relationships between pairs of driver sets. Landsat images of Baicheng and Tekes from 1998, 2006 and 2011 were used to classify landscapes by supervised classification. Redundancy analysis (RDA) and variation partitioning were performed to identify the main driving forces and to quantify the unique, shared, and total explained variation of the sets of variables. The results indicate that the proportions of otherwise identical landscapes in Baicheng and Tekes were very different. The area of the grassland in Tekes was much larger than that of the cropland; however, the differences between the grassland and cropland in Baicheng were not as pronounced. Much of the grassland in Tekes was located in an area that was near residents, whereas most of the grassland in Baicheng was far from residents. The slope, elevation, annual precipitation, annual temperature, and distance to the nearest resident were strong driving forces influencing the patterns and transitions of the landscapes. The results of the variation partitioning indicated complex interrelationships among all of the pairs of driver sets. All of the variable sets had significant explanatory roles, most of which had both unique and shared variations with the others. The results of this study can assist policy makers and planners in implementing sustainable landscape management and effective protection strategies.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.     

附有物理量和气象条件约束的光学卫星国土观测有效覆盖率评估

传统光学卫星国土观测覆盖评估建立在卫星对地理想覆盖的基础上,并未考虑卫星存储、星地数据传输、观测时长等物理量及观测区域气象因素对于覆盖性能的影响。本文针对光学遥感卫星的国土观测需求.建立国土观测有效覆盖能力评估指标体系,根据卫星数据存储能力、星地数据传输能力、卫星单圈最大观测时长、卫星观测太阳高度角等性能参数,提出了基于物理性能约束下的有效覆盖计算方法。根据气象台站历年气象数据,提出了气象约束因子的计算方法。综合考虑卫星物理性能约束与观测区域气象约束,计算光学遥感卫星对地观测有效覆盖能力。最后根据专家设计的光学遥感卫星国土观测有效覆盖能力评估指标权重,利用层次分析法(AHP)评估光学遥感卫星系统对于国土观测的需求满足程度。试验结果表明,本文方法对于国土观测有效覆盖的估算和评价结果更加精确,更接近于国土观测的实际应用需求,为对地观测有效覆盖能力评估提供了一种更为精确的可行方案。     

一种多系统组合导航快速选星方法

采用多卫星导航系统组合导航,定位精度和系统可靠性会大幅提升,但导航定位运算量也会成倍增长。为解决多系统组合导航定位精度与实时性之间的矛盾,提出一种新的选星方法。新方法不追求最小GDOP值,而是以满足导航定位精度的GDOP值为前提,结合模糊理论中隶属函数的思想,按卫星在星座中均匀分布为原则进行选星。推导伪距测量的误差模型,分析了GDOP与测量误差之间的关系。北斗、GPS和GLONASS三系统组合导航选星实验结果表明,在不超过3次求解GDOP值的情况下,新方法能以不小于98%的概率得到GDOP≤4。     

热红外与可见光数据的云边缘高度匹配

高分辨率数据中云高度的差异性突显,特别是边缘处高度在云阴影识别和地表辐射估算等方面成为需要考虑的重要因素。热红外数据获取云高度分辨率较低、缺乏细部差异性特征,为解决这一问题,首先将对应的热红外和可见光数据进行特征点配准,再将基于热红外数据计算的云高度重采样至高分辨率,然后以基于欧式距离变换的围线搜索方法及距离加权将热红外云边缘高度匹配至对应的可见光图像,最后根据云阴影的相似度匹配方法确定真实云高度。结果表明,算法在遵循热红外云高信息分布变化规律的同时,可以得到较准确的高分辨率云边缘高度,一定程度上解决了热红外技术获取云高在分辨率上的局限,扩展了其在云高反演方面的作用。     

K-GM（1，1）模型在岩体变形监测中的应用

传统GM （1,1）模型存在着长期预测效果差、模型精度不高等问题,卡尔曼滤波能够排除建模过程中随机干扰因素,滤波值能够反映更真实的数据情况。为了能更好地提高变形监测的预测精度,基于传统GM （1,1）模型和卡尔曼滤波,提出K‐GM （1,1）模型,利用该模型对岩体变形监测数据进行建模预测,并与传统GM （1,1）模型预测结果进行对比分析,结果表明,K‐GM （1,1）模型具有较高的预测精度,可作为变形监测的一种新方法。     

改进后灰色神经网络及短期基坑沉降预测研究

在短期基坑沉降监测中,由于数据量少且呈非线性变化,沉降模型很难准确建立。灰色GM(1,1)对数据少、趋势性强、波动小的数据有较高的预测精度,但不能模拟复杂的非线性函数;BP神经网络可以对非线性数据进行学习训练,具有自学习、自适应能力;通过将GM(1,1)与BP神经网络组合,并优化网络部分的学习率、权值和阈值等,建立一种改进的灰色神经网络模型,该模型具有对非线性数据自学习、自适应能力和预测精度更高等优点。通过某基坑沉降监测分析,验证改进的灰色神经网络模型预测精度更高,适合短期建模,具有很好的实用性。